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.
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.
Missing values and outliers distort the statistical patterns Einstein Prediction Service learns, biasing churn predictions. Cleaning these ensures the model trains on representative, consistent records, which is the most critical preparation step for forecast accuracy.
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 connects to the user's mailbox through supported email providers, so the account must be hosted on Gmail or Outlook. Unsupported or on-premises mail servers cannot be linked, blocking automatic email and event logging.
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.
Training additional intents and example phrases directly expands the bot's natural language understanding model, enabling it to classify complex utterances it previously misrouted. This satisfies the stem's constraint — frequent comprehension failures causing escalations — by addressing the underlying intent-recognition gap rather than masking symptoms through routing or handoff changes.
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.
Automated image classification removes the need for volunteers to manually review thousands of photographs, directly satisfying the stem's disaster-assessment scenario. Einstein Vision processes images at scale, cutting the hours of human sorting that delay relief decisions, so damage assessment completes faster and with less labour.
It can only classify images of specific disaster types.
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 Scoring derives its predictive signal from behavioural telemetry: email opens, clicks, replies and web visits tied to each lead. This interaction history, not firmographic fields, forms the training input, so prioritising leads by likelihood to engage satisfies the scenario's requirement.
Historical conversion data from closed opportunities.
Lead demographic information like industry and company size.
Social media posts and mentions of the company.
Which TWO actions are best practices when implementing Einstein Prediction Service?
Ignore correlated features to simplify the model.
Clean the data to handle missing values and outliers.
Missing values and outliers distort the statistical patterns the model learns, producing skewed or unstable predictions. Cleaning the dataset before training satisfies the data-quality prerequisite for Einstein Prediction Service, ensuring the model's outputs reflect genuine relationships rather than artefacts.
Select relevant features that are likely to influence the prediction.
Predictive accuracy depends on feeding the model fields that genuinely correlate with the outcome. Selecting relevant features, and excluding noise or leakage-prone columns, satisfies the feature-engineering requirement for Einstein Prediction Service, improving discrimination between positive and negative records.
Include all available fields in the dataset for maximum information.
Use the default field mapping without review.
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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
Einstein Activity Capture requires OAuth authorisation so the service can read and write email and calendar data on the user's behalf. This satisfies the prerequisite that users grant explicit consent to connect their Microsoft 365 or Google Workspace accounts, enabling automatic logging without manual entry.
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
Einstein Lead Scoring predictions degrade when the underlying model reflects outdated conversion patterns. Retraining with recent conversion data realigns the model to current buyer behaviour, directly addressing the stem's constraint that high-scoring leads no longer convert.
Disable field-level security for scoring fields
Lower the lead conversion threshold
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 routes work items to queues based on agent capacity and availability, enabling the bot to transfer an unresolved conversation to a live agent. This satisfies the escalation requirement by invoking a flow that creates a work item, which Omni-Channel then assigns to an available service representative.
Einstein Article Recommendations
Which TWO actions can be performed using Einstein Activity Capture?
Automatically log emails from Outlook or Gmail
Einstein Activity Capture syncs email and calendar data bidirectionally between Microsoft 365 or Google Workspace and Salesforce, automatically associating messages with matching leads and contacts. This satisfies the requirement to log emails from Outlook or Gmail without manual entry, capturing them against the correct records.
Update opportunity amounts based on email content
Create tasks from email attachments
Generate leads from email signatures
Automatically log meetings from calendar events
Einstein Activity Capture reads calendar events from the connected Microsoft 365 or Google Workspace mailbox and creates corresponding activity records against matched contacts, leads and opportunities. This meets the requirement to log meetings automatically from calendar events, keeping timelines current without manual logging.
A sales manager wants to automatically prioritize leads based on their likelihood to convert. Which Einstein feature should be used?
Einstein Prediction Builder
Einstein Relationship Health
Einstein Lead Scoring
Einstein Lead Scoring applies predictive models to lead and opportunity history, producing a numeric score that ranks each lead by conversion likelihood. This directly satisfies the sales manager's need to prioritise leads automatically rather than manually assessing them.
