{"id":2022,"date":"2025-08-28T09:04:11","date_gmt":"2025-08-28T09:04:11","guid":{"rendered":"https:\/\/blog.topexamcollection.com\/?p=2022"},"modified":"2025-08-28T09:04:11","modified_gmt":"2025-08-28T09:04:11","slug":"snowflake-certified-dsa-c03-dumps-questions-valid-dsa-c03-materials-q108-q130","status":"publish","type":"post","link":"https:\/\/blog.topexamcollection.com\/zh\/2025\/08\/snowflake-certified-dsa-c03-dumps-questions-valid-dsa-c03-materials-q108-q130\/","title":{"rendered":"Snowflake Certified DSA-C03  Dumps Questions Valid DSA-C03 Materials [Q108-Q130]"},"content":{"rendered":"\n\n<div class=\"kk-star-ratings kksr-auto kksr-align-left kksr-valign-top\"\n    data-payload='{&quot;align&quot;:&quot;left&quot;,&quot;id&quot;:&quot;2022&quot;,&quot;slug&quot;:&quot;default&quot;,&quot;valign&quot;:&quot;top&quot;,&quot;ignore&quot;:&quot;&quot;,&quot;reference&quot;:&quot;auto&quot;,&quot;class&quot;:&quot;&quot;,&quot;count&quot;:&quot;2&quot;,&quot;legendonly&quot;:&quot;&quot;,&quot;readonly&quot;:&quot;&quot;,&quot;score&quot;:&quot;4&quot;,&quot;starsonly&quot;:&quot;&quot;,&quot;best&quot;:&quot;5&quot;,&quot;gap&quot;:&quot;5&quot;,&quot;greet&quot;:&quot;Rate this post&quot;,&quot;legend&quot;:&quot;4\\\/5 - (2 votes)&quot;,&quot;size&quot;:&quot;24&quot;,&quot;title&quot;:&quot;Snowflake Certified DSA-C03  Dumps Questions Valid DSA-C03 Materials [Q108-Q130]&quot;,&quot;width&quot;:&quot;113.5&quot;,&quot;_legend&quot;:&quot;{score}\\\/{best} - ({count} {votes})&quot;,&quot;font_factor&quot;:&quot;1.25&quot;}'>\n            \n<div class=\"kksr-stars\">\n    \n<div class=\"kksr-stars-inactive\">\n            <div class=\"kksr-star\" data-star=\"1\" style=\"padding-right: 5px\">\n            \n\n<div class=\"kksr-icon\" style=\"width: 24px; height: 24px;\"><\/div>\n        <\/div>\n            <div class=\"kksr-star\" data-star=\"2\" style=\"padding-right: 5px\">\n            \n\n<div class=\"kksr-icon\" style=\"width: 24px; height: 24px;\"><\/div>\n        <\/div>\n            <div class=\"kksr-star\" data-star=\"3\" style=\"padding-right: 5px\">\n            \n\n<div class=\"kksr-icon\" style=\"width: 24px; height: 24px;\"><\/div>\n        <\/div>\n            <div class=\"kksr-star\" data-star=\"4\" style=\"padding-right: 5px\">\n            \n\n<div class=\"kksr-icon\" style=\"width: 24px; height: 24px;\"><\/div>\n        <\/div>\n            <div class=\"kksr-star\" data-star=\"5\" style=\"padding-right: 5px\">\n            \n\n<div class=\"kksr-icon\" style=\"width: 24px; height: 24px;\"><\/div>\n        <\/div>\n    <\/div>\n    \n<div class=\"kksr-stars-active\" style=\"width: 113.5px;\">\n            <div class=\"kksr-star\" style=\"padding-right: 5px\">\n            \n\n<div class=\"kksr-icon\" style=\"width: 24px; height: 24px;\"><\/div>\n        <\/div>\n            <div class=\"kksr-star\" style=\"padding-right: 5px\">\n            \n\n<div class=\"kksr-icon\" style=\"width: 24px; height: 24px;\"><\/div>\n        <\/div>\n            <div class=\"kksr-star\" style=\"padding-right: 5px\">\n            \n\n<div class=\"kksr-icon\" style=\"width: 24px; height: 24px;\"><\/div>\n        <\/div>\n            <div class=\"kksr-star\" style=\"padding-right: 5px\">\n            \n\n<div class=\"kksr-icon\" style=\"width: 24px; height: 24px;\"><\/div>\n        <\/div>\n            <div class=\"kksr-star\" style=\"padding-right: 5px\">\n            \n\n<div class=\"kksr-icon\" style=\"width: 24px; height: 24px;\"><\/div>\n        <\/div>\n    <\/div>\n<\/div>\n                \n\n<div class=\"kksr-legend\" style=\"font-size: 19.2px;\">\n            4\/5 - (2 votes)    <\/div>\n    <\/div>\n<p><span style=\"font-size: 18px\"><strong><span style=\"color: red\">Snowflake Certified DSA-C03&nbsp; Dumps Questions Valid DSA-C03 Materials<\/span><\/strong><\/span><\/p>\n<p><strong><span style=\"color: red\">Current DSA-C03 Exam Dumps [2025] Complete Snowflake Exam Smoothly<\/span><\/strong><\/p>\n<div id=\"watu_quiz\" class=\"quiz-area single-page-quiz\">\n<form action=\"\" method=\"post\" class=\"quiz-form \" id=\"quiz-852\" >\n<div class='watu-question' id='question-1'><div class='question-content'><p><strong>Q108.<\/strong> You are working with a dataset containing timestamps representing website user activity. The timestamps are stored as strings in the format &#8216;YYYY-MM-DD HH:MI:SS.SSSSSS&#8217; in a Snowflake table named &#8216;website_activity&#8217;. You need to extract the hour of the day from these timestamps and encode it as a cyclical feature using sine and cosine transformations. This is to capture the cyclical nature of user activity throughout the day (e.g., 23:00 and 00:00 are close in time). Which of the following Snowflake SQL code snippets correctly implements this cyclical encoding and creates the &#8216;hour_sin&#8217; and &#8216;hour_cos&#8217; columns?<\/p>\n<\/div><input type='hidden' name='question_id[]' value='16771' \/><div class='watu-questions-wrap '><input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='64820' \/><div class='watu-question-choice'><input type='radio' name='answer-16771[]' id='answer-id-64820' class='answer answer-1 php-answer-label answerof-16771' value='64820' \/>&nbsp;<label for='answer-id-64820' id='answer-label-64820' class='php-answer-label answer label-1'><span class='answer'><img decoding=\"async\" src=\"https:\/\/blog.topexamcollection.com\/wp-content\/uploads\/2025\/08\/DSA-C03-cf51518a7588359d6427092ec92a05f0.jpg\"\/><\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='64821' \/><div class='watu-question-choice'><input type='radio' name='answer-16771[]' id='answer-id-64821' class='answer answer-1 js-answer-label answerof-16771' value='64821' \/>&nbsp;<label for='answer-id-64821' id='answer-label-64821' class='js-answer-label answer label-1'><span class='answer'><img decoding=\"async\" src=\"https:\/\/blog.topexamcollection.com\/wp-content\/uploads\/2025\/08\/DSA-C03-63886ad046979f1e206c0eb0e4386dbf.jpg\"\/><\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='64822' \/><div class='watu-question-choice'><input type='radio' name='answer-16771[]' id='answer-id-64822' class='answer answer-1 js-answer-label answerof-16771' value='64822' \/>&nbsp;<label for='answer-id-64822' id='answer-label-64822' class='js-answer-label answer label-1'><span class='answer'><img decoding=\"async\" src=\"https:\/\/blog.topexamcollection.com\/wp-content\/uploads\/2025\/08\/DSA-C03-48387cdb8602265cd88acfc652cecfa2.jpg\"\/><\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='64823' \/><div class='watu-question-choice'><input type='radio' name='answer-16771[]' id='answer-id-64823' class='answer answer-1 js-answer-label answerof-16771' value='64823' \/>&nbsp;<label for='answer-id-64823' id='answer-label-64823' class='js-answer-label answer label-1'><span class='answer'><img decoding=\"async\" src=\"https:\/\/blog.topexamcollection.com\/wp-content\/uploads\/2025\/08\/DSA-C03-4076b6b23a59e22a0392274edcfa76b9.jpg\"\/><\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='64824' \/><div class='watu-question-choice'><input type='radio' name='answer-16771[]' id='answer-id-64824' class='answer answer-1 js-answer-label answerof-16771' value='64824' \/>&nbsp;<label for='answer-id-64824' id='answer-label-64824' class='js-answer-label answer label-1'><span class='answer'><img decoding=\"async\" src=\"https:\/\/blog.topexamcollection.com\/wp-content\/uploads\/2025\/08\/DSA-C03-0134bfd41ea0cef39e2c4ba38a2bf659.jpg\"\/><\/span><\/label><\/div>\n<\/div><div class='show-question-feedback' style='display:none;'>Option A is correct. It properly casts the timestamp string to a TIMESTAMP data type using &#8216;CAST(activity_timestamp AS TIMESTAMP) , extracts the hour using &#8216;EXTRACT(HOUR FROM &#8230; y , and applies the sine and cosine transformations to create the cyclical features. Options B, C, and D might contain syntax errors or incorrect functions. Using SUBSTRING to extract can be prone to errors as it doesn&#8217;t perform data validation. Option E also works but it uses a MOD function which is redundant. Therefore, it is less preferable to Option A.<\/div><input type='button' class='showchecked' style='margin: 10px 0;' onclick='showanswer1(1,this)' id='btn-1' value='See Answer'  \/><input type='hidden' id='questionType1' value='radio' class=''><\/div><div class='watu-question' id='question-2'><div class='question-content'><p><strong>Q109.<\/strong> You are building a data science pipeline in Snowflake to predict customer churn. The pipeline involves extracting data, transforming it using Dynamic Tables, training a model using Snowpark ML, and deploying the model for inference. The raw data arrives in a Snowflake stage daily as Parquet files. You want to optimize the pipeline for cost and performance. Which of the following strategies are MOST effective, considering resource utilization and potential data staleness?<\/p>\n<\/div><input type='hidden' name='question_id[]' value='16772' \/><div class='watu-questions-wrap '><input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='64825' \/><div class='watu-question-choice'><input type='checkbox' name='answer-16772[]' id='answer-id-64825' class='answer answer-2 js-answer-label answerof-16772' value='64825' \/>&nbsp;<label for='answer-id-64825' id='answer-label-64825' class='js-answer-label answer label-2'><span class='answer'>Use a single, large Dynamic Table to perform all transformations in one step, relying on Snowflake&#8217;s optimization to handle dependencies and incremental updates.<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='64826' \/><div class='watu-question-choice'><input type='checkbox' name='answer-16772[]' id='answer-id-64826' class='answer answer-2 php-answer-label answerof-16772' value='64826' \/>&nbsp;<label for='answer-id-64826' id='answer-label-64826' class='php-answer-label answer label-2'><span class='answer'>Implement a series of smaller Dynamic Tables, each responsible for a specific transformation step, with well-defined refresh intervals tailored to the data&#8217;s volatility and the downstream model&#8217;s requirements.<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='64827' \/><div class='watu-question-choice'><input type='checkbox' name='answer-16772[]' id='answer-id-64827' class='answer answer-2 js-answer-label answerof-16772' value='64827' \/>&nbsp;<label for='answer-id-64827' id='answer-label-64827' class='js-answer-label answer label-2'><span class='answer'>Load all data into traditional Snowflake tables and use scheduled tasks with stored procedures written in Python to perform the transformations and model training.<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='64828' \/><div class='watu-question-choice'><input type='checkbox' name='answer-16772[]' id='answer-id-64828' class='answer answer-2 php-answer-label answerof-16772' value='64828' \/>&nbsp;<label for='answer-id-64828' id='answer-label-64828' class='php-answer-label answer label-2'><span class='answer'>Use a combination of Dynamic Tables for feature engineering and Snowpark ML for model training and deployment, ensuring proper dependency management and refresh intervals for each Dynamic Table based on data freshness requirements.<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='64829' \/><div class='watu-question-choice'><input type='checkbox' name='answer-16772[]' id='answer-id-64829' class='answer answer-2 js-answer-label answerof-16772' value='64829' \/>&nbsp;<label for='answer-id-64829' id='answer-label-64829' class='js-answer-label answer label-2'><span class='answer'>Schedule all data transformations and model training as a single large Snowpark Python script executed by a Snowflake task, ignoring data freshness requirements.<\/span><\/label><\/div>\n<\/div><div class='show-question-feedback' style='display:none;'>Option B is correct because breaking down the transformations into smaller Dynamic Tables with tailored refresh intervals ensures that only necessary data is recomputed, minimizing cost and resource usage. Option D is also correct because combining Dynamic Tables for feature engineering with Snowpark ML allows for efficient model training and deployment within Snowflake, leveraging the platform&#8217;s scalability and security features. Furthermore, specifying refresh intervals based on data freshness guarantees that the model is trained on up-to-date information. Option A could lead to unnecessary computations if the entire table needs to be refreshed even for minor changes. Option C relies on traditional tables and stored procedures, which may be less efficient and harder to manage than Dynamic Tables. Option E ignores data freshness, which can significantly impact model accuracy.<\/div><input type='button' class='showchecked' style='margin: 10px 0;' onclick='showanswer1(2,this)' id='btn-2' value='See Answer'  \/><input type='hidden' id='questionType2' value='checkbox' class=''><\/div><div class='watu-question' id='question-3'><div class='question-content'><p><strong>Q110.