Salesforce AI Associate Questions and
Answers| Latest Update
NO.1 Which features of Einstein enhance sales efficiency and effectiveness?
Options:
A. Opportunity List View, Lead List View, Account List view
B. Opportunity Scoring, Opportunity List View, Opportunity Dashboard
C. Opportunity Scoring, Lead Scoring, Account Insights ✔️✔️Answer: C
Explanation: Opportunity Scoring, Lead Scoring, Account Insights are features of Einstein that
enhance sales efficiency and effectiveness. These features use predictive models and natural
language processing to provide insights and scores for better sales decision-making.
NO.2 Cloud Kicks wants to optimize its business operations by incorporating AI into its CRM.
What should the company do first to prepare its data for use with AI?
Options:
A. Remove biased data.
B. Determine data availability.
C. Determine data outcomes. ✔️✔️Answer: B
Explanation: Before using AI, Cloud Kicks should assess the availability and quality of its data.
Understanding what data is available and its condition is crucial for effective AI integration.
NO.3 A marketing manager wants to use AI to better engage their customers. Which
functionality provides the best solution?
Options:
A. Journey Optimization
B. Bring Your Own Model
C. Einstein Engagement ✔️✔️Answer: C
,Explanation: Einstein Engagement is the best solution for enhancing customer engagement
through AI. It optimizes email marketing campaigns with insights and recommendations,
personalizing customer interactions.
NO.4 What is the key difference between generative and predictive AI?
A. Generative AI creates new content based on existing data predictive AI analyzes existing
data.
B. Generative AI finds content similar to existing data; predictive AI analyzes existing data.
C. Generative AI analyzes existing data; predictive AI creates new content based on existing
data. ✔️✔️Answer: A
Explanation: The key difference is that generative AI creates new content based on existing
data, whereas predictive AI focuses on analyzing existing data to make predictions or
recommendations.
NO.5 What is the main focus of the Accountability principle in Salesforce's Trusted AI
Principles?
Options:
A. Safeguarding fundamental human rights and protecting sensitive data
B. Taking responsibility for one's actions toward customers, partners, and society
C. Ensuring transparency in AI-driven recommendations and predictions ✔️✔️Answer: B
Explanation: The Accountability principle focuses on taking responsibility for one's actions in
the development and use of AI, emphasizing ethical, legal, and regulatory compliance and
impact on stakeholders.
NO.6 Which type of bias results from data being labeled according to stereotypes?
Options:
A. Association
B. Societal
C. Interaction ✔️✔️Answer: B
, Explanation: Societal bias occurs when data is labeled based on stereotypes, reflecting societal
assumptions, norms, or values.
NO.7 How does the "right of least privilege" reduce the risk of handling sensitive personal data?
Options:
A. By limiting how many people have access to data
B. By reducing how many attributes are collected
C. By applying data retention policies ✔️✔️Answer: A
Explanation: The "right of least privilege" minimizes the risk by limiting access to sensitive data
to only those individuals who absolutely need it for their specific tasks or roles.
NO.8 Cloud Kicks discovered multiple variations of state and country values in contact records.
Which data quality dimension is affected by this issue?
Options:
A. Usage
B. Accuracy
C. Consistency ✔️✔️Answer: C
Explanation: Consistency is impacted by the multiple variations of state and country values, as it
entails uniformity and adherence to common standards in data values across records.
Q1. Which of the following is a common concern about Generative AI?
A. Deep learning
B. Natural language processing
C. Hallucinations ✔️✔️Answer: C. Hallucinations
Predictions from generative AI that diverge from an expected response, grounded in facts, are
known as hallucinations. They happen for a few reasons, like if the training data was incomplete
or biased, or if the model was not designed well.
Answers| Latest Update
NO.1 Which features of Einstein enhance sales efficiency and effectiveness?
Options:
A. Opportunity List View, Lead List View, Account List view
B. Opportunity Scoring, Opportunity List View, Opportunity Dashboard
C. Opportunity Scoring, Lead Scoring, Account Insights ✔️✔️Answer: C
Explanation: Opportunity Scoring, Lead Scoring, Account Insights are features of Einstein that
enhance sales efficiency and effectiveness. These features use predictive models and natural
language processing to provide insights and scores for better sales decision-making.
NO.2 Cloud Kicks wants to optimize its business operations by incorporating AI into its CRM.
What should the company do first to prepare its data for use with AI?
Options:
A. Remove biased data.
B. Determine data availability.
C. Determine data outcomes. ✔️✔️Answer: B
Explanation: Before using AI, Cloud Kicks should assess the availability and quality of its data.
Understanding what data is available and its condition is crucial for effective AI integration.
NO.3 A marketing manager wants to use AI to better engage their customers. Which
functionality provides the best solution?
Options:
A. Journey Optimization
B. Bring Your Own Model
C. Einstein Engagement ✔️✔️Answer: C
,Explanation: Einstein Engagement is the best solution for enhancing customer engagement
through AI. It optimizes email marketing campaigns with insights and recommendations,
personalizing customer interactions.
NO.4 What is the key difference between generative and predictive AI?
A. Generative AI creates new content based on existing data predictive AI analyzes existing
data.
B. Generative AI finds content similar to existing data; predictive AI analyzes existing data.
C. Generative AI analyzes existing data; predictive AI creates new content based on existing
data. ✔️✔️Answer: A
Explanation: The key difference is that generative AI creates new content based on existing
data, whereas predictive AI focuses on analyzing existing data to make predictions or
recommendations.
NO.5 What is the main focus of the Accountability principle in Salesforce's Trusted AI
Principles?
Options:
A. Safeguarding fundamental human rights and protecting sensitive data
B. Taking responsibility for one's actions toward customers, partners, and society
C. Ensuring transparency in AI-driven recommendations and predictions ✔️✔️Answer: B
Explanation: The Accountability principle focuses on taking responsibility for one's actions in
the development and use of AI, emphasizing ethical, legal, and regulatory compliance and
impact on stakeholders.
NO.6 Which type of bias results from data being labeled according to stereotypes?
Options:
A. Association
B. Societal
C. Interaction ✔️✔️Answer: B
, Explanation: Societal bias occurs when data is labeled based on stereotypes, reflecting societal
assumptions, norms, or values.
NO.7 How does the "right of least privilege" reduce the risk of handling sensitive personal data?
Options:
A. By limiting how many people have access to data
B. By reducing how many attributes are collected
C. By applying data retention policies ✔️✔️Answer: A
Explanation: The "right of least privilege" minimizes the risk by limiting access to sensitive data
to only those individuals who absolutely need it for their specific tasks or roles.
NO.8 Cloud Kicks discovered multiple variations of state and country values in contact records.
Which data quality dimension is affected by this issue?
Options:
A. Usage
B. Accuracy
C. Consistency ✔️✔️Answer: C
Explanation: Consistency is impacted by the multiple variations of state and country values, as it
entails uniformity and adherence to common standards in data values across records.
Q1. Which of the following is a common concern about Generative AI?
A. Deep learning
B. Natural language processing
C. Hallucinations ✔️✔️Answer: C. Hallucinations
Predictions from generative AI that diverge from an expected response, grounded in facts, are
known as hallucinations. They happen for a few reasons, like if the training data was incomplete
or biased, or if the model was not designed well.
