AI Associate Practice Test 31 — 15 Questions

Question 1

An administrator wants to use Einstein GPT to automatically generate case summaries and draft knowledge articles. Which THREE features should they enable?

Accepted Answer

Service GPT for Knowledge Article Drafts. Service GPT for Knowledge Article Drafts (Option A) is correct because it is the specific Einstein GPT feature designed to automatically generate knowledge article drafts from case details, enabling administrators to streamline content creation. This feature leverages generative AI to produce draft articles based on resolved cases, reducing manual effort.

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Sales GPT for Email Generation

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Einstein Reply Recommendations

Question 2

A data scientist is evaluating a custom Einstein model for a lead scoring use case. The model's precision is 0.9, recall is 0.5. What is the most important improvement priority?

Accepted Answer

Increase recall to reduce false negatives. With a precision of 0.9 and recall of 0.5, the model is highly selective but misses many actual leads (high false negatives). In lead scoring, false negatives mean lost sales opportunities, which is typically more costly than false positives. Therefore, increasing recall to capture more true positives is the most important improvement priority.

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Increase precision to reduce false positives

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Optimize for an F1 score of 0.7

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Improve overall accuracy above 80%

Question 3

A legal firm wants to automate contract clause generation using AI with preapproved language. Which approach should they use?

Accepted Answer

Einstein GPT with Clause Library. Einstein GPT with Clause Library is specifically designed to generate contract clauses using preapproved language. It leverages generative AI combined with a library of approved legal clauses, ensuring compliance and consistency while automating clause creation within Salesforce.

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Email Templates

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Flow

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Document Templates

Question 4

An admin is setting up Einstein Prediction Builder to predict whether a lead will convert. The admin has selected the prediction field and data set. What is the next step in the configuration wizard?

Accepted Answer

Select features (input fields) to train the model. After selecting the prediction field and data set in Einstein Prediction Builder, the next step is to select the features (input fields) that the model will use to learn patterns and make predictions. These features are the variables that correlate with the outcome. The wizard then proceeds to train the model, but feature selection is a prerequisite. Defining prediction explanation and choosing the score field come later or are optional.

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Train the model immediately

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Define the prediction explanation

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Choose the prediction score field

Question 5

A sales operations team wants to automatically categorize incoming support cases into predefined categories (e.g., Billing, Technical, General). The team has thousands of historical cases with correct category labels. Which AI approach should they use?

Accepted Answer

Text classification. Text classification is a supervised learning approach where a model is trained on labeled historical cases to predict predefined categories. With thousands of labeled examples, this is the ideal approach for automatically categorizing incoming support cases.

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Topic modeling (unsupervised)

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Sentiment analysis

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Named entity recognition

Question 6

A company wants to use Agentforce to create an autonomous agent that can handle order cancellations. Which TWO components are required when building the agent in Agent Builder?

Accepted Answer

Actions. In Agent Builder, Topics (E) are required because they define the scope of what the agent can handle — each topic groups the instructions, actions, and reasoning the agent uses for a specific job such as order cancellations. Actions (D) are also required because they are the concrete tools the agent invokes to perform tasks, such as calling a Flow or Apex to actually cancel an order. Together, a Topic provides the agent's job definition while its Actions give it the executable capabilities to complete that job. Flows (A) are not required components of the agent itself; they are one possible implementation behind an Action, not a mandatory building block. Intents (B) are not a required Agent Builder component — intent detection is handled by the agent's reasoning over topics rather than a separately authored intent. Prompts (C) are not a required component either; prompt templates are optional assets the agent may use, not a mandatory part of the agent definition.

Answer

Flows

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Intents

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Prompts

Question 7

A data analyst is evaluating data quality for an Einstein model. Which TWO dimensions are most critical for model accuracy?

Accepted Answer

Accuracy. Accuracy (B) is critical because Einstein models learn from the values in the training data, so if records contain incorrect or mislabeled values, the model will learn and predict wrong patterns, directly degrading model accuracy. Completeness (D) is equally critical because missing values in key fields reduce the effective training set and can bias or weaken the model's learned relationships, so having all required attributes populated is essential for reliable predictions. Uniqueness (A) matters mainly for deduplication and record identity rather than predictive correctness, consistency (C) concerns uniform formats/values across sources, and timeliness (E) concerns how current the data is — all useful data-quality dimensions, but not the two most directly tied to model accuracy in this scenario.

Answer

Uniqueness

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Consistency

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Timeliness

Question 8

A sales manager wants to predict which deals are likely to close this quarter. The CRM has rich historical data on won/lost opportunities, deal amount, and sales stage. Which AI approach is best suited for this task?

Accepted Answer

Predictive AI for opportunity scoring. Predictive AI is designed to analyze historical data and forecast outcomes, making it ideal for scoring opportunities based on patterns from past won/lost deals. It uses supervised learning models trained on labeled data (e.g., deal amount, sales stage, historical outcomes) to output a probability of closing. This directly addresses the sales manager's need to prioritize deals likely to close this quarter.