Einstein Activity Capture
A company uses Salesforce and wants to provide automated chat responses for common customer inquiries. Which Einstein feature should be configured?
Einstein Sentiment
Einstein Case Routing
Einstein Bots
Einstein Bots directly satisfies the requirement for automated chat responses to common customer inquiries within Salesforce. It handles conversational deflection natively, unlike predictive scoring or analytics features. Configuring bot intents and dialog flows lets the company deploy self-service chat without external tooling, matching the stated scenario precisely.
Einstein Recommender
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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.
Bias in predictions usually originates in the training data. Auditing it for under-representation of lower-income groups reveals whether skewed sampling produced the affluent-area recommendations, satisfying the requirement to diagnose the root cause before applying mitigation or retraining.
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.
Sentiment models learn language patterns from their training data. If that data under-represents non-native phrasing, the model misclassifies neutral or positive text as negative, triggering disproportionate escalation. The disparity stems from non-representative training data rather than bot logic.
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.
Bias is detected by comparing outcomes across protected or demographic groups. Examining score distributions segmented by industry reveals whether certain industries are systematically scored lower, providing the evidence needed to confirm or dismiss the suspected bias before remediation.
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.
Gender is a protected attribute unrelated to genuine performance. Training on it lets the model encode historical discrimination, producing disparate outcomes for employees of one gender and breaching fairness principles even if overall accuracy rises.
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.
Post-processing adjusts the final recommendation scores or thresholds to equalise outcomes across groups, directly countering the disparate high-interest loan offers without retraining. It satisfies the mitigation requirement by correcting biased outputs at the decision boundary.
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.
Training solely on English reviews creates a language bias: the sentiment model lacks Spanish vocabulary and syntax, so it systematically mislabels Spanish positive feedback as negative. This directly harms Spanish-speaking customers through unequal service, satisfying the stem's fairness constraint — the model is unfair to that group.
The model violates privacy regulations.
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Practice this domainWhat 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 Einstein Trust Layer sits between Salesforce data and the large language model, masking sensitive fields, detecting toxic content and preventing data retention by the model provider, so AI features meet security and compliance constraints.
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
Selecting only non-PII fields as predictors enforces data minimisation at the point of model configuration, directly satisfying the stem's requirement that no customer PII enters training. Unlike masking or tokenisation, which retain PII in the dataset, this approach excludes sensitive attributes entirely, preventing Einstein Prediction Builder from learning on regulated data.
A company deploys an AI-powered email composer that drafts responses to customer inquiries. To comply with GDPR, which control should they implement regarding automated decisions?
Allow the AI to send emails automatically if confidence is high
Disable the AI composer entirely to avoid GDPR risk
Anonymize customer data before drafting
Require human review before any AI-generated email is sent
GDPR Article 22 grants data subjects the right not to be subject to decisions based solely on automated processing that produce legal or similarly significant effects. Human review before dispatch satisfies this constraint, ensuring meaningful intervention in the decision to send each AI-drafted response.
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 exposes which fields most influenced a lead's score, giving the admin the transparency requested. It surfaces the contributing data points behind Einstein Lead Scoring's output, directly satisfying the stem's requirement to explain why a specific lead scored highly.
Einstein Activity Capture
Einstein Copilot prompt template
Einstein Trust Layer audit trail
What is the purpose of ‘toxicity detection’ in the Einstein Trust Layer?
To detect and prevent harmful or abusive language in AI outputs
Toxicity detection scans prompts and generated responses for harmful, abusive or offensive language, flagging or blocking it before delivery. This enforces responsible AI guardrails within the Einstein Trust Layer, satisfying the requirement to prevent harmful content reaching users.
To increase the speed of AI responses
To monitor user interactions for compliance with data protection laws
To identify and mask PII in user prompts
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
The DPA commits that customer data processed through Einstein and other AI services is excluded from training or improving Salesforce's base AI models, satisfying the stem's constraint on data usage. This contractual guarantee preserves customer data ownership and prevents reuse across tenants.
