Professional-Machine-Learning-Engineer Dumps Special Discount for limited time Try FOR FREE [Q44-Q65]

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Professional-Machine-Learning-Engineer Dumps Special Discount for limited time Try FOR FREE

Professional-Machine-Learning-Engineer Dumps for success in Actual Exam May-2023]

Career Bonuses

The Google Professional Machine Learning Engineer certification proves that the successful candidates possess sufficient knowledge and skills to design and create scalable solutions for optimal performance. Some of the job roles that these individuals can consider include a Data Engineer, a Senior Data Engineer, a Machine Learning Engineer, a Technical Solutions Engineer, a Software Engineer, and a Cloud Infrastructure Engineer, among others. The median salary that the certificate holders can count on is around $140,000 per annum.

Exam Details

The Google Professional Machine Learning Engineer exam is two hours long. The candidates can expect multiple-choice as well as multiple-select questions in their delivery of the certification test. The exam is currently given to the learners in the English language. To register for and schedule it, you need to pay $200 (plus applicable taxes). While registering for the test, the potential applicants will be offered to select the convenient mode of exam delivery: an online proctored session from a remote location or an in-person proctored session at the nearest testing center.

The Google Professional Machine Learning Engineer Certification Exam consists of multiple-choice questions and performance-based scenarios that test your ability to design and implement machine learning models on the Google Cloud Platform. The exam covers a wide range of topics, including data preparation, model training and evaluation, and deployment of machine learning models in a production environment.

QUESTION 44
You are training a TensorFlow model on a structured data set with 100 billion records stored in several CSV files. You need to improve the input/output execution performance. What should you do?

Load the data into BigQuery and read the data from BigQuery.

Load the data into Cloud Bigtable, and read the data from Bigtable

Convert the CSV files into shards of TFRecords, and store the data in Cloud Storage

Convert the CSV files into shards of TFRecords, and store the data in the Hadoop Distributed File System (HDFS)

QUESTION 45
You built and manage a production system that is responsible for predicting sales numbers. Model accuracy is crucial, because the production model is required to keep up with market changes. Since being deployed to production, the model hasn’t changed; however the accuracy of the model has steadily deteriorated. What issue is most likely causing the steady decline in model accuracy?

Poor data quality

Lack of model retraining

Too few layers in the model for capturing information

Incorrect data split ratio during model training, evaluation, validation, and test

QUESTION 46
You work for a magazine publisher and have been tasked with predicting whether customers will cancel their annual subscription. In your exploratory data analysis, you find that 90% of individuals renew their subscription every year, and only 10% of individuals cancel their subscription. After training a NN Classifier, your model predicts those who cancel their subscription with 99% accuracy and predicts those who renew their subscription with 82% accuracy. How should you interpret these results?

This is not a good result because the model should have a higher accuracy for those who renew their subscription than for those who cancel their subscription.

This is not a good result because the model is performing worse than predicting that people will always renew their subscription.

This is a good result because predicting those who cancel their subscription is more difficult, since there is less data for this group.

This is a good result because the accuracy across both groups is greater than 80%.

QUESTION 47
A Machine Learning Specialist deployed a model that provides product recommendations on a company’s website. Initially, the model was performing very well and resulted in customers buying more products on average. However, within the past few months, the Specialist has noticed that the effect of product recommendations has diminished and customers are starting to return to their original habits of spending less.
The Specialist is unsure of what happened, as the model has not changed from its initial deployment over a year ago.
Which method should the Specialist try to improve model performance?

The model needs to be completely re-engineered because it is unable to handle product inventory changes.

The model’s hyperparameters should be periodically updated to prevent drift.

The model should be periodically retrained from scratch using the original data while adding a regularization term to handle product inventory changes

The model should be periodically retrained using the original training data plus new data as product inventory changes.

QUESTION 48
You work for an online publisher that delivers news articles to over 50 million readers. You have built an AI model that recommends content for the company’s weekly newsletter. A recommendation is considered successful if the article is opened within two days of the newsletter’s published date and the user remains on the page for at least one minute.
All the information needed to compute the success metric is available in BigQuery and is updated hourly. The model is trained on eight weeks of data, on average its performance degrades below the acceptable baseline after five weeks, and training time is 12 hours. You want to ensure that the model’s performance is above the acceptable baseline while minimizing cost. How should you monitor the model to determine when retraining is necessary?

Use Vertex AI Model Monitoring to detect skew of the input features with a sample rate of 100% and a monitoring frequency of two days.

