Salesforce · Free Practice Questions · Last reviewed May 2026
36real exam-style questions organised by domain, each with the correct answer highlighted and a plain-English explanation of why it's right — and why the others are wrong.
What is the primary purpose of the Einstein Trust Layer in Salesforce's AI architecture?
To provide a secure gateway for AI data processing, including data masking and toxicity detection
The Trust Layer enforces zero data retention, PII masking, toxicity detection, and audit trails.
To replace all third-party AI services with Salesforce-owned models
To automatically generate AI models without any human oversight
To train large language models on customer data for better predictions
An organization using Einstein Prediction Builder wants to ensure that no customer personally identifiable information (PII) is used in model training. Which data governance practice should they enforce?
Enabling zero data retention in the Trust Layer
Data anonymization via the Einstein Trust Layer
Regularly auditing the model for bias
Data minimisation by selecting only non-PII fields as predictors
Deliberately excluding PII fields from the prediction definition is the best way to ensure PII is not used in training.
A Salesforce admin wants to display an explanation for why a specific lead received a high score from Einstein Lead Scoring. Which Salesforce feature provides this transparency?
Score Factors in Einstein Lead Scoring
Score Factors display the top contributing fields and their impact on the lead score.
Einstein Activity Capture
Einstein Copilot prompt template
Einstein Trust Layer audit trail
Under the Salesforce Data Processing Addendum (DPA), what is Salesforce's commitment regarding customer data used in AI services?
Customer data is not used to train or improve Salesforce's base AI models
This is the zero data retention commitment in the Einstein Trust Layer.
Customer data may be used to improve Salesforce's AI models unless the customer opts out
Customer data is anonymized and then used to train public AI models
Customer data is only used to train models for that specific customer
Which Salesforce AI feature provides audit logging of when AI recommendations are generated and acted upon?
Einstein Trust Layer audit trail
The Trust Layer's audit trail records AI actions and recommendations.
Einstein Discovery
Einstein Copilot
Einstein Prediction Builder
A marketing manager wants to use Einstein to personalize email content for each customer. However, they are concerned about violating CCPA if they use certain data. Which data use would be MOST likely to raise a CCPA concern?
Customer purchase history from the past year
Customer email address
Customer browsing behavior on the company website
Customer location data from mobile devices
Location data is sensitive and may require explicit consent under CCPA.
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Practice this domainA sales operations manager wants to use Einstein Lead Scoring to prioritize leads. Where can the lead score be viewed in Salesforce?
Only in Einstein Analytics dashboards
Only in the Einstein Lead Scoring setup page
In the Einstein Lead Scoring mobile app only
As a field on the lead record and in list views
Einstein Lead Scoring adds a numeric score field to the lead object, and it can be displayed in list views and reports.
A service manager wants to automatically categorize incoming cases based on their description. Which Einstein feature should be used?
Einstein Reply Recommendations
Einstein Case Classification
Einstein Case Classification auto-classifies cases into fields like Type, Priority, and Reason using machine learning.
Einstein Vision
Einstein Article Recommendations
A company uses Einstein Discovery to analyze sales data and wants to share the findings with business stakeholders who are not Salesforce users. What is the recommended way to share the story?
Create a public Salesforce site to display the story
Grant the stakeholders a Salesforce login and viewer permission
Embed the story in a Chatter post
Download the story as a PDF and email it
Einstein Discovery allows exporting stories as PDFs, which can be shared externally.
A Salesforce admin needs to create a prompt template that generates a follow-up email after a meeting. Which Prompt Builder template type should be used?
Service Reply
Field Generation
Flex Prompt
Sales Email
Sales Email templates are designed for generating email content in Sales Cloud.
A sales rep wants to automatically log emails and events to Salesforce without manual entry. Which feature should the admin enable?
Einstein Activity Capture
Einstein Activity Capture automatically logs emails and events from connected email and calendar systems.
Einstein Conversation Insights
Einstein Email Insights
Einstein Activity Capture is not available; use the standard Email-to-Salesforce
An admin is setting up Einstein Prediction Builder to predict whether a lead will convert. The admin has selected the prediction field and data set. What is the next step in the configuration wizard?
Train the model immediately
Define the prediction explanation
Choose the prediction score field
Select features (input fields) to train the model
After selecting prediction field and data set, the next step is to choose features (input fields).
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Practice this domainA retail company uses Einstein Prediction Service to forecast customer churn. To improve model accuracy, which data preparation step is most critical?
Select only the top three features based on correlation.