<\/strong> You are building a fraud detection model using transaction data stored in Snowflake. The dataset includes features like transaction amount, merchant category, location, and time. Due to regulatory requirements, you need to ensure personally identifiable information (PII) is handled securely and compliantly during the data collection and preprocessing phases. Which of the following combinations of Snowflake features and techniques would be MOST suitable for achieving this goal?<\/p>\n<\/div><input type='hidden' name='question_id[]' value='16773' \/><div class='watu-questions-wrap '><input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='64830' \/><div class='watu-question-choice'><input type='checkbox' name='answer-16773[]' id='answer-id-64830' class='answer answer-3 php-answer-label answerof-16773' value='64830' \/>&nbsp;<label for='answer-id-64830' id='answer-label-64830' class='php-answer-label answer label-3'><span class='answer'>Use Snowflake&#8217;s masking policies to redact PII columns before any data is accessed for model training. Ensure role-based access control is configured so that only authorized personnel can access the unmasked data for specific purposes.<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='64831' \/><div class='watu-question-choice'><input type='checkbox' name='answer-16773[]' id='answer-id-64831' class='answer answer-3 js-answer-label answerof-16773' value='64831' \/>&nbsp;<label for='answer-id-64831' id='answer-label-64831' class='js-answer-label answer label-3'><span class='answer'>Create a view that selects only the non-PII columns for model training. Grant access to this view to the data science team.<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='64832' \/><div class='watu-question-choice'><input type='checkbox' name='answer-16773[]' id='answer-id-64832' class='answer answer-3 js-answer-label answerof-16773' value='64832' \/>&nbsp;<label for='answer-id-64832' id='answer-label-64832' class='js-answer-label answer label-3'><span class='answer'>Encrypt the entire database containing the transaction data to protect PII from unauthorized access.<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='64833' \/><div class='watu-question-choice'><input type='checkbox' name='answer-16773[]' id='answer-id-64833' class='answer answer-3 js-answer-label answerof-16773' value='64833' \/>&nbsp;<label for='answer-id-64833' id='answer-label-64833' class='js-answer-label answer label-3'><span class='answer'>Use Snowflake&#8217;s data sharing capabilities to share the transaction data with a third-party machine learning platform for model development, without any PII masking or redaction.<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='64834' \/><div class='watu-question-choice'><input type='checkbox' name='answer-16773[]' id='answer-id-64834' class='answer answer-3 php-answer-label answerof-16773' value='64834' \/>&nbsp;<label for='answer-id-64834' id='answer-label-64834' class='php-answer-label answer label-3'><span class='answer'>Apply differential privacy techniques on aggregated data derived from the transaction data, before using it for model training. Combine this with Snowflake&#8217;s row access policies to restrict access to sensitive transaction records based on user roles and data attributes.<\/span><\/label><\/div>\n<\/div><div class='show-question-feedback' style='display:none;'>Options A and E are the MOST suitable. Option A directly addresses PII protection by leveraging Snowflake&#8217;s masking policies to redact sensitive data before it is used for model training. Role-based access control provides an additional layer of security by limiting access to the unmasked data. Option E applies differential privacy to protect individual transaction data while still enabling useful model training and combines it with Row Access policies to restrict access to sensitive transaction records. Option B is partially correct but insufficient, as it only addresses which columns are seen, not protection within those columns. Option C protects the entire database but doesn&#8217;t address PII handling during model training. Option D is highly risky and non-compliant, as it exposes PII to a third party without adequate protection.<\/div><input type='button' class='showchecked' style='margin: 10px 0;' onclick='showanswer1(3,this)' id='btn-3' value='See Answer'  \/><input type='hidden' id='questionType3' value='checkbox' class=''><\/div><div class='watu-question' id='question-4'><div class='question-content'><p><strong>Q111.<\/strong> A data scientist is analyzing sales data in Snowflake to identify seasonal trends. The &#8216;SALES TABLE&#8217; contains columns &#8216;SALE DATE&#8217; (DATE) and &#8216;SALE _ AMOUNT&#8217; (NUMBER). They want to calculate the average daily sales amount for each month and year in the dataset. Which of the following SQL queries will correctly achieve this, while also handling potential NULL values in &#8216;SALE AMOUNT?<br \/><img decoding=\"async\" src=\"https:\/\/blog.topexamcollection.com\/wp-content\/uploads\/2025\/08\/DSA-C03-9f262467d179d218a9a4a2bbd452df6d.jpg\"\/><\/p>\n<\/div><input type='hidden' name='question_id[]' value='16774' \/><div class='watu-questions-wrap '><input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='64835' \/><div class='watu-question-choice'><input type='checkbox' name='answer-16774[]' id='answer-id-64835' class='answer answer-4 js-answer-label answerof-16774' value='64835' \/>&nbsp;<label for='answer-id-64835' id='answer-label-64835' class='js-answer-label answer label-4'><span class='answer'>Option A<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='64836' \/><div class='watu-question-choice'><input type='checkbox' name='answer-16774[]' id='answer-id-64836' class='answer answer-4 php-answer-label answerof-16774' value='64836' \/>&nbsp;<label for='answer-id-64836' id='answer-label-64836' class='php-answer-label answer label-4'><span class='answer'>Option B<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='64837' \/><div class='watu-question-choice'><input type='checkbox' name='answer-16774[]' id='answer-id-64837' class='answer answer-4 js-answer-label answerof-16774' value='64837' \/>&nbsp;<label for='answer-id-64837' id='answer-label-64837' class='js-answer-label answer label-4'><span class='answer'>Option C<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='64838' \/><div class='watu-question-choice'><input type='checkbox' name='answer-16774[]' id='answer-id-64838' class='answer answer-4 php-answer-label answerof-16774' value='64838' \/>&nbsp;<label for='answer-id-64838' id='answer-label-64838' class='php-answer-label answer label-4'><span class='answer'>Option D<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='64839' \/><div class='watu-question-choice'><input type='checkbox' name='answer-16774[]' id='answer-id-64839' class='answer answer-4 php-answer-label answerof-16774' value='64839' \/>&nbsp;<label for='answer-id-64839' id='answer-label-64839' class='php-answer-label answer label-4'><span class='answer'>Option E<\/span><\/label><\/div>\n<\/div><div class='show-question-feedback' style='display:none;'>Options B, D and E correctly calculate the average daily sales for each month and year. Options B uses &#8216;COALESCE&#8217; to replace NULL SALE_AMOUNT values with 0 before calculating the average. Option D utilizes &#8216; NVL&#8217;, which is a synonym for COALESCE in Snowflake. Option E uses ZEROIFNULL&#8217; which is another way to handle NULL values. Option A does not handle NULL values, potentially skewing the average. Option C incorrectly uses &#8220;TO_CHAR which results in string format for date, but that is fine, it also tries to use &#8216;IFF which is acceptable to handle Null, but SIFF function may lead to string conversion issues when calculating the average.<\/div><input type='button' class='showchecked' style='margin: 10px 0;' onclick='showanswer1(4,this)' id='btn-4' value='See Answer'  \/><input type='hidden' id='questionType4' value='checkbox' class=''><\/div><div class='watu-question' id='question-5'><div class='question-content'><p><strong>Q112.<\/strong> You are a data scientist working with a Snowflake table named &#8216;CUSTOMER DATA&#8217; that contains a &#8216;PHONE NUMBER&#8217; column stored as VARCHAR. The &#8216;PHONE NUMBER&#8217; column sometimes contains non-numeric characters like hyphens and parentheses, and in some rows the data is missing. You need to create a new table &#8216;CLEANED CUSTOMER DATA&#8217; with a column named &#8216;CLEANED PHONE NUMBER that contains only the numeric part of the phone number (as VARCHAR) and replaces missing or invalid phone numbers with NULL. Which of the following Snowpark Python code snippets achieves this most efficiently, ensuring no errors occur during the data transformation, and considers Snowflake&#8217;s performance best practices?<br \/><img decoding=\"async\" src=\"https:\/\/blog.topexamcollection.com\/wp-content\/uploads\/2025\/08\/DSA-C03-8d28e8521d88c551e73e3686f9fd0611.jpg\"\/><\/p>\n<\/div><input type='hidden' name='question_id[]' value='16775' \/><div class='watu-questions-wrap '><input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='64840' \/><div class='watu-question-choice'><input type='radio' name='answer-16775[]' id='answer-id-64840' class='answer answer-5 js-answer-label answerof-16775' value='64840' \/>&nbsp;<label for='answer-id-64840' id='answer-label-64840' class='js-answer-label answer label-5'><span class='answer'>Option A<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='64841' \/><div class='watu-question-choice'><input type='radio' name='answer-16775[]' id='answer-id-64841' class='answer answer-5 js-answer-label answerof-16775' value='64841' \/>&nbsp;<label for='answer-id-64841' id='answer-label-64841' class='js-answer-label answer label-5'><span class='answer'>Option B<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='64842' \/><div class='watu-question-choice'><input type='radio' name='answer-16775[]' id='answer-id-64842' class='answer answer-5 js-answer-label answerof-16775' value='64842' \/>&nbsp;<label for='answer-id-64842' id='answer-label-64842' class='js-answer-label answer label-5'><span class='answer'>Option C<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='64843' \/><div class='watu-question-choice'><input type='radio' name='answer-16775[]' id='answer-id-64843' class='answer answer-5 js-answer-label answerof-16775' value='64843' \/>&nbsp;<label for='answer-id-64843' id='answer-label-64843' class='js-answer-label answer label-5'><span class='answer'>Option D<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='64844' \/><div class='watu-question-choice'><input type='radio' name='answer-16775[]' id='answer-id-64844' class='answer answer-5 php-answer-label answerof-16775' value='64844' \/>&nbsp;<label for='answer-id-64844' id='answer-label-64844' class='php-answer-label answer label-5'><span class='answer'>Option E<\/span><\/label><\/div>\n<\/div><div class='show-question-feedback' style='display:none;'>Option E is the most efficient because it leverages Snowpark&#8217;s built-in functions for string manipulation and conditional logic directly. It first removes all non-numeric characters using &#8216;regexp_replace&#8217; and then uses &#8216;iff (if and only if) to replace empty strings (resulting from cleaning) with NULL. This approach avoids using UDFs (User-Defined Functions), which can introduce overhead. Option B, although using &#8216;regexp_replace&#8217; , requires an additional &#8216;with_column&#8217; to handle empty strings after cleaning. Option A introduces UDF that decreases performance. Option C calls UDF with undefined &#8216;call_udf function and &#8216;snowflake-snowpark-python&#8217; library. Option D is missing dataframe and its transformation is not happening on top of Dataframe. Option E is preferrable over Option B, as it uses the single transformation.<\/div><input type='button' class='showchecked' style='margin: 10px 0;' onclick='showanswer1(5,this)' id='btn-5' value='See Answer'  \/><input type='hidden' id='questionType5' value='radio' class=''><\/div><div class='watu-question' id='question-6'><div class='question-content'><p><strong>Q113.<\/strong> You are building a predictive model for customer churn using linear regression in Snowflake. You have identified several features, including &#8216;CUSTOMER AGE&#8217;, &#8216;MONTHLY SPEND&#8217;, and &#8216;NUM CALLS&#8217;. After performing an initial linear regression, you suspect that the relationship between &#8216;CUSTOMER AGE and churn is not linear and that older customers might churn at a different rate than younger customers. You want to introduce a polynomial feature of &#8220;CUSTOMER AGE (specifically, &#8216;CUSTOMER AGE SQUARED&#8217;) to your regression model within Snowflake SQL before further analysis with python and Snowpark. How can you BEST create this new feature in a robust and maintainable way directly within Snowflake?