AI Associate Practice Test 3 — 10 Questions

Question 1

A developer is building an Einstein Bot that needs to understand when a customer says 'I want to return a purchase' and route them to the returns process. How should they configure the bot?

Accepted Answer

Create an intent called 'Return Purchase' and train it with sample phrases. In Einstein Bots, intents represent the customer's goal, and entities capture specifics. 'Return purchase' is an intent, not an entity. The bot uses NLP to match utterances to intents.

Answer

Create a dialogue that triggers on the exact phrase 'I want to return a purchase'

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Create an entity called 'Return Purchase' and map it to a dialogue

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Use an intent called 'Customer Service' and a custom entity for return

Question 2

A sales team wants to use Einstein Lead Scoring to prioritize leads. What is the primary benefit of using Einstein Lead Scoring over manual scoring?

Accepted Answer

It uses historical data to predict which leads are most likely to convert.. Einstein Lead Scoring uses historical data and machine learning models to analyze patterns from past leads and their conversion outcomes. This allows it to assign a predictive score to each new lead, indicating the likelihood of conversion, which is more accurate and data-driven than manual scoring based on subjective criteria.

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It automatically sends personalized emails to leads.

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It ensures all leads are contacted within 24 hours.

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It replaces the need for any manual lead qualification process.

Question 3

A service agent needs to quickly find a relevant knowledge article while working on a case. Which Einstein feature can automatically suggest articles based on the case details?

Accepted Answer

Einstein Article Recommendations. Einstein Article Recommendations is the correct answer because it is the specific Einstein feature designed to automatically surface relevant knowledge articles based on the context of a case, such as subject, description, and product. It uses natural language processing (NLP) to match case details against article content, providing agents with immediate, relevant suggestions without manual search.

Answer

Einstein Prediction Builder

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Einstein Case Classification

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Einstein Next Best Action

Question 4

Refer to the exhibit. A Salesforce admin configured the Einstein Trust Layer policy shown. What is the effect of this policy on AI model usage?

Accepted Answer

AI models receive masked data for those fields, but feature importance insights are still available.. The Einstein Trust Layer policy configured to mask specific fields ensures that sensitive data is replaced with masked values before being sent to the AI model. This preserves data privacy while still allowing the model to generate predictions and insights. Feature importance insights remain available because they are computed from the masked data, not the original values.

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All fields in the org will be masked to protect customer privacy.

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AI models will not be able to use the configured fields, and model insights are disabled.

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AI models can still use the fields but feature importance insights are blocked.

Question 5

Refer to the exhibit. Based on the JSON policy for AI fairness checks, which fairness metric is NOT enabled?

Accepted Answer

Equal opportunity. (Equal opportunity) is correct because the JSON policy shown in the exhibit configures fairness checks for demographic parity, disparate impact, and equalized odds, but does NOT include the equal opportunity metric. Equal opportunity requires equal true positive rates across groups, which is a separate metric from equalized odds and must be explicitly enabled in the policy definition.

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Demographic parity

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All are enabled

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Disparate impact

Question 6

An admin is configuring Einstein Prediction Builder to predict case escalation. Which TWO components must be selected during setup?

Accepted Answer

Prediction field (binary classification target). Einstein Prediction Builder requires a binary classification target field to define the outcome being predicted—in this case, whether a case will escalate. This field must have exactly two distinct values (e.g., 'Yes'/'No' or 0/1) to train the model. Without specifying the prediction field, the builder cannot determine what event to forecast.

Answer

Prediction explanation template

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Data set (records to train on)

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Prediction score field name

Question 7

A sales manager wants to use Einstein Lead Scoring but is concerned about transparency for the sales team. Which TWO features should they enable to provide explainability? (Choose 2)

Accepted Answer

Score Factors on the lead record. Score Factors show why a lead scored as it did, and labeling AI-generated scores helps reps understand that the score is AI-driven.

Answer

Einstein Copilot

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Einstein Activity Capture

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Einstein Trust Layer audit trail

Question 8

A sales rep wants to automatically log emails and events from Outlook to Salesforce without manual work. Which Einstein feature should be enabled?

Accepted Answer

Einstein Activity Capture. Einstein Activity Capture (C) is the correct feature because it automatically syncs emails and events from Microsoft Outlook (or Google Workspace) to Salesforce without requiring manual logging. It uses a background sync engine that captures email headers and calendar events based on configured rules, eliminating the need for users to manually log activities via add-ins or plugins.

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Einstein Conversation Insights

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Einstein Email Insights

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Einstein Lead Scoring

Question 9

Which Einstein feature uses strategy builder (flows, Apex) to recommend offers or actions to users at the right moment?

Accepted Answer

Einstein Next Best Action. Einstein Next Best Action is the correct answer because it is the Einstein feature that uses Strategy Builder (which includes flows and Apex) to define decision logic and recommend the most relevant offers or actions to users at the right moment. It evaluates real-time context and business rules to surface the optimal next step, such as a discount or a follow-up task, directly within the Salesforce user interface.

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Einstein Copilot

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Einstein Prediction Builder

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Einstein Recommendation Builder

Question 10

A data scientist notices that a churn prediction model has high variance: small changes in training data cause large changes in predictions. Which technique is BEST to address this?

Accepted Answer

Apply regularization techniques. High variance indicates overfitting. Regularization (e.g., L1/L2) reduces model complexity and variance. Adding more features would increase variance, increasing training data can help but is not always feasible, and reducing training data would worsen the problem.

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Reduce the amount of training data

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Add more features to the model

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Increase the number of training epochs