AI Associate Practice Test 33 — 15 Questions

Question 1

Refer to the exhibit. What effect does this masking policy have on the data used for training an Einstein model?

Accepted Answer

SSN and CreditCard fields are partially masked, showing only the last four characters.. The masking policy in Einstein applies a partial mask to sensitive fields like SSN and CreditCard, showing only the last four characters while obscuring the rest. This ensures that the data used for training retains its structural utility for model learning without exposing full sensitive values, which is why option D is correct.

Answer

Only SSN is masked.

Answer

SSN and CreditCard fields are encrypted.

Answer

SSN and CreditCard fields are completely removed from training data.

Question 2

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.

Answer

Article publication dates

Answer

User job titles

Answer

Article author names

Question 3

Refer to the exhibit. A Salesforce CLI output shows the status of Einstein models in the org. Which model should the administrator investigate first?

Accepted Answer

Case_Escalation because it has an Error status.. Option D is correct because an Einstein model with an 'Error' status indicates a critical failure that prevents the model from generating predictions or scoring records. This requires immediate investigation to restore functionality, as the model is non-operational and may impact business processes relying on its output.

Answer

Lead_Score_Model because its accuracy is lower than expected.

Answer

Campaign_Response because it has the oldest training date.

Answer

Opportunity_Forecast because it is still training.

Question 4

Which TWO of the following are common dimensions of data quality that must be addressed for AI training?

Accepted Answer

Accuracy of values. Accuracy of values (Option B) is a fundamental dimension of data quality because AI models learn patterns from training data; if the data contains incorrect values, the model will learn and propagate those errors, leading to unreliable predictions. For example, in a dataset of customer ages, a single erroneous entry of '200' can skew the model's understanding of age distributions, directly impacting model performance.

Answer

Storage efficiency

Answer

Encryption strength

Answer

Consistency with external benchmarks

Question 5

Which THREE actions are recommended when preparing data for Einstein Next Best Action? (Choose 3)

Accepted Answer

Provide data on which actions were offered and whether they were accepted. Option A is correct because Einstein Next Best Action (NBA) requires historical interaction data showing which actions were offered and whether they were accepted to train the predictive model. This feedback loop enables the AI to learn which actions are most effective for specific customer contexts, directly improving recommendation accuracy.

Answer

Include at least 10 different action types per strategy

Answer

Use only historical data from the last 30 days

Question 6

An admin is troubleshooting Einstein Sentiment. The model returns high confidence but wrong sentiment (e.g., positive reviews labeled negative). What is the most likely issue?

Accepted Answer

The training data has incorrect labels for sentiment.. Option C is correct because if the training data contains incorrect labels for sentiment, the model learns from erroneous ground truth, leading to high confidence in wrong predictions. In Einstein Sentiment, the model's accuracy depends directly on the quality and correctness of the labeled training data; mislabeled examples cause the classifier to associate features with the wrong sentiment class, resulting in confident but incorrect outputs.

Answer

The model was not retrained after the last data load.

Answer

The training data contains predominantly neutral examples.

Answer

The field mapping for the sentiment field is incorrect.

Question 7

A company is training a customer service chatbot using historical conversation logs. Which TWO data preparation practices should be followed to ensure data quality?

Accepted Answer

Include answers with varied phrasing to enhance language variety. Option B is correct because training a chatbot on varied phrasing (e.g., synonyms, different sentence structures) improves its ability to understand and generate natural language responses. This practice enhances the model's robustness and generalization, preventing overfitting to a narrow set of expressions and ensuring it can handle the diverse ways customers phrase their queries.

Answer

Exclude all user identifiers to protect privacy

Answer

Include only successful interactions that were resolved

Answer

Filter only English conversations for consistency

Question 8

A healthcare provider uses an AI system to predict patient readmission risk. The system was trained on historical data from the past five years, during which the hospital served a predominantly urban population. Recently, the hospital expanded to rural areas with different demographic and socioeconomic profiles. The AI predictions have been less accurate for rural patients, leading to misallocation of care resources. The AI Ethics committee is reviewing the system for potential bias. The model outputs a risk score from 0 to 100. The data science team has identified that the model uses features such as income, distance from hospital, and insurance type, which may correlate with race and socioeconomic status. The team wants to make the model fairer without retraining from scratch. Which approach best balances fairness and predictive accuracy?

Accepted Answer

Apply a post-processing calibration that adjusts the risk score thresholds separately for urban and rural populations to achieve equal false positive rates.. Option C is correct because post-processing calibration adjusts the decision thresholds for each subgroup (urban vs. rural) to equalize a fairness metric (e.g., false positive rate) without modifying the underlying model. This approach preserves the predictive signal from the original features while directly addressing the bias caused by distribution shift, making it the most practical solution when retraining from scratch is not feasible.

Answer

Remove the features income, distance, and insurance type from the model and retrain.

Answer

Continue using the current model but add a disclaimer that predictions may be less accurate for rural patients.

Answer

Retrain the model using only the latest year of data that includes rural patients.

Question 9

A sales manager wants to implement Einstein Automated Contacts to automatically create contacts from email interactions. The admin enables the feature and assigns the permission set. However, no contacts are being created automatically. What is the most likely reason?