<br \/><img decoding=\"async\" src=\"https:\/\/blog.topexamcollection.com\/wp-content\/uploads\/2025\/08\/DSA-C03-58464587c31a547ab94e4039c569db69.jpg\"\/><\/p>\n<\/div><input type='hidden' name='question_id[]' value='16776' \/><div class='watu-questions-wrap '><input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='64845' \/><div class='watu-question-choice'><input type='radio' name='answer-16776[]' id='answer-id-64845' class='answer answer-6 js-answer-label answerof-16776' value='64845' \/>&nbsp;<label for='answer-id-64845' id='answer-label-64845' class='js-answer-label answer label-6'><span class='answer'>Option A<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='64846' \/><div class='watu-question-choice'><input type='radio' name='answer-16776[]' id='answer-id-64846' class='answer answer-6 js-answer-label answerof-16776' value='64846' \/>&nbsp;<label for='answer-id-64846' id='answer-label-64846' class='js-answer-label answer label-6'><span class='answer'>Option B<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='64847' \/><div class='watu-question-choice'><input type='radio' name='answer-16776[]' id='answer-id-64847' class='answer answer-6 php-answer-label answerof-16776' value='64847' \/>&nbsp;<label for='answer-id-64847' id='answer-label-64847' class='php-answer-label answer label-6'><span class='answer'>Option C<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='64848' \/><div class='watu-question-choice'><input type='radio' name='answer-16776[]' id='answer-id-64848' class='answer answer-6 js-answer-label answerof-16776' value='64848' \/>&nbsp;<label for='answer-id-64848' id='answer-label-64848' class='js-answer-label answer label-6'><span class='answer'>Option D<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='64849' \/><div class='watu-question-choice'><input type='radio' name='answer-16776[]' id='answer-id-64849' class='answer answer-6 js-answer-label answerof-16776' value='64849' \/>&nbsp;<label for='answer-id-64849' id='answer-label-64849' class='js-answer-label answer label-6'><span class='answer'>Option E<\/span><\/label><\/div>\n<\/div><div class='show-question-feedback' style='display:none;'>Creating a VIEW (option C) is the BEST approach for several reasons. It doesn&#8217;t modify the underlying data, which is crucial for data govemance and prevents unintended side effects. The feature is calculated on-the-fly whenever the view is queried, ensuring that the feature is always up-to-date if the underlying changes. Options A, D, and E permanently alter the table, potentially leading to data redundancy and requiring manual updates if the column changes. Option B creates a temporary table, which is suitable for short-lived experiments but not ideal for a feature that will be used repeatedly. Using 2) is equivalent to CUSTOMER_AGE CUSTOMER_AGE. Views are efficient because Snowflake&#8217;s query optimizer can often push down computations into the underlying table. Option C also avoids needing to manage the lifecycle of updated calculated columns.<\/div><input type='button' class='showchecked' style='margin: 10px 0;' onclick='showanswer1(6,this)' id='btn-6' value='See Answer'  \/><input type='hidden' id='questionType6' value='radio' class=''><\/div><div class='watu-question' id='question-7'><div class='question-content'><p><strong>Q114.<\/strong> You are developing a churn prediction model using Snowpark Python and Scikit-learn. After initial model training, you observe significant overfitting. Which of the following hyperparameter tuning strategies and code snippets, when implemented within a Snowflake Python UDF, would be MOST effective to address overfitting in a Ridge Regression model and how can you implement a reproducible model with minimal code?<br \/><img decoding=\"async\" src=\"https:\/\/blog.topexamcollection.com\/wp-content\/uploads\/2025\/08\/DSA-C03-0d5cc6b49a867c7a606679c25ad7180e.jpg\"\/><\/p>\n<\/div><input type='hidden' name='question_id[]' value='16777' \/><div class='watu-questions-wrap '><input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='64850' \/><div class='watu-question-choice'><input type='checkbox' name='answer-16777[]' id='answer-id-64850' class='answer answer-7 js-answer-label answerof-16777' value='64850' \/>&nbsp;<label for='answer-id-64850' id='answer-label-64850' class='js-answer-label answer label-7'><span class='answer'>Option A<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='64851' \/><div class='watu-question-choice'><input type='checkbox' name='answer-16777[]' id='answer-id-64851' class='answer answer-7 php-answer-label answerof-16777' value='64851' \/>&nbsp;<label for='answer-id-64851' id='answer-label-64851' class='php-answer-label answer label-7'><span class='answer'>Option B<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='64852' \/><div class='watu-question-choice'><input type='checkbox' name='answer-16777[]' id='answer-id-64852' class='answer answer-7 js-answer-label answerof-16777' value='64852' \/>&nbsp;<label for='answer-id-64852' id='answer-label-64852' class='js-answer-label answer label-7'><span class='answer'>Option C<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='64853' \/><div class='watu-question-choice'><input type='checkbox' name='answer-16777[]' id='answer-id-64853' class='answer answer-7 php-answer-label answerof-16777' value='64853' \/>&nbsp;<label for='answer-id-64853' id='answer-label-64853' class='php-answer-label answer label-7'><span class='answer'>Option D<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='64854' \/><div class='watu-question-choice'><input type='checkbox' name='answer-16777[]' id='answer-id-64854' class='answer answer-7 js-answer-label answerof-16777' value='64854' \/>&nbsp;<label for='answer-id-64854' id='answer-label-64854' class='js-answer-label answer label-7'><span class='answer'>Option E<\/span><\/label><\/div>\n<\/div><div class='show-question-feedback' style='display:none;'>Options B and D are correct because they employ techniques to mitigate overfitting. Option B uses &#8216; RandomizedSearchCV&#8217; with cross-validation and a fixed &#8216;random_state&#8217; , making the search reproducible and preventing overfitting by evaluating performance on multiple validation sets. Option D leverages &#8216;BayesianSearchCV&#8217; , which uses a probabilistic model to efficiently explore the hyperparameter space, also with cross-validation and a fixed random state making search reproducible. Both methods aim to find a balance between model complexity and generalization ability. Option A is incorrect because it does not use cross-validation, which is crucial for preventing overfitting. Option C is incorrect because manual tuning without a systematic search and cross-validation is prone to bias and overfitting. Finally, option E is incorrect because while using a modern algorithm, it lacks a random state, making it difficult to reproduce the outcome.<\/div><input type='button' class='showchecked' style='margin: 10px 0;' onclick='showanswer1(7,this)' id='btn-7' value='See Answer'  \/><input type='hidden' id='questionType7' value='checkbox' class=''><\/div><div class='watu-question' id='question-8'><div class='question-content'><p><strong>Q115.<\/strong> You are performing exploratory data analysis on a large sales dataset in Snowflake using Snowpark. The dataset contains columns such as &#8216;order_id&#8217;, , and &#8216;profit&#8217;. You want to identify the top 5 most profitable products for each month. You have already created a Snowpark DataFrame named &#8216;sales_df. Which of the following Snowpark operations, when combined correctly, will efficiently achieve this?<\/p>\n<\/div><input type='hidden' name='question_id[]' value='16778' \/><div class='watu-questions-wrap '><input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='64855' \/><div class='watu-question-choice'><input type='radio' name='answer-16778[]' id='answer-id-64855' class='answer answer-8 php-answer-label answerof-16778' value='64855' \/>&nbsp;<label for='answer-id-64855' id='answer-label-64855' class='php-answer-label answer label-8'><span class='answer'>Group by and &#8216;product_id&#8217; , aggregate &#8216;sum(profit)&#8217; , then use partitioned by ordered by &#8216;sum(profit) DESC&#8217;.<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='64856' \/><div class='watu-question-choice'><input type='radio' name='answer-16778[]' id='answer-id-64856' class='answer answer-8 js-answer-label answerof-16778' value='64856' \/>&nbsp;<label for='answer-id-64856' id='answer-label-64856' class='js-answer-label answer label-8'><span class='answer'>Use &#8216;rank()&#8217; partitioned by ordered by &#8216;sum(profit) DESC&#8217; , after grouping by and &#8216;product_id&#8217; , and aggregating &#8216;sum(profity.<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='64857' \/><div class='watu-question-choice'><input type='radio' name='answer-16778[]' id='answer-id-64857' class='answer answer-8 js-answer-label answerof-16778' value='64857' \/>&nbsp;<label for='answer-id-64857' id='answer-label-64857' class='js-answer-label answer label-8'><span class='answer'>First, create a temporary table with aggregated monthly profit for each product using SQL. Then, use Snowpark to read the temporary table and apply a window function partitioned by ordered by &#8216;sum(profit) DESC&#8217;.<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='64858' \/><div class='watu-question-choice'><input type='radio' name='answer-16778[]' id='answer-id-64858' class='answer answer-8 js-answer-label answerof-16778' value='64858' \/>&nbsp;<label for='answer-id-64858' id='answer-label-64858' class='js-answer-label answer label-8'><span class='answer'>Group by &#8216;product_id&#8217;, aggregate &#8216;sum(profity, then use partitioned by ordered by &#8216;sum(profit) DESC&#8217; within a UDF.<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='64859' \/><div class='watu-question-choice'><input type='radio' name='answer-16778[]' id='answer-id-64859' class='answer answer-8 js-answer-label answerof-16778' value='64859' \/>&nbsp;<label for='answer-id-64859' id='answer-label-64859' class='js-answer-label answer label-8'><span class='answer'>Use &#8216;ntile(5)&#8217; partitioned by ordered by &#8216;sum(profit) DESC&#8217; after grouping by and &#8216;product_id&#8217;, and aggregating &#8216;sum(profit)&#8217;.<\/span><\/label><\/div>\n<\/div><div class='show-question-feedback' style='display:none;'>Option A correctly describes the process. First group by month and product to calculate total profit, then use with correct partitioning and ordering to assign a rank within each month based on profit. Options B and C use less efficient ranking functions. Option D groups by product globally, missing the monthly granularity. Option E &#8216;ntile&#8217; divides products into 5 buckets which is not what we are looking for.<\/div><input type='button' class='showchecked' style='margin: 10px 0;' onclick='showanswer1(8,this)' id='btn-8' value='See Answer'  \/><input type='hidden' id='questionType8' value='radio' class=''><\/div><div class='watu-question' id='question-9'><div class='question-content'><p><strong>Q116.<\/strong> You are tasked with training a logistic regression model in Snowflake using Snowpark Python to predict customer churn. Your data is stored in a table named &#8216;CUSTOMER DATA&#8217; with columns like &#8216;CUSTOMER D&#8217;, &#8216;FEATURE 1&#8217;, &#8216;FEATURE 2&#8217;, &#8216;FEATURE 3&#8217;, and &#8216;CHURN FLAG&#8217; (boolean representing churn). You plan to use stratified k-fold cross-validation to ensure each fold has a representative proportion of churned and non-churned customers. Which of the following code snippets demonstrates the correct way to perform stratified k-fold cross-validation with Snowpark ML? (Assume &#8216;snowpark_session&#8217; is a valid Snowpark session object).