AI Associate Practice Test 37 — 15 Questions

Question 1

A data scientist notices that an Einstein Discovery model predicts a low probability of conversion for all leads in a new campaign, even though the campaign targets high-value accounts. Which initial diagnostic step should be taken?

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

Accepted Answer

Compare the feature distributions of the training and campaign data. The most likely cause of a model predicting low conversion for all leads in a new campaign is a shift in feature distributions between the training data and the campaign data (covariate shift). Checking these distributions is the standard initial diagnostic step to identify if the model is encountering data it was not trained on, which would invalidate its predictions. This aligns with best practices for model monitoring and data validation in Einstein Discovery.

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Retrain the model with the new campaign data included

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Check the Einstein model recipe for incorrect filters

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Increase the prediction confidence threshold

Question 2

A healthcare organization is using Einstein Bots to schedule patient appointments. They are subject to HIPAA regulations. What is the most important configuration they must apply to the Einstein Trust Layer?

Accepted Answer

Enable PII masking and configure it to identify and mask health-related data fields.. HIPAA requires protection of Protected Health Information (PHI). PII masking in the Einstein Trust Layer ensures that PHI is not exposed or retained in logs or training.

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Set up an audit trail for all bot interactions.

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Enable toxicity detection to ensure the bot does not use offensive language.

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Disable data retention to ensure no customer data is stored.

Question 3

A data scientist is evaluating a custom Einstein model for a lead scoring use case. The model's precision is 0.9, recall is 0.5. What is the most important improvement priority?

Accepted Answer

Increase recall to reduce false negatives. With a precision of 0.9 and recall of 0.5, the model is highly selective but misses many actual leads (high false negatives). In lead scoring, false negatives mean lost sales opportunities, which is typically more costly than false positives. Therefore, increasing recall to capture more true positives is the most important improvement priority.

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Increase precision to reduce false positives

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Optimize for an F1 score of 0.7

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Improve overall accuracy above 80%

Question 4

When a Salesforce admin enables 'Score Factors' for an AI prediction, what does this provide to end users?

Accepted Answer

A list of the most influential fields and their contribution to the prediction. When 'Score Factors' is enabled for an AI prediction in Salesforce, end users see a breakdown of the most influential fields that contributed to the prediction, along with their relative contribution (e.g., positive or negative impact). This provides transparency into why a specific prediction was made, helping users trust and act on the AI's output without exposing the underlying model logic.

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The exact formula used by the model

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A confidence interval for the prediction

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A histogram of all prediction values

Question 5

A company needs to predict which support cases are likely to escalate based on historical case data. They have a clear binary outcome (escalated vs not escalated) and want to select features from their case records. Which Einstein tool should they use?

Accepted Answer

Einstein Prediction Builder. Einstein Prediction Builder is the correct tool because it enables users to create custom binary classification models using their own historical data without writing code. The requirement to predict a binary outcome (escalated vs not escalated) and select features from case records matches Prediction Builder's point-and-click interface for training a model on a custom object or standard object like Case.

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Einstein Next Best Action

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Einstein Discovery

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Einstein Case Classification

Question 6

Which THREE strategies can help mitigate bias in an AI model? (Choose three.)

Accepted Answer

Remove protected attributes from training data. Removing protected attributes (e.g., race, gender) from training data reduces the risk of the model directly learning correlations with these sensitive features. This is a common pre-processing technique to prevent direct discrimination, though it may not eliminate indirect bias if correlated proxy features remain.

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Focus training on majority group data for accuracy

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Randomize a portion of model outputs

Question 7

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 8

A Salesforce admin has enabled Einstein Opportunity Scoring but notices that some opportunities are not being scored. The admin checks the data and finds that the opportunities have all required fields. What could be another reason for missing scores?

Accepted Answer

The org has fewer than 100 opportunities with activities. Einstein Opportunity Scoring requires a minimum of 100 opportunities with activities (e.g., emails, events, tasks) in the last 90 days to generate scores. Even if all required fields are present, the model needs sufficient historical data to calculate a predictive score. Option C correctly identifies this data volume threshold as the likely cause.