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
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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 writes the score to a field on the lead record, so it appears on the record itself and can be added as a column in lead list views for prioritisation. This satisfies the requirement to view scores in Salesforce.
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 applies natural language processing to case subject and description text, predicting field values such as type or priority. This satisfies the requirement to categorise incoming cases automatically from their description, removing manual triage by service managers.
Einstein Vision
Einstein Article Recommendations
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 syncs emails and calendar events between Microsoft 365 or Google Workspace and Salesforce automatically, creating activity records without rep entry. This satisfies the requirement to log emails and events without manual effort, since synchronisation runs continuously in the background.
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 choosing the prediction field and data set, the wizard requires selecting features — the input fields used to train the model. This satisfies the stem's next-step requirement, since features determine which historical attributes Einstein learns from before training and scoring.
Which Einstein feature provides AI-powered predictions for opportunity win likelihood?
Einstein Forecasting
Einstein Opportunity Scoring
Einstein Opportunity Scoring applies predictive models to opportunity fields and activity history, producing a win-probability score per opportunity. This satisfies the requirement for AI-powered predictions of win likelihood, letting reps prioritise deals by scored probability rather than guesswork.
Einstein Lead Scoring
Einstein Prediction Builder
A company wants to implement an autonomous AI agent in Salesforce that can handle customer service cases end-to-end. Which feature should they use?
Einstein Copilot
Einstein Next Best Action
Einstein Bots
Agentforce
Agentforce provides autonomous agents that execute multi-step service workflows inside Salesforce, resolving cases end-to-end without human handoffs. It satisfies the stem's autonomy constraint by reasoning over CRM data, invoking flows and APIs, and escalating only when required — unlike Einstein Copilot, which merely assists users rather than acting independently.
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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.
Einstein Prediction Builder requires a clearly defined prediction objective and a target date field to establish the outcome being predicted and the timeframe for labelling examples. Without these, the model cannot determine which records are positive or negative cases, making this preparation step essential before training.
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 treats text fields with a small number of distinct values as categorical predictors, automatically encoding them for model training. Keeping Customer_Status__c as text lets Discovery recognise its three categories and apply appropriate categorical handling, rather than forcing manual numeric encoding.
Convert to numeric values 1, 2, 3 to preserve order.
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.
Einstein Bot answers draw on Salesforce Knowledge, and only published articles are indexed and retrievable at runtime. Draft or archived articles stay invisible to the bot, so published status is the constraint that guarantees accurate, available responses.
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.
High importance indicates Case_Origin strongly influences resolution time, but not which categories drive it. Examining the individual categories reveals whether specific origins cause delays, enabling targeted process fixes rather than guessing at the field's overall effect.
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.
Streaming data from Data Cloud supplies near real-time website interaction events, letting the recommendation strategy react to the customer's most recent behaviour. Batch or static profile data would lag behind, failing the personalisation requirement for recent activity.
Static profile fields like customer age and location.
Historical data from a data warehouse updated daily.
Which TWO actions are required to prepare data for an Einstein Discovery model?
Remove all records with missing values in any field.
Select exactly 10 predictor fields manually.
Ensure the data is stored in a Salesforce object or a connected data source.
Einstein Discovery consumes data from a Salesforce object or a connected external data source, so the dataset must reside in one of these locations before a model can be created. This storage requirement is a prerequisite for building the story and training the model.
Create a separate dataset for training and validation.
Define the outcome field that the model will predict.
Einstein Discovery is supervised learning, so it requires a designated outcome variable to predict. Without defining the outcome field, the model cannot determine what pattern to learn, making this a mandatory data preparation step before training.
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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: AI Fundamentals, AI Capabilities in CRM, Ethical Considerations of AI, Ethical AI and Data Privacy, Salesforce Einstein AI Features, 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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