<\/p>\n<\/div><input type='hidden' name='question_id[]' value='16779' \/><div class='watu-questions-wrap '><input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='64860' \/><div class='watu-question-choice'><input type='radio' name='answer-16779[]' id='answer-id-64860' class='answer answer-9 js-answer-label answerof-16779' value='64860' \/>&nbsp;<label for='answer-id-64860' id='answer-label-64860' class='js-answer-label answer label-9'><span class='answer'><img decoding=\"async\" src=\"https:\/\/blog.topexamcollection.com\/wp-content\/uploads\/2025\/08\/DSA-C03-2428663a8b33978b6fbb5b84b49697fa.jpg\"\/><\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='64861' \/><div class='watu-question-choice'><input type='radio' name='answer-16779[]' id='answer-id-64861' class='answer answer-9 js-answer-label answerof-16779' value='64861' \/>&nbsp;<label for='answer-id-64861' id='answer-label-64861' class='js-answer-label answer label-9'><span class='answer'><img decoding=\"async\" src=\"https:\/\/blog.topexamcollection.com\/wp-content\/uploads\/2025\/08\/DSA-C03-26c6d5b1f39c9d3514b473c1a77d197c.jpg\"\/><\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='64862' \/><div class='watu-question-choice'><input type='radio' name='answer-16779[]' id='answer-id-64862' class='answer answer-9 js-answer-label answerof-16779' value='64862' \/>&nbsp;<label for='answer-id-64862' id='answer-label-64862' class='js-answer-label answer label-9'><span class='answer'><img decoding=\"async\" src=\"https:\/\/blog.topexamcollection.com\/wp-content\/uploads\/2025\/08\/DSA-C03-89a0efce65b694f60c1da12594ac4c33.jpg\"\/><\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='64863' \/><div class='watu-question-choice'><input type='radio' name='answer-16779[]' id='answer-id-64863' class='answer answer-9 js-answer-label answerof-16779' value='64863' \/>&nbsp;<label for='answer-id-64863' id='answer-label-64863' class='js-answer-label answer label-9'><span class='answer'><img decoding=\"async\" src=\"https:\/\/blog.topexamcollection.com\/wp-content\/uploads\/2025\/08\/DSA-C03-a9990c0160d44aecb27f4b428b60655c.jpg\"\/><\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='64864' \/><div class='watu-question-choice'><input type='radio' name='answer-16779[]' id='answer-id-64864' class='answer answer-9 php-answer-label answerof-16779' value='64864' \/>&nbsp;<label for='answer-id-64864' id='answer-label-64864' class='php-answer-label answer label-9'><span class='answer'><img decoding=\"async\" src=\"https:\/\/blog.topexamcollection.com\/wp-content\/uploads\/2025\/08\/DSA-C03-c735f65433f8813dcef22812e3e11fce.jpg\"\/><\/span><\/label><\/div>\n<\/div><div class='show-question-feedback' style='display:none;'>Option E is the only correct code snippet. Here&#8217;s why: StratifiedKFold: It uses &#8216;StratifiedKFold&#8217; from , which is necessary for ensuring that each fold has a similar class distribution. Pandas Conversion: The stratified k-fold split function requires Pandas dataframes as input, so tables &#8216;CUSTOMER_DATA&#8217; is converted to Pandas DataFrame. Correct Data Preparation: The code splits features and labels correctly and passes them to StratifiedKFold&#8217;. The train and test indices derived from skf.split can be used to slice pandas dataframe and assign it to the correct variables. The ravel() converts the y into a ID array which is what is expected by the split method Snowflake ML Model Training: The &#8216;LogisticRegression&#8217; model is fit and scored within the loop using the correct data. Other options are incorrect because: A: Uses KFold instead of StratifiedKFold, so does not stratify. Does not properly handle indices derived from the folds. B: Uses StratifiedKFold but does not properly handle indices derived from the folds, and doesn&#8217;t use Pandas. C: Uses Pandas but doesn&#8217;t pass proper input features, meaning split won&#8217;t work. Also, handles indices improperly D: Improperly uses functions from Snowpark and doesn&#8217;t use Pandas. Also, handles indices improperly.<\/div><input type='button' class='showchecked' style='margin: 10px 0;' onclick='showanswer1(9,this)' id='btn-9' value='See Answer'  \/><input type='hidden' id='questionType9' value='radio' class=''><\/div><div class='watu-question' id='question-10'><div class='question-content'><p><strong>Q117.<\/strong> You are evaluating a binary classification model&#8217;s performance using the Area Under the ROC Curve (AUC). You have the following predictions and actual values. What steps can you take to reliably calculate this in Snowflake, and which snippet represents a crucial part of that calculation? (Assume tables &#8216;predictions&#8217; with columns &#8216;predicted_probability&#8217; (FLOAT) and &#8216;actual_value&#8217; (BOOLEAN); TRUE indicates positive class, FALSE indicates negative class). Which of the below code snippet should be used to calculate the &#8216;True positive Rate&#8217; and &#8216;False positive Rate&#8217; for different thresholds<\/p>\n<\/div><input type='hidden' name='question_id[]' value='16780' \/><div class='watu-questions-wrap '><input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='64865' \/><div class='watu-question-choice'><input type='checkbox' name='answer-16780[]' id='answer-id-64865' class='answer answer-10 php-answer-label answerof-16780' value='64865' \/>&nbsp;<label for='answer-id-64865' id='answer-label-64865' class='php-answer-label answer label-10'><span class='answer'>Calculate AUC directly within a Snowpark Python UDF using scikit-learn&#8217;s function. This avoids data transfer overhead, making it highly efficient for large datasets. No further SQL is needed beyond querying the predictions data.<br \/><img decoding=\"async\" src=\"https:\/\/blog.topexamcollection.com\/wp-content\/uploads\/2025\/08\/DSA-C03-5aaf5f0c5ceb005de87caa8b9c0def3f.jpg\"\/><\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='64866' \/><div class='watu-question-choice'><input type='checkbox' name='answer-16780[]' id='answer-id-64866' class='answer answer-10 js-answer-label answerof-16780' value='64866' \/>&nbsp;<label for='answer-id-64866' id='answer-label-64866' class='js-answer-label answer label-10'><span class='answer'>The AUC cannot be reliably calculated within Snowflake due to limitations in SQL functionality for statistical analysis.<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='64867' \/><div class='watu-question-choice'><input type='checkbox' name='answer-16780[]' id='answer-id-64867' class='answer answer-10 php-answer-label answerof-16780' value='64867' \/>&nbsp;<label for='answer-id-64867' id='answer-label-64867' class='php-answer-label answer label-10'><span class='answer'>Using only SQL, Create a temporary table with calculated True Positive Rate (TPR) and False Positive Rate (FPR) at different probability thresholds. Then, approximate the AUC using the trapezoidal rule.<br \/><img decoding=\"async\" src=\"https:\/\/blog.topexamcollection.com\/wp-content\/uploads\/2025\/08\/DSA-C03-86061b10ba3d7f5be83c5cde4f6f3ee7.jpg\"\/><\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='64868' \/><div class='watu-question-choice'><input type='checkbox' name='answer-16780[]' id='answer-id-64868' class='answer answer-10 js-answer-label answerof-16780' value='64868' \/>&nbsp;<label for='answer-id-64868' id='answer-label-64868' class='js-answer-label answer label-10'><span class='answer'>Export the &#8216;predicted_probability&#8217; and &#8216;actual_value&#8217; columns to a local Python environment and calculate the AUC using scikit-learn.<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='64869' \/><div class='watu-question-choice'><input type='checkbox' name='answer-16780[]' id='answer-id-64869' class='answer answer-10 js-answer-label answerof-16780' value='64869' \/>&nbsp;<label for='answer-id-64869' id='answer-label-64869' class='js-answer-label answer label-10'><span class='answer'>The best way to calculate AUC is to randomly guess the probabilities and see how it performs.<\/span><\/label><\/div>\n<\/div><div class='show-question-feedback' style='display:none;'>Options A and C are correct. Option A demonstrates calculating AUC directly within Snowflake using a Snowpark Python UDF and scikit-learn&#8217;s . This is efficient for large datasets as it avoids data transfer. Option C correctly outlines the process of calculating TPR and FPR using SQL and approximating AUC using the trapezoidal rule, another viable approach within Snowflake. Option B is incorrect; AUC can be calculated reliably within Snowflake. Option D is inefficient due to data transfer. Option E is blatantly incorrect.<\/div><input type='button' class='showchecked' style='margin: 10px 0;' onclick='showanswer1(10,this)' id='btn-10' value='See Answer'  \/><input type='hidden' id='questionType10' value='checkbox' class=''><\/div><div class='watu-question' id='question-11'><div class='question-content'><p><strong>Q118.<\/strong> A marketing analyst is building a propensity model to predict customer response to a new product launch. The dataset contains a &#8216;City&#8217; column with a large number of unique city names. Applying one-hot encoding to this feature would result in a very high-dimensional dataset, potentially leading to the curse of dimensionality. To mitigate this, the analyst decides to combine Label Encoding followed by binarization techniques. Which of the following statements are TRUE regarding the benefits and challenges of this combined approach in Snowflake compared to simply label encoding?<\/p>\n<\/div><input type='hidden' name='question_id[]' value='16781' \/><div class='watu-questions-wrap '><input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='64870' \/><div class='watu-question-choice'><input type='checkbox' name='answer-16781[]' id='answer-id-64870' class='answer answer-11 php-answer-label answerof-16781' value='64870' \/>&nbsp;<label for='answer-id-64870' id='answer-label-64870' class='php-answer-label answer label-11'><span class='answer'>Label encoding followed by binarization will reduce the memory required to store the &#8216;City&#8217; feature compared to one-hot encoding, and Snowflake&#8217;s columnar storage optimizes storage for integer data types used in label encoding.<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='64871' \/><div class='watu-question-choice'><input type='checkbox' name='answer-16781[]' id='answer-id-64871' class='answer answer-11 php-answer-label answerof-16781' value='64871' \/>&nbsp;<label for='answer-id-64871' id='answer-label-64871' class='php-answer-label answer label-11'><span class='answer'>Label encoding introduces an arbitrary ordinal relationship between the cities, which may not be appropriate. Binarization alone cannot remove this artifact.<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='64872' \/><div class='watu-question-choice'><input type='checkbox' name='answer-16781[]' id='answer-id-64872' class='answer answer-11 js-answer-label answerof-16781' value='64872' \/>&nbsp;<label for='answer-id-64872' id='answer-label-64872' class='js-answer-label answer label-11'><span class='answer'>While label encoding itself adds an ordinal relationship, applying binarization techniques like binary encoding (converting the label to binary representation and splitting into multiple columns) after label encoding will remove the arbitrary ordinal relationship.<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='64873' \/><div class='watu-question-choice'><input type='checkbox' name='answer-16781[]' id='answer-id-64873' class='answer answer-11 php-answer-label answerof-16781' value='64873' \/>&nbsp;<label for='answer-id-64873' id='answer-label-64873' class='php-answer-label answer label-11'><span class='answer'>Binarizing a label encoded column using a simple threshold (e.g., creating a &#8216;high_city_id&#8217; flag) addresses the curse of dimensionality by reducing the number of features to one, but it loses significant information about the individual cities.<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='64874' \/><div class='watu-question-choice'><input type='checkbox' name='answer-16781[]' id='answer-id-64874' class='answer answer-11 php-answer-label answerof-16781' value='64874' \/>&nbsp;<label for='answer-id-64874' id='answer-label-64874' class='php-answer-label answer label-11'><span class='answer'>Binarization following label encoding may enhance model performance if a specific split based on a defined threshold is meaningful for the target variable (e.g., distinguishing between cities above\/below a certain average income level related to marketing success).<\/span><\/label><\/div>\n<\/div><div class='show-question-feedback' style='display:none;'>Option A is true because label encoding converts strings into integers, which are more memory-efficient than storing numerous one-hot encoded columns. Snowflake&#8217;s columnar storage further optimizes integer storage. Option B is also true; label encoding inherently creates an ordinal relationship that might not be valid for nominal features like city names. Option C is incorrect; simple binarization (e.g., &gt; threshold) of label encoded data doesn&#8217;t remove the arbitrary ordinal relationship; more complex binarization techniques would be needed. Option D is accurate; binarization reduces dimensionality but sacrifices granularity, leading to information loss. Option E is correct because carefully chosen thresholds might correlate with the target variable and improve predictive power.<\/div><input type='button' class='showchecked' style='margin: 10px 0;' onclick='showanswer1(11,this)' id='btn-11' value='See Answer'  \/><input type='hidden' id='questionType11' value='checkbox' class=''><\/div><div class='watu-question' id='question-12'><div class='question-content'><p><strong>Q119.