AI Associate Practice Test 48 — 15 Questions

Question 1

A sales operations manager wants to automatically prioritize leads based on historical conversion data. Which Salesforce Einstein feature should they use to create a custom predictive model without writing code?

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

Accepted Answer

Einstein Prediction Builder. Einstein Prediction Builder (D) is the correct answer because it allows users to create custom predictive models—such as lead conversion propensity—using point-and-click tools, without writing any code. It leverages historical data from the org to train a model that outputs a prediction score for each record, directly meeting the requirement to automatically prioritize leads based on historical conversion data.

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Einstein Discovery

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Einstein Lead Scoring

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Einstein Bots

Question 2

A company needs to build a custom AI model that predicts whether a support case will be escalated based on account history, case description, and product category. The output should be a binary yes/no. Which tool should they use?

Accepted Answer

Einstein Prediction Builder. Einstein Prediction Builder is the correct tool because it is specifically designed for binary classification (yes/no) predictions using structured data from Salesforce objects, such as account history, case description, and product category. It allows admins to create custom predictive models without code, directly within the Salesforce platform, making it ideal for predicting case escalation.

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Einstein Vision and Language Platform

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Einstein Discovery

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Einstein GPT

Question 3

Which THREE of the following are best practices for training an Einstein Bot?

Accepted Answer

Test the bot with sample conversations before deployment. Testing an Einstein Bot with sample conversations before deployment allows you to validate the bot's intent recognition, dialog flow, and response accuracy in a controlled environment. This practice helps identify and fix issues with phrase matching, slot filling, and escalation paths, ensuring the bot performs reliably in production.

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Train with only one intent to avoid confusion

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Use exact match phrases only

Question 4

During the data preparation phase for an AI model, a data engineer discovers that the 'AnnualRevenue' field contains some negative values. What is the best course of action?

Accepted Answer

Investigate the data source to correct the negative values. Negative revenue values typically indicate data entry errors, system bugs, or incorrect data transformations. The best practice in data preparation is to investigate the source system to understand why negative values were generated and correct them at the origin, ensuring data integrity before any imputation or deletion. Simply deleting or imputing without root-cause analysis can introduce bias or mask underlying data quality issues.

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Delete all records with negative revenue

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Replace negative values with the mean of positive values

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Keep negative values as they might represent returns or refunds

Question 5

An admin is setting up Einstein Article Recommendations. Which type of data is essential for the model to learn which articles are relevant?

Accepted Answer

Article view events from users. Einstein Article Recommendations uses a collaborative filtering model that learns article relevance from user interaction signals, specifically article view events. The model analyzes patterns of which articles users view together to identify related content, making view events the essential training data for generating recommendations.

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Article publication dates

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User job titles

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Article author names

Question 6

A marketing manager wants to use Einstein Send Time Optimization. To generate personalized send time recommendations, which data does the model primarily rely on?

Accepted Answer

The individual contact's past email open and click behavior.. Einstein Send Time Optimization (STO) uses a machine learning model that analyzes each individual contact's historical email engagement patterns—specifically their past open and click behavior—to predict the optimal send time unique to that contact. This personalized approach ensures that each recipient receives the email when they are most likely to engage, rather than relying on aggregate or rule-based heuristics.

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The aggregated engagement data of all contacts in the same time zone.

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The industry benchmarks for optimal send times.

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The sender's historical campaign performance by hour.

Question 7

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 8

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 9

A healthcare organization uses Einstein Next Best Action to recommend treatments to patients. They must comply with GDPR's right to explanation for automated decisions. Which combination of Einstein Trust Layer features is MOST essential to meet this requirement?

Accepted Answer

Score factors and grounding. The right to explanation requires that individuals can obtain meaningful information about the logic involved in automated decisions. Score factors provide the most influential fields for a prediction, and grounding connects the AI to relevant CRM data, enabling a transparent explanation. Toxicity detection is not relevant. Audit trail logs actions but does not provide explanation to the patient.

Answer

Audit trail and PII masking

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Toxicity detection and audit trail

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Zero Data Retention and PII masking

Question 10

A healthcare organization uses Einstein Prediction Builder to predict patient no-show rates. They want to ensure that protected health information (PHI) like patient names and social security numbers are not used in the model. Which Salesforce Trusted AI principle or feature directly addresses this requirement?

Accepted Answer

Data minimisation. Data minimisation is a key principle: only use relevant features for the model and avoid including sensitive PII unnecessarily. The Einstein Trust Layer's PII masking also helps, but the principle that directly addresses using only relevant fields is data minimisation.

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Zero data retention

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Explainability

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Human oversight

Question 11

A financial services firm uses Einstein Next Best Action to offer credit products. The model recommends high-interest loans more often to minority groups. The AI Associate must mitigate this. What is the most effective approach?

Accepted Answer

Apply post-processing fairness adjustments to the recommendations.. Post-processing fairness adjustments directly modify the model's output to enforce demographic parity or equal opportunity, reducing biased recommendations without retraining the model. This approach is practical when the firm cannot easily change the underlying training data or model architecture, and it allows the AI Associate to intervene at the decision point to ensure fair lending practices.

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Remove the model and use a rule-based system.

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Use SHAP values to explain predictions.

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Add a disclaimer that recommendations may be biased.

Question 12

An administrator notices that Einstein Lead Scoring is not displaying scores for some leads. The leads have the required fields populated. What is the most likely cause?

Accepted Answer

Insufficient historical lead conversion data to train the model. Einstein Lead Scoring requires a minimum amount of historical lead conversion data to train its predictive model. If there is insufficient data, the model cannot generate scores, even if all required fields are populated. This is the most likely cause because the model relies on pattern recognition from past conversions, not just field completeness.

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The lead record type is not included in the scoring model

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The lead score field is not added to the page layout

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The user does not have the 'View Einstein Lead Scoring' permission

Question 13

Refer to the exhibit. The prediction API returns a probability of 0.85 for the label 'High Value'. What does this value represent?

Accepted Answer

The confidence score that this lead is 'High Value'. The prediction API returns a probability of 0.85 for the label 'High Value'. In machine learning classification, this output represents the model's confidence score—the estimated probability that the input instance belongs to the specified class. It is not a direct measure of conversion likelihood, model accuracy, or F1 score; it is the raw posterior probability assigned by the model to the 'High Value' label.

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The likelihood that this lead will convert

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The F1 score of the model for this prediction

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The model's accuracy on the training set

Question 14

An AI model predicts employee performance. The HR team uses it to identify high-potential employees. What is a potential ethical risk?

Accepted Answer

All of the above. All three listed risks—over-reliance on the model, privacy violation, and underutilization of human judgment—are potential ethical risks when an AI model predicts employee performance. Over-reliance can lead to automated decisions without human oversight, privacy violation may occur if sensitive employee data is mishandled, and underutilization of human judgment ignores contextual factors that the model cannot capture. Together, these represent a comprehensive set of ethical concerns in AI-driven HR practices.

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Over-reliance on the model

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Privacy violation

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Underutilization of human judgment

Question 15

A company uses Einstein Bots in Service Cloud. They want the bot to understand when a customer types 'I want to return a product' and route them appropriately. What must the admin configure first?

Accepted Answer

Define an intent named 'Return Product' with sample phrases. Einstein Bots use Natural Language Understanding (NLU) to interpret customer intents. Defining an intent named 'Return Product' with sample phrases trains the bot to recognize variations of that request and route the conversation accordingly. Without an intent, the bot cannot understand the customer's goal.

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Create a dialogue flow with a menu of options

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Enable Einstein Bots API for external integration

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Configure a handoff rule to a human agent for all queries