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Generative AI to create new sales content

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Unsupervised learning to cluster opportunities

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Reinforcement learning for sales strategy

Question 9

Which TWO of the following are capabilities of Einstein Conversation Insights?

Accepted Answer

Keyword tracking across call recordings. Einstein Conversation Insights is a Salesforce feature that analyzes recorded calls and meetings to surface insights for sales teams. Option A is correct because it provides keyword tracking across call recordings, letting teams monitor specific terms, competitor mentions, or product names within analyzed calls. Option C is correct because it delivers talk-time metrics for agents, showing how much each participant spoke during a call as part of its conversation analytics. Option B is not correct because lead scoring is handled by Einstein Lead Scoring, not Conversation Insights. Option D is not correct because Conversation Insights works on recorded calls rather than providing real-time transcription during live calls. Option E is not correct because automated email generation is not a capability of Einstein Conversation Insights.

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Lead scoring based on call sentiment

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Real-time transcription during calls

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Automated email generation for follow-ups

Question 10

A company using Einstein Bots wants to improve the bot's ability to understand varied customer expressions (e.g., 'I want a refund', 'money back', 'return my purchase'). Which component should they configure to map these expressions to a common intention?

Accepted Answer

Intents. Intents are the correct component because they define the purpose or goal behind a customer's input. In Einstein Bots, intents are trained to recognize varied phrasings (e.g., 'I want a refund', 'money back', 'return my purchase') and map them to a single, common intention, such as 'Request_Refund'. This allows the bot to understand and route the conversation appropriately without requiring exact keyword matches.

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Entities

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NLP training

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Dialog flows

Question 11

A company deploys an AI-powered email composer that drafts responses to customer inquiries. To comply with GDPR, which control should they implement regarding automated decisions?

Accepted Answer

Require human review before any AI-generated email is sent. Under GDPR, Article 22 grants individuals the right not to be subject to a decision based solely on automated processing, including profiling, which produces legal effects or similarly significant effects. For an AI email composer that drafts responses to customer inquiries, requiring human review before any AI-generated email is sent ensures that the final decision to communicate is not fully automated, thereby complying with GDPR's requirement for meaningful human intervention in automated decision-making.

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Allow the AI to send emails automatically if confidence is high

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Disable the AI composer entirely to avoid GDPR risk

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Anonymize customer data before drafting

Question 12

To recommend the most relevant action for a service agent during a call, which feature should be used?

Accepted Answer

Einstein Next Best Action. Einstein Next Best Action is the correct feature because it uses AI to analyze the call context in real time and recommend the most relevant action for a service agent, such as offering a specific discount or knowledge article. Unlike static automation tools, it leverages predictive models to adapt recommendations based on customer data and conversation sentiment, ensuring the agent takes the optimal next step.

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Flow

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Quick Actions

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Process Builder

Question 13

Refer to the exhibit. An AI loan approval policy is defined as a JSON rule set. Which ethical issue is most prominent based on this policy?

Accepted Answer

Potential for geographic discrimination due to zip code condition. Using zip code as a condition in a loan approval rule set is a well-known proxy for race and ethnicity because residential segregation correlates strongly with protected characteristics. This creates potential geographic discrimination, which is the most prominent ethical issue in the policy. Income and credit score are legitimate underwriting factors, and the rule set is actually transparent because it is expressed as explicit JSON rules.

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Use of irrelevant attributes like income and credit score

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Insufficient accuracy due to simple rules

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Lack of transparency in decision-making

Question 14

A data analyst is reviewing an Einstein Discovery story and notices that one input feature has a very high influence on the predicted outcome. Which two conclusions are justified based on this observation? (Choose 2)

Accepted Answer

The feature could be a surrogate for other correlated features.. A feature with high influence in an Einstein Discovery story may be acting as a proxy for other correlated features, meaning its apparent importance could be due to shared variance with other predictors. This is a known phenomenon in machine learning where collinearity can inflate a feature's influence score without it being uniquely causal.

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The feature has a causal relationship with the outcome.

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Removing the feature will significantly improve model performance.

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The model is likely overfitted to that feature.

Question 15

A company is deploying an AI-powered chatbot to handle customer service inquiries. The bot uses historical chat data for training. Which ethical consideration is MOST important to address before deployment?

Accepted Answer

Obtaining consent from customers whose data is used for training. The most critical ethical consideration is obtaining consent from customers whose historical chat data is used to train the chatbot. Under regulations like GDPR and CCPA, personal data (including chat transcripts) requires explicit consent for processing, especially when used to train an AI model. Deploying without consent violates data privacy laws and erodes user trust, regardless of the chatbot's technical performance.

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Maximizing the chatbot's response accuracy

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Ensuring the chatbot can handle high traffic volumes

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Designing a human handoff protocol for complex issues