AI Associate Practice Test 36 — 15 Questions

Question 1

A financial services company deploys an AI system to approve small business loans. The system uses a deep neural network trained on historical loan data. After deployment, an internal audit reveals that the approval rate for minority-owned businesses is 15% lower than for non-minority-owned businesses with similar financial profiles. The company's AI Ethics policy requires that AI systems be fair and transparent. The data science team has access to the training data, model architecture, and feature importance scores. The company wants to understand why the disparity exists and take corrective action. Which approach should the team take first?

Accepted Answer

Analyze the training data to determine if there is sampling bias or labeling bias that caused the model to associate minority ownership with higher risk.. Option A is correct because the first step in diagnosing an AI fairness issue is to audit the training data for biases such as sampling bias (e.g., underrepresentation of minority-owned businesses) or labeling bias (e.g., historical loan officers unfairly labeling minority applicants as higher risk). Since the team has access to the training data, analyzing it directly addresses the root cause of the disparity before making model-level changes. This aligns with the AI Ethics policy requirement for transparency, as data bias is a common source of unfair outcomes in deep neural networks trained on historical data.

Answer

Apply a disparate impact analysis to quantify the adverse impact and then adjust the decision threshold.

Answer

Examine the model's weights and activations to identify which features contribute to the disparity.

Answer

Retrain the model with a fairness constraint that penalizes disparities in approval rates.

Question 2

You are a Salesforce AI Specialist at a mid-sized manufacturing company. The company uses Einstein Lead Scoring to prioritize leads. The model was trained on historical lead data and has been in production for three months. Recently, the sales team reports that high-scoring leads are not converting as expected. You investigate and find that the model's data source includes leads from the past 18 months. However, six months ago, the company changed its lead qualification process: they started requiring a demo before scoring leads as 'qualified.' As a result, the definition of a converted lead changed. What is the best course of action to improve model performance?

Accepted Answer

Retrain the model using only leads from the last six months after the process change. Option B is correct because the change in lead qualification process six months ago introduced a data distribution shift (concept drift), making older leads no longer representative of the current conversion behavior. Retraining the model on only the last six months of data aligns the training set with the new definition of a 'converted lead,' allowing Einstein Lead Scoring to learn the updated patterns and improve prediction accuracy.

Answer

Manually adjust the model's prediction threshold to account for the new process

Answer

Remove the 'Demo Scheduled' field from the model to avoid bias

Answer

Add more historical leads from before the process change to increase data volume

Question 3

You are a Salesforce admin at a nonprofit organization. The organization uses Einstein Engagement Scoring to prioritize donors for outreach. The model is based on donation history and event attendance. Recently, the model stopped generating new scores for recently added donors. You check the data source and see that the model's data includes the 'Contact' and 'Opportunity' objects. The data refresh is scheduled daily. The model status is 'Active'. What should you investigate first to resolve the issue?

Accepted Answer

Check if the model has reached its scoring capacity and needs retraining. Option A is correct because Einstein Engagement Scoring models have a maximum scoring capacity (e.g., 2 million scored records per model). When new donors are added but the model stops generating scores, the most likely cause is that the model has reached this capacity and requires retraining to incorporate new records. Retraining resets the scoring queue and allows the model to score newly added donors.

Answer

Add the 'Lead' object to the data source

Answer

Increase the data refresh frequency to hourly

Answer

Check if the model was deactivated automatically

Question 4

A financial services company uses Salesforce Service Cloud with Einstein Bots to handle account balance inquiries. The bot currently uses a standard intent 'CheckBalance' which recognizes phrases like 'What is my balance?' and 'Show my account balance.' The company wants to expand the bot to also answer questions about recent transactions, such as 'What were my last five deposits?' and 'Show my recent withdrawals.' The system administrator has added a new intent called 'RecentTransactions' and mapped it to a new flow. However, during testing, the bot often misclassifies 'CheckBalance' requests as 'RecentTransactions' when the user mentions a specific amount or date. Which action should the administrator take to resolve this misclassification?

