AI Associate Practice Test 39 — 15 Questions

Question 1

An admin is configuring Einstein Bots for a service center. They want the bot to understand when a customer says 'I want to return my order' and route to a return flow. What must the admin create to enable this understanding?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

A new intent with training phrases such as 'I want to return my order'. In Einstein Bots, intents represent the customer's goal (e.g., 'Return Order'), and entities capture details (e.g., order number). The admin must create an intent with training phrases like 'I want to return my order' so the NLP model recognizes it. Actions are separate from intent definition.

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A new action in Bot Builder

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A new dialogue flow linked to a 'Return Order' topic

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A new entity for 'return order'

Question 2

A company uses an NLP model to detect customer intent from chat messages. The model correctly identifies 'billing question' 90% of the time for actual billing questions, but also flags many non-billing messages as billing (false positives). Which metric should the team prioritize to reduce false alarms?

Accepted Answer

Precision. Precision is the metric that measures the proportion of positive identifications that are actually correct — i.e., of all messages flagged as 'billing question', how many truly are billing questions. Since the problem is false positives (non-billing messages incorrectly flagged as billing), precision directly captures this error type. High precision means fewer false alarms, which is exactly what the team wants to prioritize.

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Accuracy

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F1 score

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Recall

Question 3

A company is preparing data for Einstein Article Recommendation. Which data source is most appropriate for training the model?

Accepted Answer

Historical article view and click data.. Einstein Article Recommendation uses supervised machine learning to predict which articles users are likely to find relevant. The model must be trained on historical user engagement signals—specifically article view and click data—to learn patterns of relevance. Without this behavioral data, the model cannot establish a correlation between user actions and article content.

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Org metadata.

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System debug logs.

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User profile data only.

Question 4

A company uses Einstein Conversation Insights to analyze sales calls. They want to identify when a competitor is mentioned and automatically log that mention to the opportunity. Which feature should they configure?

Accepted Answer

Keyword Tracking in Conversation Insights. Keyword Tracking in Einstein Conversation Insights allows users to define keywords (such as competitor names) and automatically track when they are mentioned in sales calls. This feature can then surface those mentions and, with configuration, log them to the opportunity record. It is specifically designed for this use case.

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Einstein Next Best Action

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Einstein Email Insights

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Einstein Activity Capture

Question 5

A service manager wants to use Einstein Case Classification to automatically categorize incoming cases. What is a prerequisite for training the model?

Accepted Answer

At least 500 closed cases with the correct category field populated.. Einstein Case Classification uses supervised machine learning to automatically categorize cases. The model learns from historical data, so it requires a sufficient number of closed cases (at least 500) with the correct category field populated to train effectively. Open cases are not used because the model needs confirmed outcomes to learn from.

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A minimum of 100 open cases with categories assigned.

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The cases must have been created within the last 30 days.

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Users must enable Einstein Case Classification in their personal settings.

Question 6

A social media platform uses an AI model to automatically detect and remove hate speech. The model uses natural language processing and was trained on public posts. Recently, an internal audit reveals that the model removes posts from minority ethnic groups at a rate 3 times higher than from majority groups, even when the content is similar. The model achieves high precision and recall on the test set. The platform's content moderation team is overwhelmed with appeals. The company wants to maintain a safe environment while being fair. Which approach best addresses both goals?

Accepted Answer

Conduct an audit of the training data to identify gaps, then retrain with more representative data including diverse examples of hate speech and non-hate speech.. The disparity stems from the training data: if the model was trained on public posts that under-represent minority dialects, slang, or reclaimed language, it will misclassify similar content differently across groups. Auditing the training data for representation gaps and retraining with balanced, diverse examples of both hate speech and non-hate speech directly addresses the root cause while preserving the AI moderation capability. This is the standard responsible-AI remediation for biased models.

Answer

Disable the AI moderation and rely solely on user reports.

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Add more human moderators to review all flagged content from minority groups.

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Adjust the detection threshold only for minority group posts to reduce flags.

Question 7

A company uses Einstein Recommendation Builder in Experience Cloud to suggest products. They notice that users who frequently purchase from one category are not getting relevant recommendations. What is the most likely cause?

Accepted Answer

There is insufficient interaction data for the product category. Einstein Recommendation Builder relies on historical interaction data (such as purchases, views, etc.) to generate personalized recommendations. If users who frequently purchase from one category are not getting relevant recommendations, it is likely because there is insufficient interaction data for that category to train the model effectively. Without enough data, the model cannot learn patterns to make accurate suggestions.

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The recommendation field is not added to the page layout

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Einstein Vision is required for product image analysis

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The recommendation model was trained on too many records, causing overfitting

Question 8

A company plans to use Einstein Discovery to analyze sales data. Which data preparation step is essential for time-series forecasting?

Accepted Answer

Ensure date fields are properly formatted and contain sufficient historical range. For time-series forecasting in Einstein Discovery, the date field must be properly formatted (e.g., as a date or datetime data type) and contain a sufficient historical range to identify patterns like seasonality and trends. Without adequate historical data, the model cannot learn temporal dependencies, making this step essential.