Clean the dataset by handling missing values and outliers.
Proper data cleaning ensures the model learns accurate patterns.
Use a different algorithm like neural networks.
Increase the dataset size by collecting more customer records.
A sales manager wants to use Einstein Activity Capture to log emails automatically. Which prerequisite must be met?
The org must be on Enterprise Edition or higher.
The user's email must be hosted on a supported platform (Gmail, Outlook).
Einstein Activity Capture integrates with supported email providers.
The user must have an Einstein AI license.
The user must manually enable email logging in personal settings.
A company uses Einstein Bots to handle customer service inquiries. The bot often fails to understand complex requests, leading to escalations. Which improvement strategy is most effective?
Train the bot with additional intents and example phrases for complex scenarios.
More training data improves NLU accuracy.
Route all complex requests directly to human agents without bot interaction.
Increase the confidence threshold for intent matching to avoid misclassification.
Reduce the number of dialogue options to simplify the bot's logic.
A nonprofit uses Einstein Vision to classify images of disaster areas. What is the primary benefit of using AI for this task?
It requires less training data than manual methods.
It eliminates all classification errors.
It reduces manual effort and speeds up damage assessment.
Automation increases efficiency.
It can only classify images of specific disaster types.
A company deploys Einstein Recommendation Builder on its e-commerce site. The recommendations are not personalized. What is the most likely cause?
The model has not been trained with enough user behavior data.
Personalization requires sufficient historical data.
The company did not hire a data scientist to tune the model.
The recommendation engine is not syncing in real-time with the website.
The product catalog is too large for the model to process.
A marketing team wants to use Einstein Engagement Scoring to prioritize leads. What is the primary input for this AI feature?
Lead interaction history with emails and web activity.
Engagement is measured by interactions.
Historical conversion data from closed opportunities.
Lead demographic information like industry and company size.
Social media posts and mentions of the company.
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A sales rep wants to use Einstein Activity Capture to automatically log emails and meetings. Which prerequisite must be met?
Chatter must be disabled for the organization
Sales Cloud Einstein licenses for all users
The feature is automatically enabled once email integration is configured
Users must grant access to their email and calendar via OAuth
Users must authorize Salesforce to access their email and calendar.
A company uses Einstein Lead Scoring and finds that leads with high scores are not converting. What should the admin do to improve prediction accuracy?
Increase the scoring model's maximum score
Retrain the model with more recent conversion data
Retraining with current data improves the model's relevance.
Disable field-level security for scoring fields
Lower the lead conversion threshold
An admin notices that Einstein Opportunity Scoring is not generating scores for new opportunities created in the past week. Which troubleshooting step should the admin take first?
Retrain the Opportunity Scoring model
Verify that users have the 'View Einstein Scores' permission
Check that there are at least 50 won and 50 lost opportunities with populated fields
Einstein models require a minimum of 50 won and 50 lost records to generate scores.
Wait 48 hours for the model to update
Which TWO actions can be performed using Einstein Activity Capture?
Automatically log emails from Outlook or Gmail
Einstein Activity Capture syncs emails to Salesforce records.
Update opportunity amounts based on email content
Create tasks from email attachments
Generate leads from email signatures
Automatically log meetings from calendar events
Meetings are logged as events in Salesforce.
Which THREE factors influence the prediction accuracy of Einstein Lead Scoring?
Custom formula fields on the lead object
Number of times a lead is viewed by sales reps
Historical conversion data of leads
The model learns from past conversions.
Conversion patterns across different lead sources
Source is a key predictor in the model.
Values in standard lead fields like industry and company size
Field values are used as predictors.
An admin reviews the Einstein service configuration JSON. Based on the exhibit, which statement is true?
Lead scoring is disabled for the org
Opportunities will not have Einstein scores
Opportunity scoring is disabled (false).
The lead scoring model retrains daily
The admin has not configured any scoring fields for leads
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A company uses Einstein Prediction Builder to recommend products. They notice the model often recommends high-priced items to users in affluent areas, potentially excluding others. What should the AI Associate do first?
Remove the model from production immediately.
Ignore the issue because the model predictions are accurate overall.
Add more features about customer income.
Check the training data for representation and bias.
Addressing data bias is the first step per Salesforce ethical AI guidelines.
An AI Associate deploys an Einstein Bot that uses sentiment analysis to escalate frustrated customers. After launch, the bot escalates disproportionately for non-native English speakers. What is the most likely cause?
The sentiment model was trained on a non-representative dataset.