Accepted Answer

Add sample utterances containing amounts and date ranges to the 'CheckBalance' intent to differentiate it.. Adding sample utterances that include amounts and date ranges to the 'CheckBalance' intent provides the Einstein Bot's natural language processing (NLP) model with more training data to distinguish between balance inquiries and transaction requests. This improves intent classification accuracy by reducing overlap in the phrases the bot recognizes, directly addressing the misclassification issue.

Answer

Reduce the confidence threshold for both intents to allow more matches.

Answer

Disable the 'RecentTransactions' intent and handle transaction requests using a flow without intents.

Answer

Create a new Einstein Bot specifically for transaction inquiries and route users there.

Question 5

A nonprofit organization uses Salesforce Nonprofit Cloud with Einstein Discovery to analyze donation patterns. They have activated a story that predicts which donors are most likely to churn (stop donating) in the next three months. The story shows a top influence called 'DonationFrequency' with a negative correlation: donors who donate less than once per quarter are 40% more likely to churn. The director of development wants to use this insight to create a retention campaign. However, the story also includes a field called 'LastDonationAmount' which has a small positive influence. The development team wants to ensure the predictions are actionable. What should the administrator do to maximize the effectiveness of the Einstein Discovery story for this retention campaign?

Accepted Answer

Create a segment of donors with low donation frequency and use that as the target for the retention campaign.. Option D is correct because the most actionable insight from the Einstein Discovery story is the strong negative correlation of 'DonationFrequency' with churn. By creating a segment of donors with low donation frequency, the administrator can directly target the highest-risk group for a retention campaign, making the prediction actionable without altering the model or its output. This approach leverages the story's findings as-is, which is the intended use of Einstein Discovery insights.

Answer

Retrain the prediction model using only 'DonationFrequency' and 'LastDonationAmount' as predictors.

Answer

Delete the 'LastDonationAmount' influence from the story to simplify the output.

Answer

Adjust the influence weight of 'DonationFrequency' to be higher in the story settings.

Question 6

Universal Containers (UC) uses Einstein Lead Scoring to prioritize leads. They have 500,000 leads in the system. Recently, the model scores have been inconsistent: some leads with low activity receive high scores, while active leads score low. The model was trained 3 months ago. UC updates lead records daily via an external system, but the data is often incomplete (e.g., missing company size). Support has reported slow performance on lead views. The admin notices that the 'Data Refresh Status' for Einstein Lead Scoring shows 'Pending' for 2 weeks. UC wants to improve model accuracy and performance.

Which action should the admin take first?

Accepted Answer

Schedule a data transform to cleanse and refresh lead data before model training. Option A is correct because the 'Data Refresh Status' has been 'Pending' for two weeks, indicating that the model is not receiving updated lead data. Scheduling a data transform to cleanse and refresh lead data before model training directly addresses the root cause: incomplete and stale data (e.g., missing company size) leads to inconsistent scores. This action ensures the model trains on clean, current data, improving both accuracy and performance.

Answer

Increase data retention period for lead records

Answer

Rebuild the Einstein Lead Scoring model using different fields

Answer

Increase the model training frequency to weekly

Question 7

A sales operations team is training an AI model to forecast quarterly revenue. They have five years of historical data, which includes a strong seasonal pattern but also a significant outlier: during the pandemic year, revenue dropped by 70% from typical values. The model trains with high accuracy on historical data but fails to predict future quarters accurately, consistently overestimating revenue. What should the data scientist do to improve forecast accuracy?

Accepted Answer

Remove the data points corresponding to the pandemic year from the training set.. Option B is correct because removing the pandemic year data eliminates the extreme outlier that is causing the model to learn a distorted seasonal pattern. The 70% revenue drop is not representative of future quarters, so including it forces the model to overestimate revenue to compensate for the anomaly. By training only on typical data, the model can learn the true seasonal pattern and generalize better to future quarters.

Answer

Add a binary feature indicating whether each quarter was during the pandemic.

Answer

Normalize the entire dataset using Z-scores to reduce the impact of the outlier.

Answer

Include the outlier data and increase the model capacity to capture the anomaly.

