[Mar 06, 2024] Passing Key To Getting Salesforce-AI-Associate Certified Exam Engine PDF [Q15-Q37]

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[Mar 06, 2024] Passing Key To Getting Salesforce-AI-Associate Certified Exam Engine PDF

Salesforce-AI-Associate Exam Dumps Pass with Updated Mar-2024 Tests Dumps

Salesforce Salesforce-AI-Associate Exam Syllabus Topics:

Topic	Details
Topic 1	AI Capabilities in CRM Ethical Considerations of AI AI Fundamentals
Topic 2	Identify CRM AI capabilities Differentiate between the types of AI and their capabilities
Topic 3	Describe the ethical challenges of AI Describe the elements components of data quality
Topic 4	Describe the importance of data quality Explain the basic principles and applications of AI within Salesforce
Topic 5	Apply Salesforce’s Trusted AI Principles to given scenarios Describe the benefits of AI as they apply to CRM

NO.15 How does a data quality assessment impact business outcome for companies using AI?

Improves the speed of AI recommendations

Accelerates the delivery of new AI solutions

Provides a benchmark for AI predictions

Explanation
“A data quality assessment impacts business outcomes for companies using AI by providing a benchmark for AI predictions. A data quality assessment is a process that measures and evaluates the quality of data for a specific purpose or task. A data quality assessment can help identify and address any issues or gaps in the data quality dimensions, such as accuracy, completeness, consistency, relevance, and timeliness. A data quality assessment can impact business outcomes for companies using AI by providing a benchmark for AI predictions, as it can help ensure that the predictions are based on high-quality data that reflects the true state or condition of the target population or domain.”

NO.16 Cloud Kicks relies on data analysis to optimize its product recommendation; however, CK encounters a recurring Issue of Incomplete customer records, with missing contact Information and incomplete purchase histories.
How will this incomplete data quality impact the company’s operations?

The accuracy of product recommendations is hindered.

The diversity of product recommendations Is Improved.

The response time for product recommendations is stalled.

Explanation
“The incomplete data quality will impact the company’s operations by hindering the accuracy of product recommendations. Incomplete data means that the data is missing some values or attributes that are relevant for the AI task. Incomplete data can affect the performance and reliability of AI models, as they may not have enough information to learn from or make accurate predictions. For example, incomplete customer records can affect the quality of product recommendations, as the AI model may not be able to capture the customers’ preferences, behavior, or needs.”

NO.17 A system admin recognizes the need to put a data management strategy in place.
What is a key component of data management strategy?

Naming Convention

Data Backup

Color Coding

Explanation
Data Backup is a key component of a data management strategy. A data backup is a process of creating and storing copies of data in a separate location or device to prevent data loss or damagein case of a disaster, accident, or malicious attack. A data backup can help ensure data availability, reliability, and security by allowing data to be restored or recovered in the event of a data breach, corruption, or deletion. A data management strategy should include a data backup plan that defines the frequency, scope, method, and location of data backups, as well as the roles and responsibilities of the data backup team.

NO.18 A consultant conducts a series of Consequence Scanning workshops to support testing diverse datasets.
Which Salesforce Trusted AI Principles is being practiced>

Transparency

Inclusivity

Accountability

Explanation
“Conducting a series of Consequence Scanning workshops to support testing diverse datasets is an action that practices Salesforce’s Trusted AI Principle of Inclusivity. Inclusivity is one of the Trusted AI Principles that states that AI systems should be designed and developed with respect for diversity and inclusion of different perspectives, backgrounds, and experiences. Conducting Consequence Scanning workshops means engaging with various stakeholders to identify and assess the potential impacts and implications of AI systems on different groups or domains. Conducting Consequence Scanning workshops can help practice Inclusivity by ensuring that diverse datasets are used to test and evaluate AI systems.”

NO.19 Cloud kicks wants to decrease the workload for its customer care agents by implementing a chatbot on its website that partially deflects incoming cases by answering frequency asked questions Which field of AI is most suitable for this scenario?

Natural language processing

Computer vision

Predictive analytics

Explanation
“Natural language processing is the field of AI that is most suitable for this scenario. Natural language processing (NLP) is a branch of AI that enables computers to understand and generate natural language, such as speech or text. NLP can be used to create conversational interfaces that can interact with users using natural language, such as chatbots. Chatbots can help automate and streamline customer service processes by providing answers, suggestions, or actions based on the user’s intent and context.”

NO.20 Cloud Kicks wants to develop a solution to predict customers product interests based on historical data. The company found that employees from one region use a text field to capture the product category, while employees from all other locations use a plckllst.
Which data quality dimension is affected in this scenario?

Completeness

Accuracy

Consistency

Explanation
“Consistency is the data quality dimension that is affected in this scenario. Consistency means that the data values are uniform and follow a common standard or format across different records, fields, or sources.
Inconsistent data can cause confusion, errors, or duplication in data analysis and processing. For example, using different field types for the same attribute can affect the consistency of the data.”