Training data lacking linguistic diversity causes biased sentiment detection.
The bot is routing to the wrong department.
The escalation threshold is set too low.
The bot is not properly connected to the escalation queue.
A sales team uses Einstein Lead Scoring. They notice the model gives low scores to leads from certain industries. The AI Associate suspects bias. What should they do to validate?
Run a holdout test to check prediction accuracy.
Retrain the model with balanced data.
Review the model's confidence intervals.
Analyze the distribution of scores across industry segments.
This reveals if certain groups are systematically scored lower.
An AI Associate is asked to build a model that predicts employee performance. The dataset includes gender, department, and tenure. Which practice could introduce ethical risk?
Evaluating model performance across different groups.
Excluding gender from the model features.
Documenting model limitations and assumptions.
Including gender to improve model accuracy.
Using protected attributes can lead to biased outcomes.
A financial services firm uses Einstein Next Best Action to offer credit products. The model recommends high-interest loans more often to minority groups. The AI Associate must mitigate this. What is the most effective approach?
Remove the model and use a rule-based system.
Use SHAP values to explain predictions.
Apply post-processing fairness adjustments to the recommendations.
This can equalize outcomes without full retraining.
Add a disclaimer that recommendations may be biased.
A company's Einstein Sentiment model is used to flag negative customer feedback. The model was trained on English reviews only. When deployed globally, it misclassifies positive reviews in Spanish as negative. What is the primary ethical concern?
The model is not interpretable.
The model has low accuracy for Spanish reviews.
The model is unfair to Spanish-speaking customers.
Lack of representation leads to unfair treatment.
The model violates privacy regulations.
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Practice this domainA company wants to use Einstein Prediction Builder to predict customer churn. Which data preparation step is essential before building the model?
Ensure the data is in a Salesforce connected data source like Data Cloud.
Define the prediction objective and the target date field.
The prediction objective (e.g., churn) is required to train the model.
Create a formula field to calculate the churn probability.
Create a new custom object to store the prediction results.
A data scientist needs to prepare data for Einstein Discovery. The dataset includes a field 'Customer_Status__c' with values 'Active', 'Inactive', and 'Churned'. How should this field be treated?
Create separate boolean fields for each value to improve model accuracy.
Remove the field because text fields cannot be used in Einstein Discovery.
Keep as a text field and let Einstein Discovery handle it as a categorical predictor.
Einstein Discovery automatically treats text fields as categorical predictors.
Convert to numeric values 1, 2, 3 to preserve order.
A company uses Salesforce Data Cloud to unify customer data from multiple sources. After connecting a data stream, they notice that records are missing from the unified profile. What is the most likely cause?
The data stream object is not a standard Salesforce object.
The data stream is not activated for identity resolution.
The data source is not from Salesforce, so it cannot be unified.
The reconciliation rule is not configured for the data source.
Reconciliation rules are needed to match records across sources.
A Salesforce admin is training an Einstein Bot to answer customer questions. Which data source should the bot use to provide accurate responses?
Chatter posts from the product team.
Knowledge articles with a published status.
Knowledge articles are designed for self-service.
Case records from the last 30 days.
Lead and contact reports.
A company wants to use Einstein Article Recommendations to suggest knowledge articles to support agents. What is a prerequisite for this feature?
Articles must be of a specific type, such as FAQ.
The org must be enabled for Einstein features.
A case must be open for the recommendation to appear.
Knowledge articles must be created and published.
Articles must exist to be recommended.
Which THREE factors should be considered when evaluating the quality of a dataset for an AI model?
Total number of records available for training.
Presence of outliers that may skew the model.
Outliers can distort the model's understanding.
Number of distinct labels in the outcome field.
Percentage of missing values in key fields.
High missingness can reduce model accuracy.
Number of duplicate records in the dataset.
Duplicates can cause overfitting.
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Practice this domainThe AI Associate exam has 40 questions and must be completed in 70 minutes. The passing score is 65/1000.
Scenario-based questions covering exam objectives with detailed answer explanations.
The exam covers 6 domains: Ethical AI and Data Privacy, Salesforce Einstein AI Features, AI Fundamentals, AI Capabilities in CRM, Ethical Considerations of AI, Data for AI. Questions are weighted by domain — higher-weight domains appear more on your actual exam.
No. These are original exam-style practice questions written against the official Salesforce AI Associate exam objectives. They are not copied from the real exam. Courseiva focuses on genuine understanding, not memorisation of braindumps.
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