[Q18-Q42] Latest Salesforce-AI-Associate Exam with Accurate Salesforce Certified AI Associate Exam PDF Questions [Jan 08, 2025]

admin -

January 8, 2025

5/5 - (1 vote)

[Jan 08, 2025] Latest Salesforce-AI-Associate Exam with Accurate Salesforce Certified AI Associate Exam PDF Questions

Practice To Salesforce-AI-Associate – DumpsTests Remarkable Practice On your Salesforce Certified AI Associate Exam Exam

NEW QUESTION 18
Which action introduces bias in the training data used for AI algorithms?

Using a large dataset that is computationally expensive

Using a dataset that represents diverse perspectives and populations

Using a dataset that underrepresents perspectives and populations

Introducing bias in training data for AI algorithms occurs when the dataset used underrepresents certain perspectives and populations. This type of bias can skew AI predictions, making the system less fair and accurate. For example, if a dataset predominantly contains information from one demographic group, the AI’s performance may not generalize well to other groups, leading to biased or unfair outcomes. Salesforce discusses the impact of biased training data and ways to mitigate this in their AI ethics guidelines, which can be explored further in the Salesforce AI documentation on Responsible Creation of AI.

NEW QUESTION 19
What is a Key consideration regarding data quality in AI implementation?

Techniques from customizing AI features in Salesforce

Data’s role in training and fine-tuning Salesforce AI models

Integration process of AI models with Salesforce workflows

Explanation
“Data’s role in training and fine-tuning Salesforce AI models is a key consideration regarding data quality in AI implementation. 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. Data’s role in training and fine-tuning Salesforce AI models means understanding how data is used to build, train, test, and improve AI models in Salesforce, such as Einstein Prediction Builder or Einstein Discovery.”

NEW QUESTION 20
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.”

NEW QUESTION 21
Cloud Kicks discovered multiple variations of state and country values in contact records.
Which data quality dimension is affected by this issue?

Usage

Accuracy

Consistency

Explanation
“Consistency is the data quality dimension that is affected by multiple variations of state and country values in contact records. 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.”

NEW QUESTION 22
What Is a benefit of data quality and transparency as it pertains to bias in generated AI?

Chances of bIas and mitigated

Chances of bias are aggravated

Chances of bias are remove

A benefit of data quality and transparency as it pertains to bias in generated AI is that the chances of bias are mitigated. High data quality ensures that AI models are trained on accurate and representative data, reducing the risk of biased outcomes. Transparency in AI processes helps stakeholders understand how decisions are made, allowing for the identification and correction of potential biases. Together, these practices contribute to the development of fairer and more accountable AI systems. Salesforce highlights the importance of these principles in its AI practices, particularly through its ethical AI framework, which advocates for fairness and accountability. More on Salesforce’s commitment to promoting unbiased AI can be found in their AI ethics guidelines at Salesforce AI Ethics.

NEW QUESTION 23
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

“EinsteinEngagement 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.”

NEW QUESTION 24
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.”

NEW QUESTION 25
What is an implication of user consent in regard to AI data privacy?

AI ensures complete data privacy by automatically obtaining user consent.

AI infringes on privacy when user consent is not obtained.

AI operates Independently of user privacy and consent.

“AI infringes on privacy when user consent is not obtained. User consent is the permission or agreement given by a user to allow their personal data to be collected, used, shared, or stored byothers. User consent is an important aspect of data privacy, which is the right of individuals to control how their personal data is handled by others. AI infringes on privacy when user consent is not obtained because it violates the user’s rights and preferences regarding their personal data.”

NEW QUESTION 26
Which action should be taken to develop and implement trusted generated AI with Salesforce’s safety guideline in mind?

Develop right-sized models to reduce our carbon footprint.

Create guardrails that mitigates toxicity and protect PII

Be transparent when AI has created and automatically delivered content.

