Salesforce · Free Practice Questions · Last reviewed May 2026
24real 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.
A 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.
An admin notices that Einstein Activity Capture is logging duplicate email records. Which action should be taken to resolve this?
Disable Einstein Activity Capture and re-enable it after 24 hours.
Increase the sync interval to reduce the chance of duplicates.
Verify that each contact has only one primary email address in Salesforce.
Multiple email addresses can cause duplicates.
Update the email client to the latest version.
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Practice this domainA 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
A company wants to use Einstein Bots to handle common customer service inquiries. Which feature should be enabled to allow the bot to escalate to a live agent when it cannot resolve the issue?
Einstein Case Classification
Einstein Reply Recommendations
Omni-Channel Flow
Omni-Channel Flow can route unresolved bot conversations to live agents.
Einstein Article Recommendations
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.
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Practice this domainA 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 healthcare organization uses Einstein Discovery to predict patient readmission risk. The model uses protected attributes like race and age as features. Which action best aligns with Salesforce's ethical AI principles?
Retain the features but monitor for disparate impact and ensure compliance with regulations.
Ethical AI allows use if monitored and regulated.
Remove race and age features entirely to ensure fairness.
Replace age with an age group bucket to reduce granularity.
Use the model as is because predictions are accurate.
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.
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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 uses Einstein Discovery to identify factors that increase case resolution time. After training, the model shows that 'Case_Origin__c' has high importance. What action should the company take?
Remove the field from the model to reduce complexity.
Create interaction terms between Case_Origin and other fields.
Increase the data quality threshold for Case_Origin records.
Investigate the categories within Case_Origin to understand their impact.
Understanding which origins cause delays helps in process improvement.
A company has set up Einstein Next Best Action with a recommendation strategy. They want to ensure that recommendations are personalized based on the customer's recent behavior. What data should be used?
Event data from the website tracked via Google Analytics.
Streaming data from Data Cloud that includes recent website interactions.
Data Cloud can ingest streaming events and make them available for real-time decisions.
Static profile fields like customer age and location.
Historical data from a data warehouse updated daily.
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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 4 domains: 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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