NO.21 How is natural language processing (NLP) used in the context of AI capabilities?

To cleanse and prepare data for AI implementations

To interpret and understand programming language

To understand and generate human language

Explanation
“Natural language processing (NLP) is used in the context of AI capabilities to understand and generate human language. NLP can enable AI systems to interact with humans using natural language, such as speech or text. NLP can also enable AI systems to analyze and extract information from natural language data, such as documents, emails, or social media posts.”

NO.22 A healthcare company implements an algorithm to analyze patient data and assist in medical diagnosis.
Which primary role does data Quality play In this AI application?

Enhanced accuracy and reliability of medical predictions and diagnoses

Ensured compatibility of AI algorithms with the system’s Infrastructure

Reduced need for healthcare expertise in interpreting AI outouts

Explanation
“Data quality plays a crucial role in enhancing the accuracy and reliability of medical predictions and diagnoses. Poor data quality can lead to inaccurate or misleading results, which can have serious consequences for patients’ health and well-being. Therefore, it is important to ensure that the data used for AI applications in healthcare is accurate, complete, consistent, and relevant.”

NO.23 Cloud Kicks implements a new product recommendation feature for its shoppers that recommends shoes of a given color to display to customers based on the color of the products from their purchase history.
Which type of bias is most likely to be encountered in this scenario?

Confirmation

Survivorship

Societal

Explanation
“Confirmation bias is most likely to be encountered in this scenario. Confirmation bias is a type of bias that occurs when data or information confirms or supports one’s existing beliefs or expectations. For example, confirmation bias can occur when a product recommendation feature only recommends shoes of a given color based on the customer’s purchase history, without considering other factors or preferences that may influence their choice.”

NO.24 Cloud Kicks wants to use Einstein Prediction Builder to determine a customer’s likelihood of buying specific products; however, data quality is a…
How can data quality be assessed quality?

Build a Data Management Strategy.

Build reports to expire the data quality.

Leverage data quality apps from AppExchange

Explanation
“Leveraging data quality apps from AppExchange is how data quality can be assessed. Data quality is the degree to which data is accurate, complete, consistent, relevant, and timely for the AI task. Data quality can affect the performance and reliability of AI systems, as they depend on the quality of the data they use to learn from and make predictions. Leveraging data quality apps from AppExchange means using third-party applications or solutions that can help measure, monitor, or improve data quality in Salesforce.”

NO.25 Which type of bias results from data being labeled according to stereotypes?

Association

Societal

Interaction

Explanation
“Societal bias results from data being labeled according to stereotypes. Societal bias is a type of bias that reflects the assumptions, norms, or values of a specific society or culture. For example, societal bias can occur when data is labeled based on gender, race, ethnicity, or religion stereotypes.”

NO.26 What are the key components of the data quality standard?

Reviewing, Updating, Archiving

Naming, formatting, Monitoring

Accuracy, Completeness, Consistency

Explanation
“Accuracy, Completeness, Consistency are the key components of the data quality standard. Data quality standard is a set of criteria or measures that define and evaluate the quality of data for a specific purpose or task. Data quality standard can vary by industry, domain, or application, but some common components are accuracy, completeness, and consistency. Accuracy means that the data values are correct and valid for the data attribute. Completeness means that the data values are not missing any relevant information for the data attribute. Consistency means that the data values are uniform and follow a common standard or format across different records, fields, or sources.”

NO.27 What are the three commonly used examples of AI in CRM?

Predictive scoring, reporting, Image classification

Predictive scoring, forecasting, recommendations

Einstein Bots, face recognition, recommendations

Explanation
“Predictive scoring, forecasting, and recommendations are three commonly used examples of AI in CRM.
Predictive scoring can help prioritize leads, opportunities, and customers based on their likelihood to convert, churn, or buy. Forecasting can help predict future sales, revenue, or demand based on historical data and trends. Recommendations can help suggest the best products, services, or actions for each customer based on their preferences, behavior, and needs.”

NO.28 What is an example of Salesforce’s Trusted AI Principle of Inclusivity in practice?

Testing models with diverse datasets

Striving for model explain ability

Working with human rights experts

Explanation
“An example of Salesforce’s Trusted AI Principle of Inclusivity in practice is testing models with diverse datasets. Inclusivity means that AI systems should be designed and developed with respect for diversity and inclusion of different perspectives, backgrounds, and experiences. Testing modelswith diverse datasets can help ensure that the models are fair, unbiased, and representative of the target population or domain.”

NO.29 Cloud Kicks wants to ensure that multiple records for the same customer are removed in Salesforce.
Which feature should be used to accomplish this?