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Opportunity team members have not been assigned

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The org is on a Professional Edition

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The opportunities are missing custom fields

Question 9

Which TWO actions help ensure transparency in AI systems according to Salesforce's ethical AI guidelines?

Accepted Answer

Documenting model assumptions and limitations.. Documenting model assumptions and limitations is a core transparency practice under Salesforce's ethical AI guidelines. It ensures stakeholders understand the boundaries and potential biases of the AI system, enabling informed trust and accountability.

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Limiting access to model outputs to only a few people.

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Using complex deep learning models without explanation.

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Automatically retraining models weekly.

Question 10

A marketing manager wants to use Einstein Next Best Action to recommend offers to customers based on their behavior. Which TWO components are used in defining Next Best Action strategies?

Accepted Answer

Apex. (Apex) is correct because Einstein Next Best Action strategies can use Apex classes to define custom logic for offer selection, such as querying external data or applying complex business rules. Option E (Flows) is correct because Flows allow you to orchestrate the decision process, including branching logic and data transformations, to determine which offers to present to a customer based on their behavior.

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Intents and entities

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Prompt templates

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Image classification models

Question 11

Which Einstein feature provides automated statistical analysis of Salesforce data, including story creation and improvement suggestions?

Accepted Answer

Einstein Discovery. Einstein Discovery provides automated statistical analysis, generates stories, waterfall charts, and improvement suggestions.

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Einstein Forecasting

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Einstein Prediction Builder

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Einstein Recommendation Builder

Question 12

During data transformation, a data scientist applies one-hot encoding to a categorical feature with 50 unique values. The resulting dataset has 50 new columns. What is a potential drawback of this transformation?

Accepted Answer

High cardinality leading to sparse data and overfitting. One-hot encoding a categorical feature with 50 unique values creates 50 binary columns, each representing one category. This high cardinality leads to a very sparse matrix (most entries are 0), which can cause the model to overfit by learning noise from rare categories, especially when the dataset is not large enough to support such dimensionality.

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Reduction in training time

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Increased interpretability of the model

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Loss of ordinal information in categories

Question 13

Refer to the exhibit. A data analyst has defined this field mapping for Einstein Prediction Builder. Which data issue would most likely arise from this mapping?

Accepted Answer

The 'Id' field should be excluded as it can cause data leakage and overfitting. Including the 'Id' field in Einstein Prediction Builder can cause data leakage and overfitting. The 'Id' field is a unique identifier that has no predictive value for the target outcome, but the model could learn to memorize specific records based on it, leading to poor generalization on unseen data.

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The 'LeadSource' field should be mapped to 'Text' instead of 'Category' to preserve verbatim values

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The 'Amount' field should be mapped to 'Category' to discretize the values

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The 'CloseDate' field should be mapped to 'Text' to avoid date parsing issues

Question 14

A sales team uses Einstein Lead Scoring and notices that the model gives disproportionately low scores to leads from a certain demographic group. The team suspects historical bias in the training data. Which THREE steps should they take to address this bias?

Accepted Answer

Investigate the historical data to identify and correct labeling errors or sampling bias. To address bias, the team should audit the model for disparate impact, investigate and correct the historical data, and consider adding features that reduce reliance on biased proxies. Removing the model entirely is unnecessary, and manually adjusting scores is not a systemic fix.

Answer

Manually increase scores for the affected group by a fixed percentage

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Remove the model and rely on manual lead scoring

Question 15

A company is implementing Einstein Lead Scoring and wants to ensure transparency for sales reps. According to Salesforce's Trusted AI principles, what should the company communicate to users about the AI-generated scores?

Accepted Answer

The scores are AI-generated, the key factors influencing each score are available, and reps should use their judgment.. The Honesty and Transparency principles require that users are informed that scores are AI-generated, understand the factors influencing them, and are aware of limitations.

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The scores are based on a secret algorithm to prevent gaming the system.

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Only managers can see the score factors to avoid confusion.

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The scores are 100% accurate and should be followed without question.