Schedule a cron job in Cloud Tasks to retrain the model every week before the newsletter is created.

Schedule a weekly query in BigQuery to compute the success metric.

Schedule a daily Dataflow job in Cloud Composer to compute the success metric.

QUESTION 49
You are an ML engineer at a large grocery retailer with stores in multiple regions. You have been asked to create an inventory prediction model. Your models features include region, location, historical demand, and seasonal popularity. You want the algorithm to learn from new inventory data on a daily basis. Which algorithms should you use to build the model?

Classification

Reinforcement Learning

Recurrent Neural Networks (RNN)

Convolutional Neural Networks (CNN)

QUESTION 50
A retail company is using Amazon Personalize to provide personalized product recommendations for its customers during a marketing campaign. The company sees a significant increase in sales of recommended items to existing customers immediately after deploying a new solution version, but these sales decrease a short time after deployment. Only historical data from before the marketing campaign is available for training.
How should a data scientist adjust the solution?

Use the event tracker in Amazon Personalize to include real-time user interactions.

Add user metadata and use the HRNN-Metadata recipe in Amazon Personalize.

Implement a new solution using the built-in factorization machines (FM) algorithm in Amazon SageMaker.

Add event type and event value fields to the interactions dataset in Amazon Personalize.

QUESTION 51
You work for a public transportation company and need to build a model to estimate delay times for multiple transportation routes. Predictions are served directly to users in an app in real time. Because different seasons and population increases impact the data relevance, you will retrain the model every month. You want to follow Google-recommended best practices. How should you configure the end-to-end architecture of the predictive model?

Configure Kubeflow Pipelines to schedule your multi-step workflow from training to deploying your model.

Use a model trained and deployed on BigQuery ML and trigger retraining with the scheduled query feature in BigQuery

Write a Cloud Functions script that launches a training and deploying job on Ai Platform that is triggered by Cloud Scheduler

Use Cloud Composer to programmatically schedule a Dataflow job that executes the workflow from training to deploying your model

QUESTION 52
Your team is building an application for a global bank that will be used by millions of customers. You built a forecasting model that predicts customers1 account balances 3 days in the future. Your team will use the results in a new feature that will notify users when their account balance is likely to drop below $25. How should you serve your predictions?

1. Create a Pub/Sub topic for each user
2 Deploy a Cloud Function that sends a notification when your model predicts that a user’s account balance will drop below the $25 threshold.

1. Create a Pub/Sub topic for each user
2. Deploy an application on the App Engine standard environment that sends a notification when your model predicts that a user’s account balance will drop below the $25 threshold

1. Build a notification system on Firebase
2. Register each user with a user ID on the Firebase Cloud Messaging server, which sends a notification when the average of all account balance predictions drops below the $25 threshold

1 Build a notification system on Firebase
2. Register each user with a user ID on the Firebase Cloud Messaging server, which sends a notification when your model predicts that a user’s account balance will drop below the $25 threshold

QUESTION 53
You work as an ML engineer at a social media company, and you are developing a visual filter for users’ profile photos. This requires you to train an ML model to detect bounding boxes around human faces. You want to use this filter in your company’s iOS-based mobile phone application. You want to minimize code development and want the model to be optimized for inference on mobile phones. What should you do?

Train a model using AutoML Vision and use the “export for Core ML” option.

Train a model using AutoML Vision and use the “export for Coral” option.

Train a model using AutoML Vision and use the “export for TensorFlow.js” option.

Train a custom TensorFlow model and convert it to TensorFlow Lite (TFLite).

QUESTION 54
You are experimenting with a built-in distributed XGBoost model in Vertex AI Workbench user-managed notebooks. You use BigQuery to split your data into training and validation sets using the following queries:
CREATE OR REPLACE TABLE ‘myproject.mydataset.training’ AS
(SELECT * FROM ‘myproject.mydataset.mytable’ WHERE RAND() <= 0.8);
CREATE OR REPLACE TABLE ‘myproject.mydataset.validation’ AS
(SELECT * FROM ‘myproject.mydataset.mytable’ WHERE RAND() <= 0.2);
After training the model, you achieve an area under the receiver operating characteristic curve (AUC ROC) value of 0.8, but after deploying the model to production, you notice that your model performance has dropped to an AUC ROC value of 0.65. What problem is most likely occurring?

There is training-serving skew in your production environment.

There is not a sufficient amount of training data.

The tables that you created to hold your training and validation records share some records, and you may not be using all the data in your initial table.

The RAND() function generated a number that is less than 0.2 in both instances, so every record in the validation table will also be in the training table.

