AI Associate Practice Test 32 — 15 Questions

Question 1

A Salesforce admin is troubleshooting Einstein Object Detection in a custom object. The model is predicting values, but the confidence score remains below 80% for most predictions. What should the admin investigate first?

Accepted Answer

The model training data size and distribution of values.. Low confidence scores in Einstein Object Detection typically indicate that the model has insufficient or imbalanced training data. Einstein AI requires a minimum number of labeled records (e.g., at least 100 per class) and a balanced distribution across predicted values to learn effectively. Without adequate data size and diversity, the model cannot generalize well, resulting in confidence scores below 80%.

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The number of fields used as input features.

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The API version used in the integration.

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The permission set for the Einstein AI feature.

Question 2

Refer to the exhibit. A Salesforce admin evaluates an Einstein Prediction Builder model for customer churn. What should be the admin's primary concern based on the exhibit?

Accepted Answer

The model has detected data drift, indicating the training data may no longer represent current patterns.. The exhibit shows a data drift alert from Einstein Prediction Builder, which indicates that the statistical properties of the input data have changed over time. This is the admin's primary concern because a model trained on outdated patterns will produce unreliable predictions, even if its accuracy or AUC were initially high. Data drift directly undermines the model's validity in production.

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The model accuracy is too low for production use.

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The AUC is low, so the model is not better than random.

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The model uses too few features to be reliable.

Question 3

A retail company implements an AI chatbot to recommend products. After launch, they notice the chatbot frequently suggests expensive items to budget-conscious customers. Which AI bias is most likely occurring?

Accepted Answer

Sample bias (biased training data). The chatbot's frequent suggestion of expensive items to budget-conscious customers indicates that the training data was biased toward high-cost products, leading the model to learn and replicate that preference. This is a classic case of sample bias (biased training data), where the dataset does not accurately represent the target user population, causing systematic errors in the model's recommendations.

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Confirmation bias

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Anchoring bias

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Overconfidence bias

Question 4

An admin creates a predictive model in Einstein Prediction Builder to forecast customer churn. The model shows high accuracy on test data but poor performance in production. What is the most likely cause?

Accepted Answer

Data drift between training and production environments. Option D is correct because the model's high accuracy on test data but poor performance in production is a classic symptom of data drift. In Einstein Prediction Builder, the model was trained on historical data that may not reflect current customer behavior patterns, leading to a mismatch between training and production distributions. This is not a model training issue but a data environment shift.

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Improper feature scaling in the training pipeline

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The model is overfitted to the training data

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Target leakage in the training dataset

Question 5

A company wants to deploy an Einstein AI model that uses sensitive customer data. Which practice should they follow to comply with data privacy regulations?

Accepted Answer

Use Einstein Trust Layer features to mask personally identifiable information (PII) in the model.. Option D is correct because the Einstein Trust Layer provides built-in capabilities to automatically mask or redact personally identifiable information (PII) before data is sent to the underlying AI model, ensuring compliance with data privacy regulations like GDPR and CCPA without requiring manual data handling. This feature operates at the platform level, intercepting data in transit and applying masking rules based on predefined patterns, so sensitive customer data is never exposed to the model or stored in its training logs.

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Store all sensitive data in an external data lake and connect via APIs.

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Obtain explicit consent from data subjects before using their data in AI models.

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Limit the data used for training to only essential fields.

Question 6

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 7

Which THREE capabilities are provided by Einstein GPT in Sales and Service?

Accepted Answer

Summarizing case conversations from service interactions.. Option B is correct because Einstein GPT leverages generative AI to automatically summarize case conversations from service interactions, enabling agents to quickly grasp key details without reading entire transcripts. This capability is built on Salesforce's proprietary AI models that process natural language from service records to produce concise, actionable summaries.

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Generating report charts and dashboards.

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Creating workflow rules based on user behavior.

Question 8

A company is implementing Einstein Activity Capture. Users have enabled the feature, but emails are not being automatically logged. Which configuration should the administrator verify first?

