AI Associate Practice Test 27 — 15 Questions

Question 1

A sales manager wants to automatically prioritize leads based on their likelihood to convert. Which Einstein feature should the admin enable?

Accepted Answer

Einstein Lead Scoring. Einstein Lead Scoring is the purpose-built Salesforce feature that uses AI to score leads based on historical conversion patterns, surfacing a score and key positive/negative factors directly on the lead record. Enabling it lets the sales manager prioritize leads by likelihood to convert without building a custom model.

Answer

Einstein Activity Capture

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Einstein Prediction Builder

Answer

Einstein Bot

Question 2

An admin is configuring Einstein Opportunity Scoring and notices that the score is not appearing on the Opportunity record page. They have enabled the feature and assigned the permission set. What else is required for the score to display?

Accepted Answer

The Einstein Opportunity Score field must be added to the page layout.. The Einstein Opportunity Score is a custom field that must be manually added to the Opportunity page layout to appear on the record. Enabling the feature and assigning the permission set only activates the backend scoring engine and grants access; without the field on the layout, the score cannot render on the record page.

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The org must have at least 100 closed opportunities in the last 12 months.

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The user must have the 'View Einstein Scores' permission in their profile.

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The opportunity must have a closed date within the next 30 days.

Question 3

Which THREE are valid considerations when deploying an Einstein Bot?

Accepted Answer

Defining the intents the bot should handle.. Option A is correct because an Einstein Bot must have its intents defined so the bot can recognize and route user utterances to the appropriate dialog and action; intents are the core NLP mapping that determines what the bot can handle. Option B is correct because Salesforce best practice is to build and test Einstein Bots in a sandbox (or a developer sandbox with the bot deployed) before activating in production, validating dialogs, actions, and channel behavior without affecting live users. Option D is correct because training the bot with sample user dialogs (utterances) improves the intent model's accuracy, and Einstein Bots require training utterances per intent to classify user input reliably. Option C is not a valid consideration because Einstein Bots can be deployed across multiple channels (e.g., Experience Cloud sites, Messaging, and enhanced channels), not limited to a single communication channel. Option E is not valid because Einstein Bots can escalate to a human agent through standard Omni-Channel routing and live agent transfer, not only via email.

Answer

Limiting the bot to a single communication channel.

Answer

Ensuring the bot can escalate to a human agent only via email.

Question 4

A company wants to use Einstein Prediction Builder to predict whether a support case will escalate (binary: escalate or not). They have historical case data in Salesforce. Which THREE steps are required to set up this prediction?

Accepted Answer

Select the dataset (records to train on). In Einstein Prediction Builder, setting up a prediction requires defining the outcome and the data used to learn it. Option B is correct because you must select the dataset — the Salesforce object and the set of historical records (e.g., Case records) that the model will train on. Option C is correct because you must choose the features (input fields) from that object that the model will use as predictors, such as case priority, origin, or account attributes. Option E is correct because you must select the prediction field, i.e., the field containing the binary outcome (the escalation flag) that the model will learn to predict. Option A does not belong because an Einstein Bot is a separate conversational service for handling interactions, not a required step in building a Prediction Builder model. Option D does not belong because dashboards are an optional monitoring/reporting aid, not a required configuration step to create the prediction.

Answer

Build an Einstein Bot for escalation handling

Answer

Create a dashboard to monitor model performance

Question 5

A company uses Einstein Bots to handle sales inquiries. The bot sometimes provides incorrect product information, leading to customer dissatisfaction. What is the MOST ethical course of action?

Accepted Answer

Add a disclaimer that the bot may make mistakes and escalate complex issues. Adding a disclaimer that the bot may make mistakes and escalating complex issues to humans is the most ethical approach: it maintains transparency about the AI's limitations, sets accurate customer expectations, and provides a safe path to human help for cases the bot cannot handle reliably. This aligns with ethical AI principles of honesty, accountability, and customer protection rather than hiding flaws or shifting blame. Option B is not the most ethical course because replacing the bot entirely is an overreaction that discards a useful tool instead of mitigating its risks. Option C is unethical because concealing known errors from customers is deceptive and could cause harm. Option D is also unethical because publicly blaming developers is scapegoating rather than taking responsibility and fixing the problem.

Answer

Replace the bot with human agents entirely

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Keep the bot but do not inform customers of errors

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Blame the bot developers publicly

Question 6

Which feature uses automated statistical analysis to identify key drivers in your data and generate natural language stories about the insights?

Accepted Answer

Einstein Discovery. Einstein Discovery is the correct answer because it is the Salesforce feature specifically designed to perform automated statistical analysis (using regression, classification, and clustering algorithms) on your data to identify key drivers and patterns, then automatically generate natural language narratives that explain those insights in plain English. This combines machine learning with automated insight communication, which directly matches the question's description.

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Einstein Prediction Builder

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Einstein Conversation Insights

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Einstein Next Best Action

Question 7

A customer service director wants to implement an AI-powered chat assistant that can answer common questions. To align with Salesforce's Trusted AI principle of Empathy, which THREE design choices should the director make?

Accepted Answer

Ensure the bot can detect user frustration and escalate to a human agent. Option B is correct because Salesforce's Empathy principle requires AI to recognize human emotion and know when to hand off — detecting frustration and escalating to a human agent keeps the customer's emotional state and needs at the center of the interaction. Option C is correct because giving users an easy opt-out to a human preserves their agency and respects their preference, which is a core expression of empathy rather than forcing them through an automated flow. Option D is correct because a friendly tone and acknowledging the user's feelings when the bot cannot answer demonstrates genuine consideration for the customer's experience instead of a cold dead-end. Option A is not appropriate because automating 100% of interactions removes the human escalation path that empathy demands. Option E is not appropriate because always answering without clarifying questions ignores the user's actual intent and can produce unhelpful or frustrating responses.