<\/strong> You are building a binary classification model in Snowflake to predict customer churn based on historical customer data, including demographics, purchase history, and engagement metrics. You are using the SNOWFLAKE.ML.ANOMALY package. You notice a significant class imbalance, with churn representing only 5% of your dataset. Which of the following techniques is LEAST appropriate to handle this class imbalance effectively within the SNOWFLAKE.ML framework for structured data and to improve the model&#8217;s performance on the minority (churn) class?<\/p>\n<\/div><input type='hidden' name='question_id[]' value='16782' \/><div class='watu-questions-wrap '><input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='64875' \/><div class='watu-question-choice'><input type='radio' name='answer-16782[]' id='answer-id-64875' class='answer answer-12 js-answer-label answerof-16782' value='64875' \/>&nbsp;<label for='answer-id-64875' id='answer-label-64875' class='js-answer-label answer label-12'><span class='answer'>Using the &#8216;sample_weight&#8217; parameter in the &#8216;SNOWFLAKE.ML.ANOMALY.FIT function to assign higher weights to the minority class instances during model training.<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='64876' \/><div class='watu-question-choice'><input type='radio' name='answer-16782[]' id='answer-id-64876' class='answer answer-12 js-answer-label answerof-16782' value='64876' \/>&nbsp;<label for='answer-id-64876' id='answer-label-64876' class='js-answer-label answer label-12'><span class='answer'>Applying a SMOTE (Synthetic Minority Over-sampling Technique) or similar oversampling technique to generate synthetic samples of the minority class before training the model outside of Snowflake, and then loading the augmented data into Snowflake for model training.<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='64877' \/><div class='watu-question-choice'><input type='radio' name='answer-16782[]' id='answer-id-64877' class='answer answer-12 js-answer-label answerof-16782' value='64877' \/>&nbsp;<label for='answer-id-64877' id='answer-label-64877' class='js-answer-label answer label-12'><span class='answer'>Adjusting the decision threshold of the trained model to optimize for a specific metric, such as precision or recall, using a validation set. This can be done by examining the probability outputs and choosing a threshold that maximizes the desired balance.<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='64878' \/><div class='watu-question-choice'><input type='radio' name='answer-16782[]' id='answer-id-64878' class='answer answer-12 js-answer-label answerof-16782' value='64878' \/>&nbsp;<label for='answer-id-64878' id='answer-label-64878' class='js-answer-label answer label-12'><span class='answer'>Downsampling the majority class to create a more balanced training dataset within Snowflake using SQL before feeding the data to the modeling function.<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='64879' \/><div class='watu-question-choice'><input type='radio' name='answer-16782[]' id='answer-id-64879' class='answer answer-12 php-answer-label answerof-16782' value='64879' \/>&nbsp;<label for='answer-id-64879' id='answer-label-64879' class='php-answer-label answer label-12'><span class='answer'>Using a clustering algorithm (e.g., K-Means) on the features and then training a separate binary classification model for each cluster to capture potentially different patterns of churn within different customer segments.<\/span><\/label><\/div>\n<\/div><div class='show-question-feedback' style='display:none;'>E is the LEAST appropriate. While clustering and training separate models per cluster can be a useful strategy for improving overall model performance by capturing heterogeneous patterns, it doesn&#8217;t directly address the class imbalance problem within each cluster&#8217;s dataset. Applying clustering does nothing about the class imbalance and adds unnecessary complexity. A, B, C, and D are all standard methods for handling class imbalance. A uses weighted training. B and D address resampling of the training set. C addresses the classification threshold.<\/div><input type='button' class='showchecked' style='margin: 10px 0;' onclick='showanswer1(12,this)' id='btn-12' value='See Answer'  \/><input type='hidden' id='questionType12' value='radio' class=''><\/div><div class='watu-question' id='question-13'><div class='question-content'><p><strong>Q120.<\/strong> You are deploying a large language model (LLM) to Snowflake using a user-defined function (UDF). The LLM&#8217;s model file, &#8217;11m model.pt&#8217;, is quite large (5GB). You&#8217;ve staged the file to Which of the following strategies should you employ to ensure successful deployment and efficient inference within Snowflake? Select all that apply.<\/p>\n<\/div><input type='hidden' name='question_id[]' value='16783' \/><div class='watu-questions-wrap '><input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='64880' \/><div class='watu-question-choice'><input type='checkbox' name='answer-16783[]' id='answer-id-64880' class='answer answer-13 js-answer-label answerof-16783' value='64880' \/>&nbsp;<label for='answer-id-64880' id='answer-label-64880' class='js-answer-label answer label-13'><span class='answer'>Use the &#8216;PUT&#8217; command with to compress the model file before staging it. Snowflake will automatically decompress it during UDF execution.<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='64881' \/><div class='watu-question-choice'><input type='checkbox' name='answer-16783[]' id='answer-id-64881' class='answer answer-13 php-answer-label answerof-16783' value='64881' \/>&nbsp;<label for='answer-id-64881' id='answer-label-64881' class='php-answer-label answer label-13'><span class='answer'>Increase the warehouse size to XLARGE or larger to provide sufficient memory for loading the large model into the UDF environment.<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='64882' \/><div class='watu-question-choice'><input type='checkbox' name='answer-16783[]' id='answer-id-64882' class='answer answer-13 php-answer-label answerof-16783' value='64882' \/>&nbsp;<label for='answer-id-64882' id='answer-label-64882' class='php-answer-label answer label-13'><span class='answer'>Leverage Snowflake&#8217;s Snowpark Container Services to deploy the LLM as a separate containerized application and expose it via a Snowpark API. Then call that endpoint from snowflake.<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='64883' \/><div class='watu-question-choice'><input type='checkbox' name='answer-16783[]' id='answer-id-64883' class='answer answer-13 php-answer-label answerof-16783' value='64883' \/>&nbsp;<label for='answer-id-64883' id='answer-label-64883' class='php-answer-label answer label-13'><span class='answer'>Use the &#8216;IMPORTS&#8217; clause in the UDF definition to reference Ensure the UDF code loads the model lazily (i.e., only when it&#8217;s first needed) to minimize startup time and memory usage.<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='64884' \/><div class='watu-question-choice'><input type='checkbox' name='answer-16783[]' id='answer-id-64884' class='answer answer-13 js-answer-label answerof-16783' value='64884' \/>&nbsp;<label for='answer-id-64884' id='answer-label-64884' class='js-answer-label answer label-13'><span class='answer'>Split the large model file into smaller chunks and stage each chunk separately. Reassemble the model within the UDF code before inference.<\/span><\/label><\/div>\n<\/div><div class='show-question-feedback' style='display:none;'>Options B, C and D are correct. B: A large model requires sufficient memory, so using an XLARGE or larger warehouse is crucial. C: Snowpark Container Services are designed for such scenarios and is the recommended best practice. D: Specifying the model file as an import and using lazy loading helps manage memory efficiently. Option A can work, but since &#8216;Ilm_model.pt&#8217; is already compressed. Compressing again will be not efficient. Splitting the model into chunks (Option E) is overly complicated. Option C gives flexibility of calling out functions from containerized environment, so better scalability.<\/div><input type='button' class='showchecked' style='margin: 10px 0;' onclick='showanswer1(13,this)' id='btn-13' value='See Answer'  \/><input type='hidden' id='questionType13' value='checkbox' class=''><\/div><div class='watu-question' id='question-14'><div class='question-content'><p><strong>Q121.<\/strong> You are tasked with preparing a Snowflake table named &#8216;PRODUCT REVIEWS&#8217; for sentiment analysis. This table contains columns like &#8216;REVIEW ID, &#8216;PRODUCT ID&#8217;, &#8216;REVIEW TEXT&#8217;, &#8216;RATING&#8217;, and &#8216;TIMESTAMP&#8217;. Your goal is to remove irrelevant fields to optimize model training. Which of the following options represent valid and effective strategies, using Snowpark SQL, for identifying and removing irrelevant or problematic fields from the &#8216;PRODUCT REVIEWS&#8217; table, considering both storage efficiency and model accuracy? Assume that the model only need review text and review id and the rating.<\/p>\n<\/div><input type='hidden' name='question_id[]' value='16784' \/><div class='watu-questions-wrap '><input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='64885' \/><div class='watu-question-choice'><input type='radio' name='answer-16784[]' id='answer-id-64885' class='answer answer-14 js-answer-label answerof-16784' value='64885' \/>&nbsp;<label for='answer-id-64885' id='answer-label-64885' class='js-answer-label answer label-14'><span class='answer'>Using &#8216;ALTER TABLE DROP COLUMN&#8217; to directly remove &#8216;TIMESTAMP column, which is deemed irrelevant for the sentiment analysis model. SQL: &#8216;ALTER TABLE PRODUCT REVIEWS DROP COLUMN TIMESTAMP;&#8217;<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='64886' \/><div class='watu-question-choice'><input type='radio' name='answer-16784[]' id='answer-id-64886' class='answer answer-14 js-answer-label answerof-16784' value='64886' \/>&nbsp;<label for='answer-id-64886' id='answer-label-64886' class='js-answer-label answer label-14'><span class='answer'>Creating a VIEW that only selects the &#8216;REVIEW _ TEXT , &#8216;REVIEW_ID&#8217;, and &#8216;RATING&#8217; columns, effectively hiding the irrelevant columns from the model. SQL: &#8216;CREATE OR REPLACE VIEW REVIEWS FOR ANALYSIS AS SELECT REVIEW TEXT, REVIEW ID, RATING FROM PRODUCT REVIEWS;&#8217;<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='64887' \/><div class='watu-question-choice'><input type='radio' name='answer-16784[]' id='answer-id-64887' class='answer answer-14 js-answer-label answerof-16784' value='64887' \/>&nbsp;<label for='answer-id-64887' id='answer-label-64887' class='js-answer-label answer label-14'><span class='answer'>creating a new table &#8216;REVIEWS_CLEANED containing only the relevant columns CREVIEW_TEXT , &#8216;REVIEW_ID&#8217; , and &#8216;RATING&#8217;) using &#8216;CREATE TABLE AS SELECT. SQL: &#8216;CREATE OR REPLACE TABLE REVIEWS CLEANED AS SELECT REVIEW TEXT, REVIEW ID, RATING FROM PRODUCT REVIEWS;&#8217;<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='64888' \/><div class='watu-question-choice'><input type='radio' name='answer-16784[]' id='answer-id-64888' class='answer answer-14 js-answer-label answerof-16784' value='64888' \/>&nbsp;<label for='answer-id-64888' id='answer-label-64888' class='js-answer-label answer label-14'><span class='answer'>Dropping rows with &#8216;NULL&#8217; values in REVIEW_TEXT and then dropping the &#8216;PRODUCT_ID&#8217; and &#8216;TIMESTAMP&#8217; columns using &#8216;ALTER TABLE. SQL: &#8216;CREATE OR REPLACE TABLE PRODUCT REVIEWS AS SELECT FROM PRODUCT REVIEWS WHERE REVIEW TEXT IS NOT NULL; ALTER TABLE PRODUCT REVIEWS DROP COLUMN PRODUCT ID; ALTER TABLE PRODUCT REVIEWS DROP COLUMN TIMESTAMP;&#8217;<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='64889' \/><div class='watu-question-choice'><input type='radio' name='answer-16784[]' id='answer-id-64889' class='answer answer-14 php-answer-label answerof-16784' value='64889' \/>&nbsp;<label for='answer-id-64889' id='answer-label-64889' class='php-answer-label answer label-14'><span class='answer'>All of the above.<\/span><\/label><\/div>\n<\/div><div class='show-question-feedback' style='display:none;'>All of the options are valid strategies. A directly removes the irrelevant &#8216;TIMESTAMP&#8217; column, saving storage. B creates a VIEW which offers a non-destructive way to filter columns. C creates a new table with only the necessary columns. D handles rows with missing review text and removes other irrelevant columns. Therefore, choosing &#8216;All of the above&#8217; is the correct response. Depending on use case and downstream application we can make use of any of the options, hence more than one option is correct.