Accepted Answer

The admin did not set up the contact creation criteria in the Einstein Automated Contacts settings.. Einstein Automated Contacts requires the admin to explicitly define the criteria that trigger automatic contact creation from email interactions. Simply enabling the feature and assigning the permission set does not configure the rules for when and how contacts should be created. Without these criteria, the system has no instructions to act upon, so no contacts are generated.

Answer

The users have not logged any emails to Salesforce.

Answer

The feature requires at least 500 emails to be processed before it starts creating contacts.

Answer

The users need to install a plugin for their email client.

Question 10

A service organization wants to use Einstein Reply Recommendations to suggest responses to customer chats. The feature is enabled, but agents report that no recommendations appear. The admin has ensured the permission set is assigned and the chat data is flowing. What should the admin examine next?

Accepted Answer

The language settings for the chat channels.. Einstein Reply Recommendations require the chat channel language to be set to a supported language (e.g., English, Spanish, French, German, Portuguese, or Japanese). If the language setting is unsupported or mismatched, the model cannot generate suggestions, even if permissions and data flow are correct. This is a common misconfiguration because the feature silently fails when the language is not recognized.

Answer

The number of closed cases with successful chat histories.

Answer

The model training schedule.

Answer

The user's profile settings for chat.

Question 11

A global retail company with 50,000+ users has deployed Einstein Activity Capture across Sales and Service Clouds. After two weeks, the VP of Sales reports that only 60% of emails sent from Outlook are being logged in Salesforce. Users have installed the Einstein Activity Capture plugin and have the correct permission set. The admin has verified that the email logging settings are enabled for all users. The company uses Exchange Online. What should the admin investigate first?

Accepted Answer

Confirm that the Salesforce connected app is authorized in Exchange Online.. Option C is correct because Einstein Activity Capture for Exchange Online requires an OAuth 2.0 connected app authorization in Exchange Online to access mailbox data. Even if the plugin is installed and permissions are set, without the connected app being authorized, the service cannot retrieve email metadata, causing a significant logging gap. This is the first thing to verify since it is a common misconfiguration during initial setup.

Answer

Review the Exchange Online mailbox audit logs to see if emails are being accessed.

Answer

Check the email synchronization frequency in the Einstein Activity Capture settings.

Answer

Ensure that all users have the 'Email Integration' permission enabled in their profiles.

Question 12

A healthcare organization uses Salesforce to develop an AI model for patient readmission prediction. They must comply with HIPAA regulations. The dataset includes patient names, addresses, medical record numbers, and detailed clinical notes. The data scientist plans to train a supervised model using historical readmission outcomes. What is the most important data governance step before model training?

Accepted Answer

De-identify all protected health information (PHI) by removing or masking identifiers.. Option B is correct because HIPAA mandates that protected health information (PHI) must be de-identified before it can be used for model training without patient authorization. Removing or masking identifiers such as names, addresses, and medical record numbers ensures the dataset no longer contains individually identifiable information, allowing the organization to comply with the HIPAA Privacy Rule while still using clinical notes for predictive modeling.

Answer

Use only aggregated data that does not include any patient-level details.

Answer

Obtain written patient consent for every record used in training.

Answer

Store the data in a separate, encrypted environment with access controls.

Question 13

Which TWO considerations are important when labeling data for a supervised learning model?

Accepted Answer

Maintaining consistent guidelines.. Maintaining consistent guidelines (A) is critical because supervised learning models learn patterns from labeled data; inconsistent labels introduce noise and confuse the model, degrading its accuracy. Labeler expertise (B) ensures that domain-specific nuances are correctly captured, which is especially important for tasks like medical imaging or legal document classification where errors have high cost.

Answer

Using automated labeling for all tasks.

Answer

Ignoring inter-labeler agreement.

Answer

Labeling only a small sample.

Question 14

A company is preparing data for Einstein Article Recommendation. Which data source is most appropriate for training the model?

Accepted Answer

Historical article view and click data.. Einstein Article Recommendation uses supervised machine learning to predict which articles users are likely to find relevant. The model must be trained on historical user engagement signals—specifically article view and click data—to learn patterns of relevance. Without this behavioral data, the model cannot establish a correlation between user actions and article content.

Answer

Org metadata.

Answer

System debug logs.

Answer

User profile data only.

Question 15

A company uses Einstein Prediction Builder to predict customer churn. The model's accuracy is low. The admin reviews the training data and notices that only 2% of records are churned. What should the admin do to improve the model?

Accepted Answer

Use oversampling techniques.. Option C is correct because when a dataset has severe class imbalance (only 2% churned records), the model becomes biased toward predicting the majority class (non-churned), leading to low accuracy despite high apparent performance. Oversampling techniques, such as SMOTE or random oversampling, artificially increase the number of churned records in the training set to balance the classes, allowing Einstein Prediction Builder to learn patterns for the minority class more effectively.

Answer

Remove the churned records.

Answer

Increase the amount of training data.

Answer

Change the prediction field.

AI Associate Practice Test 33 — 15 Questions

Exhibit

AI Associate Practice Test 33 — 15 Questions

Exhibit