QUESTION 55
A Machine Learning Specialist is training a model to identify the make and model of vehicles in images. The Specialist wants to use transfer learning and an existing model trained on images of general objects. The Specialist collated a large custom dataset of pictures containing different vehicle makes and models.
What should the Specialist do to initialize the model to re-train it with the custom data?

Initialize the model with random weights in all layers including the last fully connected layer.

Initialize the model with pre-trained weights in all layers and replace the last fully connected layer.

Initialize the model with random weights in all layers and replace the last fully connected layer.

Initialize the model with pre-trained weights in all layers including the last fully connected layer.

QUESTION 56
A Machine Learning Specialist is developing a daily ETL workflow containing multiple ETL jobs. The workflow consists of the following processes:
* Start the workflow as soon as data is uploaded to Amazon S3.
* When all the datasets are available in Amazon S3, start an ETL job to join the uploaded datasets with multiple terabyte-sized datasets already stored in Amazon S3.
* Store the results of joining datasets in Amazon S3.
* If one of the jobs fails, send a notification to the Administrator.
Which configuration will meet these requirements?

Use AWS Lambda to trigger an AWS Step Functions workflow to wait for dataset uploads to complete in Amazon S3. Use AWS Glue to join the datasets. Use an Amazon CloudWatch alarm to send an SNS notification to the Administrator in the case of a failure.

Develop the ETL workflow using AWS Lambda to start an Amazon SageMaker notebook instance. Use a lifecycle configuration script to join the datasets and persist the results in Amazon S3. Use an Amazon CloudWatch alarm to send an SNS notification to the Administrator in the case of a failure.

Develop the ETL workflow using AWS Batch to trigger the start of ETL jobs when data is uploaded to Amazon S3. Use AWS Glue to join the datasets in Amazon S3. Use an Amazon CloudWatch alarm to send an SNS notification to the Administrator in the case of a failure.

Use AWS Lambda to chain other Lambda functions to read and join the datasets in Amazon S3 as soon as the data is uploaded to Amazon S3. Use an Amazon CloudWatch alarm to send an SNS notification to the Administrator in the case of a failure.

QUESTION 57
Your organization wants to make its internal shuttle service route more efficient. The shuttles currently stop at all pick-up points across the city every 30 minutes between 7 am and 10 am. The development team has already built an application on Google Kubernetes Engine that requires users to confirm their presence and shuttle station one day in advance. What approach should you take?

1. Build a tree-based regression model that predicts how many passengers will be picked up at each shuttle station.
2. Dispatch an appropriately sized shuttle and provide the map with the required stops based on the prediction.

1. Build a tree-based classification model that predicts whether the shuttle should pick up passengers at each shuttle station.
2. Dispatch an available shuttle and provide the map with the required stops based on the prediction

1. Define the optimal route as the shortest route that passes by all shuttle stations with confirmed attendance at the given time under capacity constraints.
2 Dispatch an appropriately sized shuttle and indicate the required stops on the map

1. Build a reinforcement learning model with tree-based classification models that predict the presence of passengers at shuttle stops as agents and a reward function around a distance-based metric
2. Dispatch an appropriately sized shuttle and provide the map with the required stops based on the simulated outcome.

QUESTION 58
You work for a bank and are building a random forest model for fraud detection. You have a dataset that includes transactions, of which 1% are identified as fraudulent. Which data transformation strategy would likely improve the performance of your classifier?

Write your data in TFRecords.

Z-normalize all the numeric features.

Oversample the fraudulent transaction 10 times.

Use one-hot encoding on all categorical features.

QUESTION 59
You are training an object detection machine learning model on a dataset that consists of three million X-ray images, each roughly 2 GB in size. You are using Vertex AI Training to run a custom training application on a Compute Engine instance with 32-cores, 128 GB of RAM, and 1 NVIDIA P100 GPU. You notice that model training is taking a very long time. You want to decrease training time without sacrificing model performance. What should you do?

Increase the instance memory to 512 GB and increase the batch size.

Replace the NVIDIA P100 GPU with a v3-32 TPU in the training job.

Enable early stopping in your Vertex AI Training job.

Use the tf.distribute.Strategy API and run a distributed training job.

QUESTION 60
You are an ML engineer at a bank that has a mobile application. Management has asked you to build an ML-based biometric authentication for the app that verifies a customer’s identity based on their fingerprint. Fingerprints are considered highly sensitive personal information and cannot be downloaded and stored into the bank databases. Which learning strategy should you recommend to train and deploy this ML model?