<\/div><input type='button' class='showchecked' style='margin: 10px 0;' onclick='showanswer1(14,this)' id='btn-14' value='See Answer'  \/><input type='hidden' id='questionType14' value='radio' class=''><\/div><div class='watu-question' id='question-15'><div class='question-content'><p><strong>Q122.<\/strong> A data engineer is tasked with removing duplicates from a table named &#8216;USER ACTIVITY&#8217; in Snowflake, which contains user activity logs. The table has columns: &#8216;ACTIVITY TIMESTAMP&#8217;, &#8216;ACTIVITY TYPE&#8217;, and &#8216;DEVICE_ID. The data engineer wants to remove duplicate rows, considering only &#8216;USER ID&#8217;, &#8216;ACTIVITY TYPE, and &#8216;DEVICE_ID&#8217; columns. What is the most efficient and correct SQL query to achieve this while retaining only the earliest &#8216;ACTIVITY TIMESTAMP&#8217; for each unique combination of the specified columns?<br \/><img decoding=\"async\" src=\"https:\/\/blog.topexamcollection.com\/wp-content\/uploads\/2025\/08\/DSA-C03-53a9f551a8e4bb1e0de966dc330623cb.jpg\"\/><\/p>\n<\/div><input type='hidden' name='question_id[]' value='16785' \/><div class='watu-questions-wrap '><input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='64890' \/><div class='watu-question-choice'><input type='radio' name='answer-16785[]' id='answer-id-64890' class='answer answer-15 js-answer-label answerof-16785' value='64890' \/>&nbsp;<label for='answer-id-64890' id='answer-label-64890' class='js-answer-label answer label-15'><span class='answer'>Option A<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='64891' \/><div class='watu-question-choice'><input type='radio' name='answer-16785[]' id='answer-id-64891' class='answer answer-15 php-answer-label answerof-16785' value='64891' \/>&nbsp;<label for='answer-id-64891' id='answer-label-64891' class='php-answer-label answer label-15'><span class='answer'>Option B<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='64892' \/><div class='watu-question-choice'><input type='radio' name='answer-16785[]' id='answer-id-64892' class='answer answer-15 js-answer-label answerof-16785' value='64892' \/>&nbsp;<label for='answer-id-64892' id='answer-label-64892' class='js-answer-label answer label-15'><span class='answer'>Option C<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='64893' \/><div class='watu-question-choice'><input type='radio' name='answer-16785[]' id='answer-id-64893' class='answer answer-15 js-answer-label answerof-16785' value='64893' \/>&nbsp;<label for='answer-id-64893' id='answer-label-64893' class='js-answer-label answer label-15'><span class='answer'>Option D<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='64894' \/><div class='watu-question-choice'><input type='radio' name='answer-16785[]' id='answer-id-64894' class='answer answer-15 js-answer-label answerof-16785' value='64894' \/>&nbsp;<label for='answer-id-64894' id='answer-label-64894' class='js-answer-label answer label-15'><span class='answer'>Option E<\/span><\/label><\/div>\n<\/div><div class='show-question-feedback' style='display:none;'>Option B provides the most efficient and correct solution. &#8211; It uses the &#8216;QUALIFY&#8217; clause along with the window function to partition the data by &#8216;USER ID, &#8216;ACTIVITY TYPE, and &#8216;DEVICE ICY. Within each partition, it orders the rows by &#8216;ACTIVITY _ TIMESTAMP&#8217; in ascending order. The function assigns a unique rank to each row within the partition. The &#8216;QUALIFY clause filters the result set, keeping only the rows where the &#8216;ROW NUMBER()&#8217; is equal to 1, which effectively selects the earliest activity timestamp for each unique combination of &#8216;ACTIVITY _ TYPE , and &#8216;DEVICE_ID&#8217;. Option A is incorrect because it aggregates and only retains the minimum &#8216;ACTIVITY TIMESTAMP&#8217; , discarding other potentially relevant columns. Option C is incorrect because it only returns rows where a combination of &#8216;USER_ID, ACTIVITY_TYPE, DEVICE_ID, and ACTIVITY_TIMESTAMP&#8221; appears only once, not removing duplicates based on the desired columns. Option D is incorrect because it only selects distinct combinations of USER ID, ACTIVITY_TYPE, and DEVICE_ID, thus losing the ACTIVITY_TIMESTAMP. option E is incorrect. While it keeps the ACTIVITY_TIMESTAMP as the earliest, FIRST VALUE generates all other columns based on the input data which will generate duplicates.<\/div><input type='button' class='showchecked' style='margin: 10px 0;' onclick='showanswer1(15,this)' id='btn-15' value='See Answer'  \/><input type='hidden' id='questionType15' value='radio' class=''><\/div><div class='watu-question' id='question-16'><div class='question-content'><p><strong>Q123.<\/strong> You are building a machine learning pipeline that uses data stored in Snowflake. You want to connect a Jupyter Notebook running on your local machine to Snowflake using Snowpark. You need to securely authenticate to Snowflake and ensure that you are using a dedicated compute resource for your Snowpark session. Which of the following approaches is the MOST secure and efficient way to achieve this?<\/p>\n<\/div><input type='hidden' name='question_id[]' value='16786' \/><div class='watu-questions-wrap '><input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='64895' \/><div class='watu-question-choice'><input type='radio' name='answer-16786[]' id='answer-id-64895' class='answer answer-16 js-answer-label answerof-16786' value='64895' \/>&nbsp;<label for='answer-id-64895' id='answer-label-64895' class='js-answer-label answer label-16'><span class='answer'>Store your Snowflake username and password directly in the Jupyter Notebook and create a Snowpark session using these credentials and the default Snowflake warehouse.<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='64896' \/><div class='watu-question-choice'><input type='radio' name='answer-16786[]' id='answer-id-64896' class='answer answer-16 js-answer-label answerof-16786' value='64896' \/>&nbsp;<label for='answer-id-64896' id='answer-label-64896' class='js-answer-label answer label-16'><span class='answer'>Use the Snowflake Python connector with username and password and execute SQL commands to create a Snowpark DataFrame.<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='64897' \/><div class='watu-question-choice'><input type='radio' name='answer-16786[]' id='answer-id-64897' class='answer answer-16 js-answer-label answerof-16786' value='64897' \/>&nbsp;<label for='answer-id-64897' id='answer-label-64897' class='js-answer-label answer label-16'><span class='answer'>Configure OAuth authentication for your Snowflake account and use the OAuth token to establish a Snowpark session with a dedicated virtual warehouse.<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='64898' \/><div class='watu-question-choice'><input type='radio' name='answer-16786[]' id='answer-id-64898' class='answer answer-16 php-answer-label answerof-16786' value='64898' \/>&nbsp;<label for='answer-id-64898' id='answer-label-64898' class='php-answer-label answer label-16'><span class='answer'>Use key pair authentication to connect to Snowflake, storing the private key securely on your local machine. Specify a dedicated virtual warehouse during session creation.<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='64899' \/><div class='watu-question-choice'><input type='radio' name='answer-16786[]' id='answer-id-64899' class='answer answer-16 js-answer-label answerof-16786' value='64899' \/>&nbsp;<label for='answer-id-64899' id='answer-label-64899' class='js-answer-label answer label-16'><span class='answer'>Hardcode a role with &#8216;ACCOUNTADMIN&#8217; privileges in your Jupyter Notebook using username and password.<\/span><\/label><\/div>\n<\/div><div class='show-question-feedback' style='display:none;'>Option D is the most secure. Key pair authentication is more secure than username\/password. Specifying a dedicated virtual warehouse ensures dedicated compute. Option A is highly insecure. Option B doesn&#8217;t directly create a Snowpark session. Option C, while using OAuth, requires proper setup and key pair provides more control. Option E is highly insecure and grants excessive privileges.<\/div><input type='button' class='showchecked' style='margin: 10px 0;' onclick='showanswer1(16,this)' id='btn-16' value='See Answer'  \/><input type='hidden' id='questionType16' value='radio' class=''><\/div><div class='watu-question' id='question-17'><div class='question-content'><p><strong>Q124.<\/strong> A data scientist is exploring customer purchase data in Snowflake to identify high-value customer segments. They have a table named &#8216;CUSTOMER TRANSACTIONS with columns &#8216;CUSTOMER ID&#8217;, &#8216;TRANSACTION_DATE&#8217;, and &#8216;PURCHASE_AMOUNT&#8217;. They want to calculate the interquartile range (IQR) of &#8216;PURCHASE AMOUNT for each customer. Which SQL query using Snowsight is the most efficient and accurate way to calculate and display the IQR for each &#8216;CUSTOMER ID?<br \/><img decoding=\"async\" src=\"https:\/\/blog.topexamcollection.com\/wp-content\/uploads\/2025\/08\/DSA-C03-f8e85aa6aa6739acc02ceb4a1965a457.jpg\"\/><\/p>\n<\/div><input type='hidden' name='question_id[]' value='16787' \/><div class='watu-questions-wrap '><input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='64900' \/><div class='watu-question-choice'><input type='radio' name='answer-16787[]' id='answer-id-64900' class='answer answer-17 js-answer-label answerof-16787' value='64900' \/>&nbsp;<label for='answer-id-64900' id='answer-label-64900' class='js-answer-label answer label-17'><span class='answer'>Option A<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='64901' \/><div class='watu-question-choice'><input type='radio' name='answer-16787[]' id='answer-id-64901' class='answer answer-17 js-answer-label answerof-16787' value='64901' \/>&nbsp;<label for='answer-id-64901' id='answer-label-64901' class='js-answer-label answer label-17'><span class='answer'>Option B<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='64902' \/><div class='watu-question-choice'><input type='radio' name='answer-16787[]' id='answer-id-64902' class='answer answer-17 js-answer-label answerof-16787' value='64902' \/>&nbsp;<label for='answer-id-64902' id='answer-label-64902' class='js-answer-label answer label-17'><span class='answer'>Option C<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='64903' \/><div class='watu-question-choice'><input type='radio' name='answer-16787[]' id='answer-id-64903' class='answer answer-17 js-answer-label answerof-16787' value='64903' \/>&nbsp;<label for='answer-id-64903' id='answer-label-64903' class='js-answer-label answer label-17'><span class='answer'>Option D<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='64904' \/><div class='watu-question-choice'><input type='radio' name='answer-16787[]' id='answer-id-64904' class='answer answer-17 php-answer-label answerof-16787' value='64904' \/>&nbsp;<label for='answer-id-64904' id='answer-label-64904' class='php-answer-label answer label-17'><span class='answer'>Option E<\/span><\/label><\/div>\n<\/div><div class='show-question-feedback' style='display:none;'>Option E, using &#8216;QUANTILE, is the most accurate way to calculate the IQR. 4)&#8217; returns an array representing the quartiles (0%, 25%, 50%, 75%, 100%). Subtracting the 25th percentile (index 1) from the 75th percentile (index 3) gives the IQR. Other options either approximate the percentiles (APPROX_PERCENTILE), calculate the range (MAX-MIN), or calculate standard deviation, none of which directly give the IQR. Option B while syntactically valid is less performant and returns the IQR on entire table not grouped by customer.<\/div><input type='button' class='showchecked' style='margin: 10px 0;' onclick='showanswer1(17,this)' id='btn-17' value='See Answer'  \/><input type='hidden' id='questionType17' value='radio' class=''><\/div><div class='watu-question' id='question-18'><div class='question-content'><p><strong>Q125.<\/strong> You are tasked with forecasting the daily sales of a specific product for the next 30 days using Snowflake. You have historical sales data for the past 3 years, stored in a Snowflake table named &#8216;SALES DATA&#8217;, with columns &#8216;SALE DATE (DATE type) and &#8216;SALES AMOUNT&#8217; (NUMBER type). You want to use the Prophet library within a Snowflake User-Defined Function (UDF) for forecasting. The Prophet model requires the input data to have columns named &#8216;ds&#8217; (for dates) and &#8216;y&#8217; (for values). Which of the following code snippets demonstrates the CORRECT way to prepare and pass your data to the Prophet UDF in Snowflake, assuming you&#8217;ve already created the Python UDF &#8216;prophet_forecast&#8217;?