Question 8

A multinational corporation uses Salesforce AI to analyze customer feedback across multiple languages. They have 10,000 English reviews, 2,000 Spanish reviews, and 500 French reviews. The sentiment model performs well on English (F1=0.85) but poorly on French (F1=0.40). The data scientist wants to improve French sentiment performance without collecting new data. What should they do?

Accepted Answer

Apply data augmentation to the French reviews using back-translation (translate to another language and back) to create more training examples.. Data augmentation techniques like back-translation generate synthetic French samples, effectively increasing the minority language's representation and helping the model learn better.

Answer

Translate all French reviews to English and train only on English data.

Answer

Use a multilingual pre-trained model without any additional French data.

Answer

Remove French data and use only English and Spanish to avoid imbalance.

Question 9

A financial services firm uses Data Cloud to enrich sales data with external credit scores via an API. They set up a Data Action to call the credit bureau API for each new lead. Over time, API costs are rising, and the action is slowing down lead processing. They only need credit scores for leads with a high probability of conversion. What is the best approach to reduce costs and improve performance?

Accepted Answer

Use a Calculated Insight to score leads based on internal data and only invoke the Data Action for leads with a high probability of conversion. Option C is the best solution because it uses a Calculated Insight to compute a conversion probability score based on internal data, then only triggers the Data Action for leads above a threshold. This reduces API calls significantly. Option A filters but still requires an initial call? Actually, a Data Transform filter could be applied before the Data Action, but a Calculated Insight allows dynamic scoring. Option B runs the action in batch, but still calls for all leads. Option D removes the action entirely, losing the enrichment.

Answer

Remove the Data Action and manually verify credit scores for top leads

Answer

Apply a Data Transform to filter leads that have incomplete data before the Data Action

Answer

Schedule the Data Action to run daily in batch instead of real-time

Question 10

A mid-size company uses Sales Cloud with Einstein Lead Scoring and Einstein Activity Capture. The sales team reports that lead scores are not updating for leads that have been engaged via email and calendar events over the past two weeks. The admin checks the Einstein Lead Scoring model and finds that the model status is 'Active' and was retrained last month. The admin also verifies that Einstein Activity Capture is enabled and syncing data correctly. However, the lead scores remain unchanged. Upon further investigation, the admin discovers that the leads were created before the Einstein Lead Scoring model was activated, and the model's training data includes only leads created after activation. The company has over 10,000 leads, but only 200 were created after activation. Historical conversion data for leads created before activation is not being used. What should the admin do to ensure lead scores reflect recent engagement?

Accepted Answer

Retrain the Einstein Lead Scoring model using all historical lead data, including pre-activation leads. Option D is correct because retraining the model with all historical lead data (including pre-activation leads) will include conversion patterns from a larger dataset, improving accuracy and enabling scores for older leads. Option A is wrong because field mapping alone does not cause scoring to update. Option B is wrong because Einstein Activity Capture is already syncing; the issue is with the model. Option C is wrong because the scoring fields are separate from activity tracking.

Answer

Map the email and event fields to the lead object so that the model can use them

Answer

Add the Activity Count field to the scoring fields list in the model configuration

Answer

Re-enable Einstein Activity Capture to resync all historical emails and events

Question 11

You are a data scientist at a retail company. The company uses Einstein Discovery to analyze customer purchase patterns. The model is built on a dataset of 50,000 transactions. The model's R-squared is 0.85, but the predictions for new customers are consistently off by a large margin. The data includes features like 'Customer Age', 'Income', 'Previous Purchases', and 'Product Category'. The model was trained on data from the past two years. However, six months ago, the company launched a new loyalty program that significantly changed purchasing behavior. You suspect the model is not generalizing to new customers. What should you do to validate your hypothesis?

Accepted Answer

Create a holdout set of transactions from the last six months and compare model performance on it vs. older data. Option A is correct because creating a holdout set of transactions from the last six months directly tests whether the model's performance has degraded due to the loyalty program's impact on purchasing behavior. By comparing the R-squared or other metrics on this recent holdout set versus older data, you can quantify the drop in predictive accuracy and confirm that the model fails to generalize to the new data distribution. This approach is a standard method for detecting concept drift in machine learning models, especially when external changes (like a loyalty program) alter the underlying patterns.

