AI Associate AI Fundamentals Practice Test 6 — 15 Questions

Question 1

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

Accepted Answer

Summarizing case conversations from service interactions.. 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 2

According to Salesforce's AI Trust Principles, which TWO practices are essential for ethical AI deployment?

Accepted Answer

Hold the organization accountable for AI-driven outcomes.. Salesforce's AI Trust Principles emphasize organizational accountability for AI-driven outcomes, ensuring that the organization takes responsibility for the decisions and impacts of its AI systems. This principle aligns with ethical AI deployment by requiring governance, oversight, and mechanisms to address unintended consequences, rather than shifting blame to the technology itself.

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Use AI to replace human jobs entirely.

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Ensure models achieve 100% accuracy before deployment.

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Fully automate decision-making without human review.

Question 3

Refer to the exhibit. A Salesforce admin configured the Einstein Trust Layer policy shown. What is the effect of this policy on AI model usage?

Accepted Answer

AI models receive masked data for those fields, but feature importance insights are still available.. The Einstein Trust Layer policy configured to mask specific fields ensures that sensitive data is replaced with masked values before being sent to the AI model. This preserves data privacy while still allowing the model to generate predictions and insights. Feature importance insights remain available because they are computed from the masked data, not the original values.

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All fields in the org will be masked to protect customer privacy.

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AI models will not be able to use the configured fields, and model insights are disabled.

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AI models can still use the fields but feature importance insights are blocked.

Question 4

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 5

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.

Answer

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 6

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 7

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 8

A financial services company is deploying Einstein Prediction Builder to predict customer churn. The data includes both numerical and categorical fields. Which step is essential to ensure the model is not biased against protected attributes like race or gender?

Accepted Answer

Exclude any protected attributes from the training data and ensure the model does not use correlated proxies.. Excluding protected attributes like race or gender from the training data and ensuring the model does not use correlated proxies is essential to prevent bias in Einstein Prediction Builder. This approach directly removes the risk of the model learning discriminatory patterns based on these attributes, as the platform relies on the data provided and does not automatically enforce fairness constraints. Including such attributes or relying on built-in fairness would not guarantee unbiased predictions because the model could still infer protected characteristics from correlated features.

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Include race and gender as predictors to allow the model to adjust for them.

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Rely on the model's built-in fairness constraints.

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Use a deep learning algorithm to automatically handle bias correction.

Question 9

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 10

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 11

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 12

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.. 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.

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

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

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

Question 13

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 14

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 15

A company uses Einstein Prediction Builder to create a custom model that predicts whether a support case will be escalated. The model is built and published, but when the admin looks at the case record, the prediction field shows 'No Prediction' for all cases. The prediction is set to run on case creation and update. What should the admin check?

Accepted Answer

The model's confidence threshold is too high, causing no predictions.. When Einstein Prediction Builder shows 'No Prediction' for all cases, a common cause is that the model's confidence threshold is set too high. The prediction field only displays a value when the model's confidence in its prediction exceeds that threshold; if no case meets the threshold, all predictions are suppressed. The admin should lower the confidence threshold in the model settings to allow predictions to appear.

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The prediction field is not added to the case page layout.

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The model was not activated for all record types.

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The data prep steps included all required fields.