Differential privacy

Federated learning

MD5 to encrypt data

Data Loss Prevention API

QUESTION 61
A financial services company is building a robust serverless data lake on Amazon S3. The data lake should be flexible and meet the following requirements:
* Support querying old and new data on Amazon S3 through Amazon Athena and Amazon Redshift Spectrum.
* Support event-driven ETL pipelines
* Provide a quick and easy way to understand metadata
Which approach meets these requirements?

Use an AWS Glue crawler to crawl S3 data, an AWS Lambda function to trigger an AWS Glue ETL job, and an AWS Glue Data catalog to search and discover metadata.

Use an AWS Glue crawler to crawl S3 data, an AWS Lambda function to trigger an AWS Batch job, and an external Apache Hive metastore to search and discover metadata.

Use an AWS Glue crawler to crawl S3 data, an Amazon CloudWatch alarm to trigger an AWS Batch job, and an AWS Glue Data Catalog to search and discover metadata.

Use an AWS Glue crawler to crawl S3 data, an Amazon CloudWatch alarm to trigger an AWS Glue ETL job, and an external Apache Hive metastore to search and discover metadata.

QUESTION 62
You work for a company that is developing a new video streaming platform. You have been asked to create a recommendation system that will suggest the next video for a user to watch. After a review by an AI Ethics team, you are approved to start development. Each video asset in your company’s catalog has useful metadata (e.g., content type, release date, country), but you do not have any historical user event dat a. How should you build the recommendation system for the first version of the product?

Launch the product without machine learning. Present videos to users alphabetically, and start collecting user event data so you can develop a recommender model in the future.

Launch the product without machine learning. Use simple heuristics based on content metadata to recommend similar videos to users, and start collecting user event data so you can develop a recommender model in the future.

Launch the product with machine learning. Use a publicly available dataset such as MovieLens to train a model using the Recommendations AI, and then apply this trained model to your data.

Launch the product with machine learning. Generate embeddings for each video by training an autoencoder on the content metadata using TensorFlow. Cluster content based on the similarity of these embeddings, and then recommend videos from the same cluster.

QUESTION 63
You built a custom ML model using scikit-learn. Training time is taking longer than expected. You decide to migrate your model to Vertex AI Training, and you want to improve the model’s training time. What should you try out first?

Migrate your model to TensorFlow, and train it using Vertex AI Training.

Train your model in a distributed mode using multiple Compute Engine VMs.

Train your model with DLVM images on Vertex AI, and ensure that your code utilizes NumPy and SciPy internal methods whenever possible.

Train your model using Vertex AI Training with GPUs.

QUESTION 64
You work for a large hotel chain and have been asked to assist the marketing team in gathering predictions for a targeted marketing strategy. You need to make predictions about user lifetime value (LTV) over the next 30 days so that marketing can be adjusted accordingly. The customer dataset is in BigQuery, and you are preparing the tabular data for training with AutoML Tables. This data has a time signal that is spread across multiple columns. How should you ensure that AutoML fits the best model to your data?

Manually combine all columns that contain a time signal into an array Allow AutoML to interpret this array appropriately Choose an automatic data split across the training, validation, and testing sets

Submit the data for training without performing any manual transformations Allow AutoML to handle the appropriate transformations Choose an automatic data split across the training, validation, and testing sets

Submit the data for training without performing any manual transformations, and indicate an appropriate column as the Time column Allow AutoML to split your data based on the time signal provided, and reserve the more recent data for the validation and testing sets

Submit the data for training without performing any manual transformations Use the columns that have a time signal to manually split your data Ensure that the data in your validation set is from 30 days after the data in your training set and that the data in your testing set is from 30 days after your validation set

QUESTION 65
A Machine Learning Specialist at a company sensitive to security is preparing a dataset for model training. The dataset is stored in Amazon S3 and contains Personally Identifiable Information (PII).
The dataset:
* Must be accessible from a VPC only.
* Must not traverse the public internet.
How can these requirements be satisfied?

Create a VPC endpoint and apply a bucket access policy that restricts access to the given VPC endpoint and the VPC.

Create a VPC endpoint and apply a bucket access policy that allows access from the given VPC endpoint and an Amazon EC2 instance.

Create a VPC endpoint and use Network Access Control Lists (NACLs) to allow traffic between only the given VPC endpoint and an Amazon EC2 instance.

Create a VPC endpoint and use security groups to restrict access to the given VPC endpoint and an Amazon EC2 instance

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