AI Associate AI Fundamentals Practice Test 7 — 15 Questions

Question 1

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.. 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.

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Lead_Score_Model because its accuracy is lower than expected.

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Campaign_Response because it has the oldest training date.

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Opportunity_Forecast because it is still training.

Question 2

A mid-sized company uses Salesforce for sales and service. They have implemented Einstein Prediction Builder on a custom object 'Support_Ticket__c' to predict whether a ticket will be escalated (field: 'Escalated__c' Boolean). The model was trained with 10,000 records and 15 fields including 'Subject', 'Description_Summary__c', 'Priority__c', 'Hours_to_Resolution__c', and others. After deployment, the model's precision for escalated tickets is only 30%, while recall is 80%. The business finds too many false positives. The admin notices that the 'Priority__c' field has many missing values (60% null) and that the field 'Is_Critical__c' (a formula field) was included though it flags tickets as critical only rarely. The data spans 12 months but the last 3 months have a significantly higher escalation rate due to a product bug that has since been fixed. Which course of action will most likely improve the model's precision without harming recall?

Accepted Answer

Filter training data to exclude tickets from the last 3 months and impute missing 'Priority__c' values with the most common priority.. It addresses both the data drift and data quality issues that degrade precision. Excluding the last 3 months removes the biased escalation pattern caused by a fixed product bug, ensuring the model learns from stable historical patterns. Imputing missing 'Priority__c' values with the most common priority reduces noise from nulls without discarding the field entirely, which helps maintain recall by preserving predictive signal.

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Roll back the model to the version trained 6 months ago when escalation rates were lower.

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Exclude the 'Priority__c' field from the model and retrain.

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Remove the 'Is_Critical__c' field and increase training data to 50,000 records.

Question 3

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.. 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 4

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 5

A service manager wants to use Einstein Case Classification to automatically categorize incoming cases. What is a prerequisite for training the model?

Accepted Answer

At least 500 closed cases with the correct category field populated.. Einstein Case Classification uses supervised machine learning to automatically categorize cases. The model learns from historical data, so it requires a sufficient number of closed cases (at least 500) with the correct category field populated to train effectively. Open cases are not used because the model needs confirmed outcomes to learn from.

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A minimum of 100 open cases with categories assigned.

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The cases must have been created within the last 30 days.

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Users must enable Einstein Case Classification in their personal settings.

Question 6

A company wants to use Einstein Relationship Intelligence to analyze email and calendar data for opportunity insights. Which two conditions must be met? (Select two answers.)

Accepted Answer

Users must have the Einstein Relationship Intelligence permission set.. The Einstein Relationship Intelligence permission set grants users access to the feature's insights and data processing capabilities. Without this permission set, users cannot view or interact with Einstein Relationship Intelligence data, even if the feature is enabled at the org level.

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Exchange or Gmail must be used for email.

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The feature is only available in Unlimited Edition.

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All users must opt in to email logging.

Question 7

A data scientist is evaluating Salesforce's Einstein features for predictive analytics. Which three statements accurately describe Einstein Discovery? (Select three answers.)

Accepted Answer

It can generate natural language explanations of model insights.. Einstein Discovery includes a natural language generation (NLG) engine that automatically produces plain-English explanations of model insights, such as key drivers and predictions. This allows non-technical users to understand the output without needing to interpret raw statistical data.

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It is limited to 1000 rows of data per prediction.

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It requires a separate data preparation tool before use.

Question 8

A sales operations admin wants to use Einstein Opportunity Scoring. Which two steps are required to activate Einstein Opportunity Scoring? (Select two answers.)

Accepted Answer

Enable Einstein in the Einstein Setup menu.. Option A is correct because Einstein features, including Einstein Opportunity Scoring, must first be turned on by enabling Einstein in the Einstein Setup menu (Setup → Einstein Setup), which activates the underlying Einstein services for the org. Option B is correct because Einstein Opportunity Scoring requires a sufficient volume of historical data to train its model — specifically, at least 100 won opportunities and 100 lost opportunities within the last 24 months — before scoring can be activated. Option C is not required because Einstein Opportunity Scoring uses a built-in, pre-trained model rather than a custom scoring model the admin must create. Option D is not required because there is no prerequisite to deactivate existing scoring rules to activate Einstein Opportunity Scoring. Option E is not required for activation because assigning the permission set controls user access to the feature, not the activation of the scoring model itself.

Answer

Create a custom scoring model specific to the business.

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Deactivate any existing scoring rules.

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Assign the Einstein Opportunity Scoring permission set to users.

Question 9

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.

Answer

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 10

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.

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The leads have not yet been assigned to a user.

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The leads were created less than 30 days ago.

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The lead scoring model is still training.

Question 11

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.

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The number of closed cases with successful chat histories.

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The model training schedule.

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The user's profile settings for chat.

Question 12

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.

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The users have not logged any emails to Salesforce.

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

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

Question 13

A marketing manager wants to predict which customers are most likely to respond to a new email campaign. Which type of machine learning is most appropriate?

Accepted Answer

Supervised learning. Supervised learning is appropriate because the task is to predict a binary outcome (respond or not respond) based on historical data where the outcome is known. The model learns from labeled examples of customers who did or did not respond, then predicts for new customers.

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Generative AI

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Unsupervised learning

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Reinforcement learning

Question 14

A sales team notices that their lead scoring model assigns high scores to leads that rarely convert. The model was trained on data from the past 5 years. What is the most likely cause?

Accepted Answer

There is concept drift because buying behaviors have changed. Concept drift is the most likely cause because the model was trained on five years of historical data, and buying behaviors have changed over that period, so the relationship between features and conversion has shifted. The model's learned patterns no longer reflect current reality, causing high scores for leads that rarely convert. Retraining on recent data or monitoring for drift is the standard remedy.

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The features used are irrelevant to lead conversion

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The model is overfitting to noise in the training data

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The model is underfitting and needs more features

Question 15

A data scientist is training a model to predict churn. The model achieves 99% accuracy on training data but only 60% on test data. Which issue is most likely occurring?

Accepted Answer

Overfitting. A large gap between training accuracy (99%) and test accuracy (60%) is the classic signature of overfitting: the model has memorized training data, including noise, and fails to generalize. This typically results from excessive model complexity, too little training data, or insufficient regularization.

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Concept drift

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Data leakage

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Underfitting