Answer

Exclude new customers from the dataset entirely

Answer

Increase the training data size to include older transactions

Answer

Remove the 'Product Category' feature to simplify the model

Question 12

You are an admin at a financial services firm. The firm wants to use Einstein Next Best Action to offer personalized product recommendations to customers on its service portal. The data includes customer profiles, transaction history, and support case history. The Einstein Next Best Action strategy is configured with a recommendation that shows a 'Savings Account' offer to customers who have a checking account. However, the recommendation is not appearing for any customers. You check the Data Flow and see that the 'Account' object data is flowing correctly. The recommendation's filter condition is: AND( Has_Checking_Account__c = true, Age__c > 18 ). You verify that many customers meet these conditions. What is the most likely reason the recommendation is not appearing?

Accepted Answer

The recommendation is not activated or published. The most likely reason the recommendation is not appearing is that it has not been activated or published. In Einstein Next Best Action, recommendations must be explicitly activated or published to become available for serving to customers; configuration alone does not make them live. Since the data flow is correct and the filter conditions are valid, the missing activation step is the typical cause of a recommendation not showing.

Answer

The 'Account' object is not supported by Einstein Next Best Action

Answer

The customer data is not being refreshed in real time

Answer

The filter condition is syntactically incorrect

Question 13

A manufacturing company uses Salesforce Sales Cloud with Einstein Activity Capture to log emails and events automatically. The sales team has noticed that some emails from Microsoft Outlook are not appearing in the Einstein Activity Capture dashboard, even though the users have configured the connection correctly. The administrator checks the activity capture settings and sees that the data source is enabled and users have the correct permissions. The missing emails are from a specific domain, @supplier.com, which is a known vendor. What should the administrator do to ensure these emails are captured?

Accepted Answer

Add the @supplier.com domain to the allowlist in Einstein Activity Capture settings.. Einstein Activity Capture uses allowlists and blocklists to control which emails are captured. By default, emails from external domains may be excluded to reduce noise. Adding @supplier.com to the allowlist ensures that emails from that domain are explicitly included in the capture process, overriding any default filtering.

Answer

Increase the sync frequency for Einstein Activity Capture to every 5 minutes.

Answer

Instruct users to manually log the supplier emails as activities in Salesforce.

Answer

Assign the 'Einstein Activity Capture User' permission set to the supplier's email domain.

Question 14

A retail company has implemented a Salesforce AI lead scoring model to prioritize high-value customers. After three months, the model's AUC-ROC score is only 0.55, indicating poor performance. The data scientist reviews the training data and finds that 20% of the records are exact duplicates due to multiple data imports from different sources. The duplicates have inconsistent target labels (some labeled 'converted', others 'not converted'). What should the data scientist do to improve model performance?

Accepted Answer

Remove all duplicate records and keep only one instance per duplicate group, resolving label conflicts by majority vote.. Duplicate records with conflicting labels confuse the model. Removing duplicates and resolving label conflicts (e.g., by majority vote) is the most effective step to clean the data and improve performance.

Answer

Downsample duplicates to reduce their impact but keep all records.

Answer

Use the duplicates as a separate class to indicate noisy data.

Answer

Keep all duplicates because they represent multiple interactions; increase model complexity to handle them.

Question 15

A sales rep noticed that the Einstein Lead Scoring prediction bar shows 'No score available' for many leads. The admin confirmed that Einstein Lead Scoring is enabled and the permission set is assigned. What is the most likely cause?

Accepted Answer

The org does not have enough historical data to train the scoring model.. Einstein Lead Scoring requires a minimum amount of historical lead data (typically at least 2,000 converted and 2,000 unconverted leads) to train its predictive model. If the org lacks sufficient historical data, the model cannot generate scores, resulting in 'No score available' for leads. This is the most likely cause because the admin confirmed the feature and permissions are correctly enabled.

Answer

The leads have not yet been assigned to a user.

Answer

The leads were created less than 30 days ago.

Answer

The lead scoring model is still training.