Duplicate management

Trigger deletion of old records

Standardized field names

Explanation
“Duplicate management should be used to remove multiple records for the same customer in Salesforce.
Duplicate management is a feature that helps prevent and manage duplicate records in Salesforce. Duplicate management can help define matching rules, duplicate rules, and alert messages to detect and merge duplicate records.”

NO.30 What is a key characteristic of machine learning in the context of AI capabilities?

Uses algorithms to learn from data and make decisions

Relies on preprogrammed rules to make decisions

Can perfectly mimic human intelligence and decision-making

Explanation
“Machine learning is a key characteristic of AI capabilities that uses algorithms to learn from data and make decisions. Machine learning is a branch of AI that enables computers to learn from data without being explicitly programmed. Machine learning algorithms can analyze data, identify patterns, and make predictions or recommendations based on the data.”

NO.31 What is a potential source of bias in training data for AI models?

The data is collected in area time from sources systems.

The data is skewed toward is particular demographic or source.

The data is collected from a diverse range of sources and demographics.

Explanation
“A potential source of bias in training data for AI models is that the data is skewed toward a particular demographic or source. Skewed data means that the data is not balanced or representative of the target population or domain. Skewed data can introduce or exacerbate bias in AI models, as they may overfit or underfit the model to a specific subset of data. For example, skewed data can lead to bias if the data is collected from a limited or biased demographic or source, such as a certain age group, gender, race, location, or platform.”

NO.32 A sales manager wants to improve their processes using AI in Salesforce?
Which application of AI would be most beneficial?

Lead soring and opportunity forecasting

Sales dashboards and reporting

Data modeling and management

Explanation
“Lead scoring and opportunity forecasting are applications of AI that would be most beneficial for a sales manager who wants to improve their processes using AI in Salesforce. Lead scoring can help prioritize leads based on their likelihood to convert, while opportunity forecasting can help predict future sales or revenue based on historical data and trends. These applications of AI can help optimize sales processes by providing insights and recommendations that can increase sales efficiency and effectiveness.”

NO.33 Cloud Kicks learns of complaints from customers who are receiving too many sales calls and emails.
Which data quality dimension should be assessed to reduce these communication Inefficiencies?

Duplication

Usage

Consent

Explanation
“Duplication is the data quality dimension that should be assessed to reduce communication inefficiencies.
Duplication means that the data contains multiple copies or instances of the same record or value. Duplication can cause confusion, errors, or waste in data analysis and processing. For example, duplication can lead to communication inefficiencies if customers receive multiple calls or emails from different sources for the same purpose.”

NO.34 A developer is tasked with selecting a suitable dataset for training an AI model in Salesforce to accurately predict current customer behavior.
What Is a crucial factor that the developer should consider during selection?

Number of variables ipn the dataset

Size of the dataset

Age of the dataset

Explanation
“The size of the dataset is a crucial factor that the developer should consider during selection. The size of the dataset refers to the amount or volume of data available for training an AI model. The size of the dataset can affect the feasibility and quality of the AI model, as well as the choice of AI techniques and tools. The size of the dataset should be large enough to provide sufficient information for the AI model to learn from and generalize well to new data.”

NO.35 What role does data quality play in the ethical us of AI applications?

High-quality data is essential for ensuring unbased and for fair AI decisions, promoting ethical use, and preventing discrimi…

High-quality data ensures the process of demographic attributes requires for personalized campaigns.

Low-quality data reduces the risk of unintended bias as the data is not overfitted to demographic groups.

Explanation
“High-quality data is essential for ensuring unbiased and fair AI decisions, promoting ethical use, and preventing discrimination. High-quality data means that the data is accurate, complete, consistent, relevant, and timely for the AI task. High-quality data can help ensure unbiased and fair AI decisions by providing a balanced and representative sample of the target population or domain. High-quality data can also help promote ethical use and prevent discrimination by respecting the rights and preferences of users regarding their personal data.”

NO.36 What should organizations do to ensure data quality for their AI initiatives?

Collect and curate high-quality data from reliable sources.

Rely on AI algorithms to automatically handle data quality issues.

Prioritize model fine-tuning over data quality improvements.

Explanation
“Organizations should collect and curate high-quality data from reliable sources to ensure data quality for their AI initiatives. High-quality data means that the data is accurate, complete, consistent, relevant, and timely for the AI task. Reliable sources mean that the data is trustworthy, credible, and authoritative. Collecting and curating high-quality data from reliable sources can improve the performance and reliability of AI systems.”

NO.37 A marketing manager wants to use AI to better engage their customers.
Which functionality provides the best solution?

Journey Optimization

Bring Your Own Model

Einstein Engagement

Explanation
“Einstein Engagement provides the best solution for a marketing manager who wants to use AI to better engage their customers. Einstein Engagement is a feature that uses AI to optimize email marketing campaigns by providing insights and recommendations on the best time, frequency, content, and subject lines to send emails to each customer. Einstein Engagement can help increase customer engagement, retention, and loyalty by delivering personalized and relevant messages.”

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