Answer

Remove all outliers in sales amounts

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Remove duplicate records

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Scale all numeric fields to a 0-1 range

Question 9

An admin wants to use Einstein Reply Recommendations in Service Cloud. Which ethical consideration is most important to implement before enabling the feature?

Accepted Answer

Ensure customer data is anonymized.. Customer data privacy is paramount; anonymizing data ensures recommendations do not expose sensitive information.

Answer

Reduce the cost of agent training.

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Maximize the number of recommendations.

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Increase the speed of case resolution.

Question 10

A nonprofit uses an AI system to allocate resources to communities in need. The system uses historical data which shows that certain neighborhoods have lower service usage. What ethical risk should be considered?

Accepted Answer

The system may perpetuate historical inequities. The AI system uses historical data that reflects lower service usage in certain neighborhoods. If that historical data is biased due to past inequities (e.g., redlining, underinvestment, or systemic discrimination), the model will learn and amplify those patterns, leading to unfair resource allocation that perpetuates historical disadvantages. This is a classic case of algorithmic bias where the training data encodes societal biases, and the model's predictions reinforce them.

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The system may violate data minimization principles

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The system cannot be held accountable for decisions

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The system lacks explainability

Question 11

An admin is setting up Einstein in a new Salesforce org. They need to automatically log emails from Gmail and analyze sales call recordings. Which TWO features should they enable? (Choose 2)

Accepted Answer

Einstein Conversation Insights. Einstein Conversation Insights (A) is correct because it analyzes sales call recordings and transcripts, surfacing talk-time metrics, keywords, and competitor mentions directly on records. Einstein Activity Capture (C) is correct because it automatically syncs and logs emails and calendar events from Gmail (and Outlook) into Salesforce without manual data entry. Together they satisfy both requirements: email logging from Gmail and call-recording analysis. Einstein Lead Scoring (B) only predicts lead conversion likelihood and does not log emails or analyze calls. Einstein Case Classification (D) predicts case fields for service, not sales email or call data. Einstein Email Insights (E) generates recommended email content and is not the mechanism for automatic Gmail logging.

Answer

Einstein Lead Scoring

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Einstein Case Classification

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Einstein Email Insights

Question 12

Based on the exhibit, what is the primary issue with this Einstein Bot conversation?

Accepted Answer

The bot lacks alternative ways to identify the customer.. The exhibit shows the bot repeatedly asking for the customer's account number without offering alternative identification methods (e.g., email, phone number, or name). This is the primary issue because Einstein Bot's conversational design should include fallback paths to handle cases where the user cannot provide the requested information, ensuring a smooth user experience and reducing drop-offs.

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The bot is confused about the user's intent.

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The bot is repeating itself excessively.

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The bot does not understand the initial intent.

Question 13

An insurance company uses an AI model to set auto insurance premiums. The model uses factors including driving history, age, and ZIP code. A regulator finds that premiums in certain low-income neighborhoods are significantly higher than in affluent neighborhoods with similar risk profiles. The company's actuaries argue that the model is actuarially sound because it accurately predicts claims based on historical data. The company wants to comply with ethical guidelines and avoid legal action. Which action should they take?

Accepted Answer

Incorporate a fairness constraint that requires similar premiums for similar risk profiles regardless of ZIP code.. The model produces disparate impact by using ZIP code as a proxy for race or socioeconomic status, resulting in higher premiums for similar-risk individuals in low-income neighborhoods. Incorporating a fairness constraint that requires similar premiums for similar risk profiles regardless of ZIP code directly addresses the discriminatory outcome while preserving actuarial risk differentiation. This aligns with ethical AI principles and reduces legal exposure under fair-lending and insurance regulations.

Answer

Defend the model based on its actuarial accuracy and historical claims data.

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Cap premium increases in low-income neighborhoods at a fixed percentage.

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Remove ZIP code from the model inputs entirely.

Question 14

An AI Associate is asked to build a model that predicts employee performance. The dataset includes gender, department, and tenure. Which practice could introduce ethical risk?

Accepted Answer

Including gender to improve model accuracy.. Including gender as a feature in a predictive model for employee performance can introduce bias and lead to unfair or discriminatory outcomes. Even if the model's accuracy improves, using protected attributes like gender may violate ethical guidelines and regulations such as GDPR or anti-discrimination laws, as it could perpetuate historical biases or result in disparate impact.

Answer

Evaluating model performance across different groups.

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Excluding gender from the model features.

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Documenting model limitations and assumptions.

Question 15

A data scientist is training a model to predict churn. The model achieves 99% accuracy on training data but only 60% on test data. Which issue is most likely occurring?

Accepted Answer

Overfitting. A large gap between training accuracy (99%) and test accuracy (60%) is the classic signature of overfitting: the model has memorized training data, including noise, and fails to generalize. This typically results from excessive model complexity, too little training data, or insufficient regularization.

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Concept drift

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Data leakage

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Underfitting