Answer

Automate 100% of interactions to provide instant responses

Answer

Design the bot to always provide a direct answer without asking clarifying questions

Question 8

A data scientist needs to prepare data for Einstein Discovery. The dataset includes a field 'Customer_Status__c' with values 'Active', 'Inactive', and 'Churned'. How should this field be treated?

Accepted Answer

Keep as a text field and let Einstein Discovery handle it as a categorical predictor.. Einstein Discovery natively supports text fields as categorical predictors, automatically encoding them for model training. The platform handles string values like 'Active', 'Inactive', and 'Churned' without requiring manual transformation, preserving the semantic meaning and cardinality of the data.

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Create separate boolean fields for each value to improve model accuracy.

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Remove the field because text fields cannot be used in Einstein Discovery.

Answer

Convert to numeric values 1, 2, 3 to preserve order.

Question 9

A company is building a text classification model for customer support tickets. They have a dataset of 10,000 tickets. The team decides to use active learning for labeling. Which approach best aligns with active learning principles?

Accepted Answer

Train a preliminary model and prioritize labeling tickets with low prediction confidence.. Active learning iteratively selects the most informative unlabeled data points for labeling, typically those with low prediction confidence from a preliminary model. This minimizes labeling effort while maximizing model performance, which is the core principle of active learning.

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Randomly select 2,000 tickets and label them manually.

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Use a pre-trained model to label all tickets automatically.

Answer

Have subject matter experts label all 10,000 tickets.

Question 10

A data scientist trains a model to predict customer churn. The model achieves 98% accuracy on training data but only 72% on test data. What issue is most likely occurring?

Accepted Answer

Overfitting. A large gap between training performance (98%) and test performance (72%) is the classic signature of overfitting: the model memorized training noise and patterns that do not generalize. The model has high variance, fitting the training set too closely while failing on unseen data. Remedies include regularization, dropout, pruning, more training data, or simpler model architectures.

Answer

Data leakage

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Underfitting

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Bias in the training data

Question 11

Based on the exhibit, what does the accuracy of 0.85 indicate?

Accepted Answer

85% of the model's predictions matched the actual outcomes.. Accuracy is defined as the ratio of correctly predicted instances (both true positives and true negatives) to the total number of predictions. An accuracy of 0.85 means that 85% of the model's predictions (whether 'won' or 'lost') matched the actual outcomes in the dataset. This is a standard classification metric that evaluates overall correctness, not just one class.

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85% of the features are important for prediction.

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85% of predictions that the opportunity will be won are correct.

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85% of opportunities in the training data were won.

Question 12

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 uses Data Cloud unified profile data as its primary data source to build segments. This allows marketers to leverage unified customer profiles for precise targeting.

Answer

Standard report snapshots.

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External web analytics.

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Einstein Activity Capture data.

Question 13

A company wants to predict which leads are most likely to convert. They have historical lead data with a 'Converted' field (True/False). Which Einstein feature should they use to build a custom prediction model from this data?

Accepted Answer

Einstein Prediction Builder. Einstein Prediction Builder is the correct choice because it allows users to build custom prediction models using their own historical data, including a binary outcome field like 'Converted'. It is designed for non-data scientists to create models without code, directly from standard or custom objects.

Answer

Einstein Lead Scoring

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Einstein Discovery

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Einstein Opportunity Scoring

Question 14

A service team uses Einstein Case Classification to auto-classify incoming cases. They notice that most cases are being classified as 'Low' priority regardless of the actual urgency. What is the most likely cause?

Accepted Answer

The historical case data used for training has a severe class imbalance with very few high-priority cases.. D is correct because a severe class imbalance in the historical training data—where high-priority cases are rare—causes the Einstein Case Classification model to bias predictions toward the majority class ('Low'). The model learns that most cases in the training set are low priority, so it classifies new cases as 'Low' even when the actual urgency is higher, as the algorithm optimizes for overall accuracy rather than per-class performance.

Answer

The model is overfitting to the training data.

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The model has not been retrained in over 90 days.

Answer

The field mapping for priority is incorrect in the classification configuration.

Question 15

A retail company uses Einstein Article Recommendations to suggest knowledge articles to customer service agents. To ensure compliance with the Salesforce Trusted AI principle of Safety, which THREE measures should the company implement?

Accepted Answer

Use grounding to connect the AI recommendations to the company's CRM data. Option B is correct because grounding ties Einstein Article Recommendations to the company's actual CRM data, ensuring suggestions are based on verified, relevant records rather than ungrounded model output, which directly supports the Trusted AI Safety principle by reducing hallucination and misinformation. Option C is correct because enabling toxicity detection in the Einstein Trust Layer filters inappropriate or harmful content before it reaches agents or customers, a core safety control for generative AI outputs. Option E is correct because requiring human agents to review and approve recommendations before sharing with customers enforces human-in-the-loop oversight, catching unsafe or inaccurate suggestions before they cause harm. Option A is incorrect because logging recommendations without any review process provides no safety oversight or corrective action. Option D is incorrect because hiding confidence scores reduces transparency and can increase unsafe over-reliance rather than mitigate it.

Answer

Log all recommendations without setting up any review process

Answer

Hide the model's confidence score from agents to avoid over-reliance