AI Associate Practice Exam 2 — 20 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.

Answer

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

Answer

Einstein Case Classification

Answer

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.

Answer

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.

Answer

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.

Answer

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.

Answer

Einstein Copilot

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Einstein Prediction Builder

Answer

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.

Answer

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

Question 11

A marketer wants to use Einstein Segment Creation to build a segment for a campaign. Which data source can be used?

Accepted Answer

Data Cloud unified profile data.. Einstein Segment Creation works with Data Cloud unified profiles.

Answer

Standard report snapshots.

Answer

External web analytics.

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Einstein Activity Capture data.

Question 12

A financial services company uses Salesforce AI to detect fraudulent transactions. The dataset has 1 million legitimate transactions and only 1,000 fraudulent ones. The model trained with default parameters achieves 99.9% accuracy but identifies no fraud (precision and recall of 0). The data scientist wants to maximize fraud detection (recall) while minimizing false positives. Which approach is most effective?

Accepted Answer

Use SMOTE to generate synthetic fraud samples to balance the dataset.. With extreme imbalance, oversampling the minority class (e.g., SMOTE) generates synthetic fraud examples, helping the model learn fraud patterns and improve recall without discarding legitimate data.

Answer

Increase the weight of the majority class in the loss function.

Answer

Train multiple models on different random subsets and average predictions.

Answer

Use a simpler model to avoid overfitting on the majority class.

Question 13

A company deploys an AI recommender system that personalizes content. The system is trained on user click data. After deployment, the company notices that the system increasingly recommends sensationalist content, leading to user polarization. Which principle is being violated?

Accepted Answer

Beneficence. The recommender system's shift toward sensationalist content, which polarizes users, violates the principle of beneficence because it causes harm (user polarization) rather than promoting well-being. Beneficence requires AI systems to act in the best interests of users and society, not to optimize for engagement metrics at the expense of ethical outcomes.

Answer

Accuracy

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Privacy

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Transparency

Question 14

A financial services company is deploying Einstein AI and must comply with regulations requiring explainable decisions. Which Einstein capability allows them to understand why an AI model made a specific prediction?

Accepted Answer

Einstein Trust Layer with model explainability features.. The Einstein Trust Layer includes model explainability features that provide insights into why a specific prediction was made, such as highlighting the key input features that influenced the outcome. This directly addresses regulatory requirements for explainable AI decisions by offering transparency into the model's reasoning process.

Answer

Data Mask to protect sensitive information.

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Salesforce Shield with encryption and monitoring.

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Field Audit Trail to track changes to data.

Question 15

A data scientist is using Einstein Vision and Language Platform for text classification. They need to handle custom entities (NER) and classify text into multiple categories. Which THREE capabilities of the Einstein Platform Services API should they use?

Accepted Answer

Sentiment Analysis. Sentiment Analysis is a key capability of the Einstein Platform Services API that allows the data scientist to determine the emotional tone (positive, negative, or neutral) of text, which is essential for understanding customer feedback or social media posts. This complements the other required capabilities—Text Classification for categorizing text into multiple categories and Named Entity Recognition (NER) for extracting custom entities—forming a complete solution for the described text classification and NER tasks.

Answer

Object Detection

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Image Classification

Question 16

An AI system is used to approve loan applications. The model uses income, zip code, and credit score as features. What is a potential ethical concern?

Accepted Answer

Zip code may act as a proxy for race, leading to discrimination. Using zip code as a feature can introduce proxy discrimination. Zip codes are strongly correlated with race and socioeconomic status due to historical redlining and residential segregation. When the model learns patterns from zip code, it may inadvertently deny loans to applicants from certain racial or ethnic groups, violating fair lending laws and ethical AI principles.

Answer

The model should be a black box to avoid bias

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Credit scores are rarely accurate

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Income is not a reliable predictor of repayment

Question 17

A company uses an NLP model to detect intent in customer messages. The model works well for English but fails for Spanish messages. What is the most likely cause?

Accepted Answer

The training data is predominantly English. If the training data lacks Spanish examples, the model cannot learn Spanish patterns.

Answer

The model is overfitted to English

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The inference threshold is too high

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The model uses the wrong algorithm

Question 18

Which Einstein feature provides AI-powered call recording analysis that tracks keywords, talk-time metrics, and next steps?

Accepted Answer

Einstein Conversation Insights. Einstein Conversation Insights analyzes call recordings and provides metrics and action items.

Answer

Einstein Email Insights

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Einstein Activity Capture

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Einstein Voice

Question 19

A company wants to use customer purchase history to train a recommendation model. Which action is essential to comply with data privacy regulations?

Accepted Answer

Obtain explicit consent from customers.. Data privacy regulations such as GDPR and CCPA require a lawful basis for processing personal data, and explicit consent is a primary lawful basis when using customer purchase history for training a recommendation model. Without obtaining explicit consent, the company would be processing personal data without a valid legal ground, violating regulations that mandate transparency and user control over their data.

Answer

Use only publicly available data.

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Ignore regulations because data is internal.

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Anonymize the data after training.

Question 20

A company is designing an AI system to screen job applicants. To ensure fairness, which practice should be implemented?

Accepted Answer

Conduct regular fairness audits on model outcomes. Regular fairness audits are essential because they systematically evaluate model outcomes for bias across demographic groups, using metrics like disparate impact or equal opportunity difference. This practice aligns with responsible AI frameworks (e.g., NIST AI Risk Management Framework) and helps detect subtle biases that may emerge from proxy variables or data drift, ensuring the screening process remains equitable over time.

Answer

Use only one data source for consistency

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Maximize the model's accuracy on historical hiring decisions

Answer

Remove all demographic data from the training set