Accepted Answer

Confirm that the Einstein Activity Capture permission set is assigned to users.. Option C is correct because Einstein Activity Capture requires the dedicated permission set to be assigned to users before it can automatically log emails and events. Without this permission set, the feature is enabled at the org level but users lack the necessary access rights to capture activities, so verifying this assignment is the first logical step.

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Check that the users' email clients are supported.

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Verify that Email-to-Salesforce is enabled.

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Ensure users have the 'Log a Task' permission.

Question 9

Which TWO of the following are ethical considerations when deploying AI in Salesforce?

Accepted Answer

Providing transparency in AI-generated outcomes. Option B is correct because transparency in AI-generated outcomes is a core ethical principle, especially in Salesforce's Einstein platform, where users must understand how predictions (e.g., lead scoring or opportunity insights) are made. Salesforce provides tools like 'Why This Prediction?' to explain model outputs, ensuring trust and accountability. Without transparency, users cannot validate or challenge AI decisions, leading to potential bias or misuse.

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Maximizing model complexity for better accuracy

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Using only historical data without review for biases

Question 10

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?

Accepted Answer

Compare the feature distributions of the training and campaign data. Option C is correct because 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 11

In Salesforce CRM Analytics (formerly Einstein Analytics), what is the primary purpose of a dataset?

Accepted Answer

To prepare data for AI and analytics. In Salesforce CRM Analytics, a dataset is the foundational data structure that transforms raw data into an optimized, columnar format for analytics and AI features like Einstein Discovery. It is created by extracting, cleaning, and aggregating data from sources such as Salesforce objects or external connectors, enabling efficient querying, dashboarding, and machine learning model training. This makes option A correct because the primary purpose is to prepare data specifically for AI and analytics workloads.

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To run SQL queries directly

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To store raw, unprocessed records

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To create dashboards only

Question 12

A marketing director wants to use Einstein Engagement Scoring to prioritize leads. She has enabled Einstein and assigned the permission set to users. However, the Engagement Score field is not visible on any lead record. The admin checked the field-level security and it is visible to all profiles. What should the admin do next?

Accepted Answer

Add the Engagement Score field to the lead page layout.. The Engagement Score field is a standard field that must be added to the lead page layout to be visible on the record. Even though field-level security grants access, the field will not appear on the record detail page unless it is explicitly placed on the page layout. This is a common layout-level visibility requirement in Salesforce.

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Verify that there are at least 500 leads with activity in the last 30 days.

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Run the 'Calculate Einstein Engagement Scores' scheduled job.

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Wait 24 hours for the model to train.

Question 13

A company notices that Einstein Prediction Builder predictions for 'Churn' are less accurate than expected. Which action should the administrator take first to improve model performance?

Accepted Answer

Review the training data for missing values and ensure relevant fields are included in the model.. Option B is correct because the first step in improving Einstein Prediction Builder model performance is to review the training data for missing values and ensure relevant fields are included. Missing values or irrelevant fields can introduce noise and bias, directly degrading predictive accuracy. Einstein Prediction Builder relies on high-quality, complete training data to learn meaningful patterns, so data quality issues must be addressed before any other tuning steps.

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Enable field history tracking on all object fields used in the prediction.

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Change the prediction outcome to a different field to see if accuracy improves.

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Retrain the model with the same data but increase the number of training iterations.

Question 14

A company is preparing data for Einstein Prediction Builder to forecast lead conversion. They have historical data with fields like Lead Source, Industry, Number of Employees, and Converted (boolean). Which data preparation step is most critical?

Accepted Answer

Ensure data completeness by handling missing values in Lead Source. Handling missing values in Lead Source is critical because Einstein Prediction Builder requires complete, high-quality data to train accurate predictive models. Missing categorical fields like Lead Source can introduce bias or cause the model to ignore important patterns in lead conversion. Ensuring data completeness through imputation or removal of incomplete records is a standard data preparation step for AI/ML in Salesforce.

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Mix data from all lead sources without normalization

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Use only the last 3 months of data for training

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Remove all records with outliers in Number of Employees

Question 15

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.. Option A is correct because 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.

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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.