<\/p>\n<\/div><input type='hidden' name='question_id[]' value='16788' \/><div class='watu-questions-wrap '><input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='64905' \/><div class='watu-question-choice'><input type='radio' name='answer-16788[]' id='answer-id-64905' class='answer answer-18 js-answer-label answerof-16788' value='64905' \/>&nbsp;<label for='answer-id-64905' id='answer-label-64905' class='js-answer-label answer label-18'><span class='answer'><img decoding=\"async\" src=\"https:\/\/blog.topexamcollection.com\/wp-content\/uploads\/2025\/08\/DSA-C03-f1b107ee9d3c86d1cec156c8e5ef9076.jpg\"\/><\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='64906' \/><div class='watu-question-choice'><input type='radio' name='answer-16788[]' id='answer-id-64906' class='answer answer-18 js-answer-label answerof-16788' value='64906' \/>&nbsp;<label for='answer-id-64906' id='answer-label-64906' class='js-answer-label answer label-18'><span class='answer'><img decoding=\"async\" src=\"https:\/\/blog.topexamcollection.com\/wp-content\/uploads\/2025\/08\/DSA-C03-7371da8f2170a26a5409840c152922d2.jpg\"\/><\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='64907' \/><div class='watu-question-choice'><input type='radio' name='answer-16788[]' id='answer-id-64907' class='answer answer-18 php-answer-label answerof-16788' value='64907' \/>&nbsp;<label for='answer-id-64907' id='answer-label-64907' class='php-answer-label answer label-18'><span class='answer'><img decoding=\"async\" src=\"https:\/\/blog.topexamcollection.com\/wp-content\/uploads\/2025\/08\/DSA-C03-bd6f95bf92727a38ff0edfd68bfff211.jpg\"\/><\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='64908' \/><div class='watu-question-choice'><input type='radio' name='answer-16788[]' id='answer-id-64908' class='answer answer-18 js-answer-label answerof-16788' value='64908' \/>&nbsp;<label for='answer-id-64908' id='answer-label-64908' class='js-answer-label answer label-18'><span class='answer'><img decoding=\"async\" src=\"https:\/\/blog.topexamcollection.com\/wp-content\/uploads\/2025\/08\/DSA-C03-7ea5a94e11fd8ca7ab1c72e0848af372.jpg\"\/><\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='64909' \/><div class='watu-question-choice'><input type='radio' name='answer-16788[]' id='answer-id-64909' class='answer answer-18 js-answer-label answerof-16788' value='64909' \/>&nbsp;<label for='answer-id-64909' id='answer-label-64909' class='js-answer-label answer label-18'><span class='answer'><img decoding=\"async\" src=\"https:\/\/blog.topexamcollection.com\/wp-content\/uploads\/2025\/08\/DSA-C03-f5c67ec39cce4892730dcb2c0be25d29.jpg\"\/><\/span><\/label><\/div>\n<\/div><div class='show-question-feedback' style='display:none;'>The correct approach is to construct JSON objects with &#8216;ds&#8217; and &#8216;y&#8217; as keys and the corresponding &#8216;SALE_DATE and &#8216;SALES_AMOUNT as values. Then, these JSON objects are aggregated into an array using ARRAY AGG(). This array is then passed to the prophet_forecast&#8217; UDF. Options A, B, and D are incorrect because they either pass individual dates and sales amounts as separate arrays or pass the JSON object one by one which is not the desired approach for Prophet UDE.<\/div><input type='button' class='showchecked' style='margin: 10px 0;' onclick='showanswer1(18,this)' id='btn-18' value='See Answer'  \/><input type='hidden' id='questionType18' value='radio' class=''><\/div><div class='watu-question' id='question-19'><div class='question-content'><p><strong>Q126.<\/strong> You are working with a large sales transaction dataset in Snowflake, stored in a table named &#8216;SALES DATA&#8217;. This table contains columns such as &#8216;TRANSACTION_ID (unique identifier), &#8216;CUSTOMER_ID&#8217;, &#8216;PRODUCT_ID, &#8216;TRANSACTION_DATE&#8217; , and &#8216;AMOUNT&#8217;. Due to a system error, some transactions were duplicated in the table. Your goal is to remove these duplicates efficiently using Snowpark for Python. You want to use the &#8216;window.partitionBy()&#8217; and functions. Which of the following code snippets correctly removes duplicates based on all columns, while also creating a new column &#8216;ROW NUM&#8217; to indicate the row number within each partition?<\/p>\n<\/div><input type='hidden' name='question_id[]' value='16789' \/><div class='watu-questions-wrap '><input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='64910' \/><div class='watu-question-choice'><input type='radio' name='answer-16789[]' id='answer-id-64910' class='answer answer-19 php-answer-label answerof-16789' value='64910' \/>&nbsp;<label for='answer-id-64910' id='answer-label-64910' class='php-answer-label answer label-19'><span class='answer'><img decoding=\"async\" src=\"https:\/\/blog.topexamcollection.com\/wp-content\/uploads\/2025\/08\/DSA-C03-470b702af54f692c638200f25f4c5f05.jpg\"\/><\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='64911' \/><div class='watu-question-choice'><input type='radio' name='answer-16789[]' id='answer-id-64911' class='answer answer-19 js-answer-label answerof-16789' value='64911' \/>&nbsp;<label for='answer-id-64911' id='answer-label-64911' class='js-answer-label answer label-19'><span class='answer'><img decoding=\"async\" src=\"https:\/\/blog.topexamcollection.com\/wp-content\/uploads\/2025\/08\/DSA-C03-0d975055657a2658668ab56e9220557a.jpg\"\/><\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='64912' \/><div class='watu-question-choice'><input type='radio' name='answer-16789[]' id='answer-id-64912' class='answer answer-19 js-answer-label answerof-16789' value='64912' \/>&nbsp;<label for='answer-id-64912' id='answer-label-64912' class='js-answer-label answer label-19'><span class='answer'><img decoding=\"async\" src=\"https:\/\/blog.topexamcollection.com\/wp-content\/uploads\/2025\/08\/DSA-C03-ffeca667256d10209df7be5280c038c7.jpg\"\/><\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='64913' \/><div class='watu-question-choice'><input type='radio' name='answer-16789[]' id='answer-id-64913' class='answer answer-19 js-answer-label answerof-16789' value='64913' \/>&nbsp;<label for='answer-id-64913' id='answer-label-64913' class='js-answer-label answer label-19'><span class='answer'><img decoding=\"async\" src=\"https:\/\/blog.topexamcollection.com\/wp-content\/uploads\/2025\/08\/DSA-C03-c6268cba0fe7b43e3a8595499398798a.jpg\"\/><\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='64914' \/><div class='watu-question-choice'><input type='radio' name='answer-16789[]' id='answer-id-64914' class='answer answer-19 js-answer-label answerof-16789' value='64914' \/>&nbsp;<label for='answer-id-64914' id='answer-label-64914' class='js-answer-label answer label-19'><span class='answer'><img decoding=\"async\" src=\"https:\/\/blog.topexamcollection.com\/wp-content\/uploads\/2025\/08\/DSA-C03-d984c35ab0af1466482284c1b89079eb.jpg\"\/><\/span><\/label><\/div>\n<\/div><div class='show-question-feedback' style='display:none;'>Option A is the correct answer because it correctly partitions the data by all columns using &#8216;sales_df.columns&#8217; within the function. It then assigns a row number within each partition using Finally, it filters the data to keep only the first row (ROW_NUM = 1) within each partition, effectively removing duplicates. The removes the temporary column and saves the unique data to a new table. Option B is incorrect because it uses &#8216;orderBy&#8217; instead of &#8216;partitionBy&#8217; , which does not group identical rows together for duplicate removal. Option C is incorrect because it uses &#8216; F.rank()&#8217; instead of &#8216;rank()&#8217; assigns the same rank to identical rows within a partition, potentially keeping more than one duplicate. Option D is incorrect because unpacking the dataframe column in partitionby using sales_df.columns causes TypeError: Column is not iterable. Option E is incorrect because passing the entire sales_df to partitionBy is not valid.<\/div><input type='button' class='showchecked' style='margin: 10px 0;' onclick='showanswer1(19,this)' id='btn-19' value='See Answer'  \/><input type='hidden' id='questionType19' value='radio' class=''><\/div><div class='watu-question' id='question-20'><div class='question-content'><p><strong>Q127.<\/strong> You are developing a model to predict equipment failure in a factory using sensor data stored in Snowflake. The data is partitioned by &#8216;EQUIPMENT ID&#8217; and &#8216;TIMESTAMP. After initial model training and cross-validation using the following code snippet:<br \/><img decoding=\"async\" src=\"https:\/\/blog.topexamcollection.com\/wp-content\/uploads\/2025\/08\/DSA-C03-5fd4119b55f28f015ca2989daa13df45.jpg\"\/><br \/>You observe significant performance variations across different equipment groups when evaluating on out-of-sample data&#8217;. Which of the following strategies could you employ to address this issue within the Snowflake environment to improve the model&#8217;s generalization ability across all equipment?<\/p>\n<\/div><input type='hidden' name='question_id[]' value='16790' \/><div class='watu-questions-wrap '><input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='64915' \/><div class='watu-question-choice'><input type='checkbox' name='answer-16790[]' id='answer-id-64915' class='answer answer-20 js-answer-label answerof-16790' value='64915' \/>&nbsp;<label for='answer-id-64915' id='answer-label-64915' class='js-answer-label answer label-20'><span class='answer'>Increase the overall size of the &#8220;TRAINING_DATR to include more historical data for all equipment, assuming this will balance the representation of each EQUIPMENT ID&#8217;<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='64916' \/><div class='watu-question-choice'><input type='checkbox' name='answer-16790[]' id='answer-id-64916' class='answer answer-20 js-answer-label answerof-16790' value='64916' \/>&nbsp;<label for='answer-id-64916' id='answer-label-64916' class='js-answer-label answer label-20'><span class='answer'>Implement a hyperparameter search using &#8216;SYSTEM$OPTIMIZE_MODEL&#8217; with a wider range of parameters for each &#8216;EQUIPMENT_ID individually, creating a separate model for each &#8216;EQUIPMENT ID.<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='64917' \/><div class='watu-question-choice'><input type='checkbox' name='answer-16790[]' id='answer-id-64917' class='answer answer-20 php-answer-label answerof-16790' value='64917' \/>&nbsp;<label for='answer-id-64917' id='answer-label-64917' class='php-answer-label answer label-20'><span class='answer'>Retrain the model with additional feature engineering to create interaction terms between &#8216;EQUIPMENT_ID&#8217; and other relevant sensor features to capture equipment-specific patterns. For instance, you can one hot encode and add to model and include in &#8216;INPUT DATA&#8217;.<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='64918' \/><div class='watu-question-choice'><input type='checkbox' name='answer-16790[]' id='answer-id-64918' class='answer answer-20 js-answer-label answerof-16790' value='64918' \/>&nbsp;<label for='answer-id-64918' id='answer-label-64918' class='js-answer-label answer label-20'><span class='answer'>Implement cross-validation at the partition level by splitting &#8216;TRAINING_DATX into train and test sets before creating the model, and then using the &#8216;FIT&#8217; command to train on the train set and &#8216;PREDICT to evaluate on the test set, repeating for each partition.<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='64919' \/><div class='watu-question-choice'><input type='checkbox' name='answer-16790[]' id='answer-id-64919' class='answer answer-20 php-answer-label answerof-16790' value='64919' \/>&nbsp;<label for='answer-id-64919' id='answer-label-64919' class='php-answer-label answer label-20'><span class='answer'>Create seperate models per equipment ID. For each equipment ID, split data into training and testing data. For each equipment ID, use &#8216;SYSTEM$OPTIMIZE MODEL&#8217; to perform hyper parameter search individually. Train and Deploy the model at equipement ID Level.<\/span><\/label><\/div>\n<\/div><div class='show-question-feedback' style='display:none;'>Options C and E are the most effective strategies. Option C (Feature Engineering): By creating interaction terms between EQUIPMENT _ ICY and other sensor features, the model can learn equipment-specific patterns. This enables the model to account for the unique characteristics of each equipment group, improving its ability to generalize across all equipment. For example, the optimal temperature threshold for triggering a failure might differ significantly between EQUIPMENT_ID&#8217; groups, and this can be captured using interaction terms. Option E (Seperate models per Equipment ID) : Hyperparameter tuning and training separate models per equipment ID enables you to optimize and customize the model specific to each equipment ID. The downsize is that we need to create and manage more models. Options A and D are less effective or may have limitations: Option A (Increase Training Data Size): While increasing the training data size can sometimes improve model performance, it doesn&#8217;t guarantee that the model will learn to differentiate between the equipment groups effectively, especially if some groups have significantly different data characteristics. This can also consume a lot of resources unnecessarily. Option D (Custom cross Validation) : While it&#8217;s valid, it is difficult to implement and the built in Snowflake cross validation features is much more performant and easier to use.