“Creating guardrails that mitigate toxicity and protect PII is an action that should be taken to develop and implement trusted generativeAI with Salesforce’s safety guideline in mind. Salesforce’s safety guideline is one of the Trusted AI Principles that states that AI systems should be designed and developed with respect for the safety and well-being of humans and the environment. Creatingguardrails means implementing measures or mechanisms that can prevent or limit the potential harm or risk caused by AI systems. For example, creating guardrails can help mitigate toxicity by filtering out inappropriate or offensive content generated by AIsystems. Creating guardrails can also help protect PII by masking or anonymizing personal or sensitive information generated by AI systems.”

NEW QUESTION 27
Cloud Kicks relies on data analysis to optimize its product recommendation; however, CK encounters a recurring Issue of Incomplete customer records, withmissing contact Information and incomplete purchase histories.
How will this incomplete data quality impact the company’s operations?

The response time for product recommendations is stalled.

The accuracy of product recommendations is hindered.

The diversity of product recommendations Is Improved.

“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.”

NEW QUESTION 28
Cloud Kicks wants to use AI to enhance its sales processes and customer support.
Which capacity should they use?

Dashboard of Current Leads and Cases

Sales path and Automaton Case Escalations

Einstein Lead Scoring and Case Classification

“Einstein Lead Scoring and Case Classification are the capabilities that Cloud Kicks should use to enhance its sales processes and customer support. Einstein Lead Scoring and Case Classification are features that use AI to optimize sales and service processes by providing insights and recommendations based on data.
Einstein Lead Scoring can help prioritize leads based on their likelihood to convert, while Einstein Case Classification can help categorize and route cases based on their attributes.”

NEW QUESTION 29
What is a sensitive variable that car esc to bias?

Education level

Country

Gender

“Gender is a sensitive variable that can leadto bias. A sensitive variable is a variable that can potentially cause discrimination or unfair treatment based on a person’s identity or characteristics. For example, gender is a sensitive variable because it can affect how people are perceived, treated,or represented by AI systems.”

NEW QUESTION 30
What is a potential outcome of using poor-quality data in AI application?

AI model training becomes slower and less efficient

AI models may produce biased or erroneous results.

AI models become more interpretable

“A potential outcome of using poor-quality data inAI applications is that AI models may produce biased or erroneous results. Poor-quality data means that the data is inaccurate, incomplete, inconsistent, irrelevant, or outdated for the AI task. Poor-quality data can affect the performance and reliabilityof AI models, as they may not have enough or correct information to learn from or make accurate predictions. Poor-quality data can also introduce or exacerbate biases or errors in AI models, such as human bias, societal bias, confirmation bias, or overfitting or underfitting.”

NEW QUESTION 31
Cloud Kicks is testing a new AI model.
Which approach aligns with Salesforce’s Trusted AI Principle of Incluslvity?

Test only with data from a specific region or demographic to limit the risk of data leaks.

Rely on a development team with uniform backgrounds to assess the potential societal implications of the model.

Test with diverse and representative datasets appropriate for how the model will be used.

“Testing with diverse and representative datasets appropriate for how the model will be used aligns with Salesforce’s Trusted AI Principle of Inclusivity. Inclusivity means that AI systems should be designed and developed with respect for diversity and inclusion of different perspectives, backgrounds, and experiences.
Testing with diverse and representative datasets can help ensure that the models are fair, unbiased, and representative of the target population or domain.”

NEW QUESTION 32
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.”

NEW QUESTION 33
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.”

NEW QUESTION 34
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.”

NEW QUESTION 35
A Salesforce administrator creates a new field to capture an order’s destination country.
Which field type should they use to ensure data quality?

Text

Picklist

Number

“A picklist field type should be used to ensure data quality for capturing an order’s destinationcountry. A picklist field type allows the user to select one or more predefined values from a list. A picklist field type can ensure data quality by enforcing consistency, accuracy, and completeness of the data values.”

NEW QUESTION 36
Cloud Kicks plans to use automated chat as its primary support channel.
Which Einstein feature should they use?

Discovery

Bots

Next Best Action

For Cloud Kicks, using automated chat as the primary support channel, the recommended Einstein feature is Bots. Einstein Bots are designed to automate customer interactions on common issues through chat and messaging platforms. They can handle routine requests, provide quick answers to frequently asked questions, and escalate more complex issues to human agents. Using Einstein Bots helps improve customer service efficiency and speed, leading to enhanced customer satisfaction. To learn more about setting up and optimizing Einstein Bots for a business, you can visit the Salesforce documentation on Einstein Bots at Salesforce Einstein Bots.