<\/div><input type='button' class='showchecked' style='margin: 10px 0;' onclick='showanswer1(20,this)' id='btn-20' value='See Answer'  \/><input type='hidden' id='questionType20' value='checkbox' class=''><\/div><div class='watu-question' id='question-21'><div class='question-content'><p><strong>Q128.<\/strong> You&#8217;re working with a large dataset of user transactions in Snowflake. You need to identify potential outliers in transaction amounts C TRANSACTION AMOUNT) for each user CUSER ID&#8217;). Your goal is to flag transactions that are more than 3 standard deviations away from the mean transaction amount for that specific user. Which of the following approaches, utilizing Snowflake&#8217;s statistical functions and window functions, would be MOST efficient and accurate for achieving this?<\/p>\n<\/div><input type='hidden' name='question_id[]' value='16791' \/><div class='watu-questions-wrap '><input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='64920' \/><div class='watu-question-choice'><input type='radio' name='answer-16791[]' id='answer-id-64920' class='answer answer-21 js-answer-label answerof-16791' value='64920' \/>&nbsp;<label for='answer-id-64920' id='answer-label-64920' class='js-answer-label answer label-21'><span class='answer'>Using a correlated subquery to calculate the mean and standard deviation for each user and then filtering the transactions.<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='64921' \/><div class='watu-question-choice'><input type='radio' name='answer-16791[]' id='answer-id-64921' class='answer answer-21 js-answer-label answerof-16791' value='64921' \/>&nbsp;<label for='answer-id-64921' id='answer-label-64921' class='js-answer-label answer label-21'><span class='answer'>Calculating the overall mean and standard deviation for all transactions and filtering transactions based on those global statistics.<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='64922' \/><div class='watu-question-choice'><input type='radio' name='answer-16791[]' id='answer-id-64922' class='answer answer-21 php-answer-label answerof-16791' value='64922' \/>&nbsp;<label for='answer-id-64922' id='answer-label-64922' class='php-answer-label answer label-21'><span class='answer'>Using window functions to calculate the mean and standard deviation for each user within the same query, and then comparing each transaction amount to the calculated range.<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='64923' \/><div class='watu-question-choice'><input type='radio' name='answer-16791[]' id='answer-id-64923' class='answer answer-21 js-answer-label answerof-16791' value='64923' \/>&nbsp;<label for='answer-id-64923' id='answer-label-64923' class='js-answer-label answer label-21'><span class='answer'>Exporting the data to a Python environment, performing the calculations using Pandas, and then re-importing the results to Snowflake.<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='64924' \/><div class='watu-question-choice'><input type='radio' name='answer-16791[]' id='answer-id-64924' class='answer answer-21 js-answer-label answerof-16791' value='64924' \/>&nbsp;<label for='answer-id-64924' id='answer-label-64924' class='js-answer-label answer label-21'><span class='answer'>Creating a stored procedure that iterates through each user and calculates the mean and standard deviation individually.<\/span><\/label><\/div>\n<\/div><div class='show-question-feedback' style='display:none;'>Using window functions (option C) is the most efficient and accurate approach. It allows you to calculate the mean and standard deviation for each user within the same query, avoiding the overhead of correlated subqueries (option A) or the inaccuracy of global statistics (option B). Options D and E are less efficient due to data transfer and procedural logic overhead. Correlated subquery will lead to performance issue and is not advisable for bigger datasets.<\/div><input type='button' class='showchecked' style='margin: 10px 0;' onclick='showanswer1(21,this)' id='btn-21' value='See Answer'  \/><input type='hidden' id='questionType21' value='radio' class=''><\/div><div class='watu-question' id='question-22'><div class='question-content'><p><strong>Q129.<\/strong> You are deploying a fraud detection model using Snowpark Container Services. The model requires a substantial amount of GPU memory. After deploying your service, you notice that it frequently crashes due to Out-Of-Memory (OOM) errors. You have verified that the container image itself is not the source of the problem. Which of the following strategies are most appropriate to mitigate these OOM errors when using Snowpark Container Services, assuming you want to minimize costs and complexity?<\/p>\n<\/div><input type='hidden' name='question_id[]' value='16792' \/><div class='watu-questions-wrap '><input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='64925' \/><div class='watu-question-choice'><input type='checkbox' name='answer-16792[]' id='answer-id-64925' class='answer answer-22 php-answer-label answerof-16792' value='64925' \/>&nbsp;<label for='answer-id-64925' id='answer-label-64925' class='php-answer-label answer label-22'><span class='answer'>Increase the &#8216;container.resources.memory&#8217; configuration setting in the service definition to a value significantly larger than the model&#8217;s memory footprint. Monitor memory utilization and adjust as needed.<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='64926' \/><div class='watu-question-choice'><input type='checkbox' name='answer-16792[]' id='answer-id-64926' class='answer answer-22 js-answer-label answerof-16792' value='64926' \/>&nbsp;<label for='answer-id-64926' id='answer-label-64926' class='js-answer-label answer label-22'><span class='answer'>Implement model parallelism across multiple containers, splitting the model&#8217;s workload and data across them. Configure each container with a smaller &#8216;container.resources.memory&#8217; allocation.<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='64927' \/><div class='watu-question-choice'><input type='checkbox' name='answer-16792[]' id='answer-id-64927' class='answer answer-22 js-answer-label answerof-16792' value='64927' \/>&nbsp;<label for='answer-id-64927' id='answer-label-64927' class='js-answer-label answer label-22'><span class='answer'>Utilize CPU-based inference instead of GPU-based inference, as CPU inference is generally less memory-intensive. Convert the model to a format optimized for CPU inference (e.g., using ONNX). Reduce the &#8216;container.resources.cpu&#8217; count.<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='64928' \/><div class='watu-question-choice'><input type='checkbox' name='answer-16792[]' id='answer-id-64928' class='answer answer-22 php-answer-label answerof-16792' value='64928' \/>&nbsp;<label for='answer-id-64928' id='answer-label-64928' class='php-answer-label answer label-22'><span class='answer'>Implement a mechanism within your model&#8217;s inference code to explicitly free up unused memory after each prediction. Use Python&#8217;s &#8216;gc.collect()&#8217; and ensure proper cleanup of large data structures. Configure a smaller &#8216;container.resources.memory&#8217; allocation.<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='64929' \/><div class='watu-question-choice'><input type='checkbox' name='answer-16792[]' id='answer-id-64929' class='answer answer-22 js-answer-label answerof-16792' value='64929' \/>&nbsp;<label for='answer-id-64929' id='answer-label-64929' class='js-answer-label answer label-22'><span class='answer'>Ignore OOM errors and rely on the container service to automatically restart the container. The model will eventually process all requests.<\/span><\/label><\/div>\n<\/div><div class='show-question-feedback' style='display:none;'>Options A and D are the best strategies. Option A directly addresses the OOM issue by increasing the memory allocation. Monitoring memory usage is crucial to optimize resource utilization. Option D focuses on efficient memory management within the model itself. Explicitly freeing memory and garbage collection can reduce memory footprint. If model need very less gpu memory then decrease container.resources.memory&#8217; configuration Option B is a valid strategy, but it introduces significantly more complexity with model parallelism and inter-container communication. Option C might be an option if GPU inference is not strictly necessary and acceptable performance can be achieved with CPU inference, but it is a significant change to the model architecture and potentially impacts performance. Option E is incorrect because ignoring OOM errors leads to unreliable service behavior and data loss.<\/div><input type='button' class='showchecked' style='margin: 10px 0;' onclick='showanswer1(22,this)' id='btn-22' value='See Answer'  \/><input type='hidden' id='questionType22' value='checkbox' class=''><\/div><div class='watu-question' id='question-23'><div class='question-content'><p><strong>Q130.<\/strong> A financial institution is analyzing transaction data in Snowflake to detect fraudulent activity. They have a &#8216;Transaction_Amount&#8217; column. They want to binarize this feature, creating a new &#8216;ls_High_Value&#8217; column. Transactions with amounts greater than $1000 should be marked as 1 (High Value), and all other transactions (including NULLs) should be marked as 0. Which of the following SQL statements would be the MOST efficient and correct way to achieve this in Snowflake?<br \/><img decoding=\"async\" src=\"https:\/\/blog.topexamcollection.com\/wp-content\/uploads\/2025\/08\/DSA-C03-2ed4fa1be8a5f0e2f6b3435201d54bb9.jpg\"\/><\/p>\n<\/div><input type='hidden' name='question_id[]' value='16793' \/><div class='watu-questions-wrap '><input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='64930' \/><div class='watu-question-choice'><input type='radio' name='answer-16793[]' id='answer-id-64930' class='answer answer-23 js-answer-label answerof-16793' value='64930' \/>&nbsp;<label for='answer-id-64930' id='answer-label-64930' class='js-answer-label answer label-23'><span class='answer'>Option A<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='64931' \/><div class='watu-question-choice'><input type='radio' name='answer-16793[]' id='answer-id-64931' class='answer answer-23 js-answer-label answerof-16793' value='64931' \/>&nbsp;<label for='answer-id-64931' id='answer-label-64931' class='js-answer-label answer label-23'><span class='answer'>Option B<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='64932' \/><div class='watu-question-choice'><input type='radio' name='answer-16793[]' id='answer-id-64932' class='answer answer-23 js-answer-label answerof-16793' value='64932' \/>&nbsp;<label for='answer-id-64932' id='answer-label-64932' class='js-answer-label answer label-23'><span class='answer'>Option C<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='64933' \/><div class='watu-question-choice'><input type='radio' name='answer-16793[]' id='answer-id-64933' class='answer answer-23 php-answer-label answerof-16793' value='64933' \/>&nbsp;<label for='answer-id-64933' id='answer-label-64933' class='php-answer-label answer label-23'><span class='answer'>Option D<\/span><\/label><\/div>\n<input type='hidden' name='answer_ids[]' class='watu-answer-ids' value='64934' \/><div class='watu-question-choice'><input type='radio' name='answer-16793[]' id='answer-id-64934' class='answer answer-23 js-answer-label answerof-16793' value='64934' \/>&nbsp;<label for='answer-id-64934' id='answer-label-64934' class='js-answer-label answer label-23'><span class='answer'>Option E<\/span><\/label><\/div>\n<\/div><div class='show-question-feedback' style='display:none;'>The &#8216; IIF function in Snowflake provides a concise and efficient way to perform conditional logic. It&#8217;s specifically designed for this type of binary assignment. Options A would not handle NULL values correctly, potentially resulting in NULL &#8216;ls_High_Value&#8217; entries. Options B and C are correct, but using a Numeric column (Option D) might be preferred in some ML models. Options E is more complex and less readable for a simple binarization task. 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