NEW QUESTION 37
Which best describes the difference between predictive AI and generative Al?

Predictive AT uses machine learning to classify or predict outputs from its input data whereas generative Al does not use machine learning to generate its output.

Predictive Al uses machine learning to classify or predict outputs from its input data whereas generative Al uses machine learning to generate new and original output for 4 given input

Predictive Al and generative Al have the same capabilities but differ in the type of input they receive; predictive AT receives raw data whereas generative AT receives natural language.

Predictive AI and generative AI represent two different applications of machine learning technologies.
Predictive AI focuses on making predictions based on historical data. It analyzes past data to forecast future outcomes, such as customer churn or sales trends. On the other hand, generative AI is designed to generate new and original outputs based on the learned data patterns. This includes tasks like creating new images, text, or music that resemble the training data but do not duplicate it. Both types of AI use machine learning, but their objectives and outputs are distinct. For detailed differences and applications in a Salesforce context, Salesforce’s guide on AI technologies is a helpful resource, accessible at Salesforce AI Technologies.

NEW QUESTION 38
Cloud kicks wants to develop a solution to predict customers’ interest based on historical data. The company found that employee region uses a text field to capture the product category while employee from all other locations use a picklist.
Which dimension of data quality is affected in this scenario?

Accuracy

Completeness

Consistency

NEW QUESTION 39
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.”

NEW QUESTION 40
A customer using Einstein Prediction Builder is confused about why a certain prediction was made.
Following Salesforce’s Trusted AI Principle of Transparency, which customer information should be accessible on the Salesforce Platform?

An explanation of how Prediction Builder works and a link to Salesforce’s Trusted AI Principles

An explanation of the prediction’s rationale and a model card that describes how the model was created

A marketing article of the product that clearly outlines the oroduct’s capabilities and features

Explanation
“An explanation of the prediction’s rationale and a model card that describes how the model was created should be accessible on the Salesforce Platform following Salesforce’s Trusted AI Principle of Transparency.
Transparency means that AI systems should be designed and developed with respect for clarity and openness in how they work and why they make certain decisions. Transparency also means that AI users should be able to access relevant information and documentation about the AI systems they interact with.”

NEW QUESTION 41
In the context of Salesforce’s Trusted AI Principles what does the principle of Empowerment primarily aim to achieve?

Empower users to off all skill level to build AI application with clicks, not code.

Empower users to contribute to the growing body of knowledge of leading AI research.

Empower users to solve challenging technical problems using neural networks.

Explanation
“The principle of Empowerment primarily aims to achieve empowering users of all skill levels to build AI applications with clicks, not code. Empowerment is one of the Trusted AI Principles that states that AI systems should be designed and developed with respect for the empowerment and education of humans. Empowering users means enabling users to access, use, and benefit from AI systems regardless of their technical expertise or background. For example, empowering users means providing tools and platforms that allow users to build AI applications with clicks, not code, such as Einstein Prediction Builder or Einstein Discovery.”

NEW QUESTION 42
Cloud kicks wants to develop a solution to predict customers’ interest based on historical data. The company found that employee region uses a text field to capture the product category while employee from all other locations use a picklist.
Which dimensionof data quality is affected in this scenario?

Accuracy

Consistency

Completeness

“Consistency is the dimension of data quality 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.”

Loading …

Salesforce Salesforce-AI-Associate Exam Syllabus Topics:

Topic	Details
Topic 1	Data for AI: Questions about the importance of data quality and different elements or components of data quality are related to this topic.
Topic 2	AI Capabilities in CRM: Get familiar with the benefits of AI and capabilities of CRM.
Topic 3	AI Fundamentals: This topic discusses the major principles and applications of AI within Salesforce. It also focuses on different types of AI and their capabilities.
Topic 4	Ethical Considerations of AI: It delves into the ethical challenges of AI such as human bias in machine learning, lack of transparency, etc. The topic also explains how to apply Trusted AI Principles of Salesforce to given scenarios.