Sample questions
Salesforce AI Associate AI Associate practice questions
A Salesforce admin implements Einstein Bots for customer service. To ensure the bot does not use biased language, what should the admin do?
Trap 1: Use only predefined responses
Predefined responses may still be biased if not carefully designed.
Trap 2: Disable the bot for sensitive topics
Avoidance is not a comprehensive solution; addressing bias is better.
Trap 3: Monitor conversations regularly
Monitoring detects issues but does not prevent bias in the bot initially.
- A
Use only predefined responses
Why wrong: Predefined responses may still be biased if not carefully designed.
- B
Disable the bot for sensitive topics
Why wrong: Avoidance is not a comprehensive solution; addressing bias is better.
- C
Review training data for representativeness
Correct. Diverse and representative training data reduces bias.
- D
Monitor conversations regularly
Why wrong: Monitoring detects issues but does not prevent bias in the bot initially.
A data architect is designing a data model for Einstein Discovery. The data includes categorical variables with high cardinality (e.g., postal codes). What is the best practice to handle such features?
Trap 1: Encode them as one-hot vectors.
One-hot encoding with high cardinality creates too many features.
Trap 2: Exclude them from the model.
Excluding may lose valuable information.
Trap 3: Use the raw values without transformation.
Raw values with many levels can cause overfitting.
- A
Encode them as one-hot vectors.
Why wrong: One-hot encoding with high cardinality creates too many features.
- B
Exclude them from the model.
Why wrong: Excluding may lose valuable information.
- C
Use the raw values without transformation.
Why wrong: Raw values with many levels can cause overfitting.
- D
Group them into higher-level categories (e.g., region).
Reduces cardinality while preserving signal.
A data analyst is evaluating data quality for an Einstein model. Which TWO dimensions are most critical for model accuracy?
Trap 1: Uniqueness
Important for deduplication but not as critical for model accuracy.
Trap 2: Consistency
Important but less critical than completeness and accuracy.
Trap 3: Timeliness
Timeliness matters for relevance, but not directly for accuracy.
- A
Uniqueness
Why wrong: Important for deduplication but not as critical for model accuracy.
- B
Accuracy
Incorrect values directly degrade model predictions.
- C
Consistency
Why wrong: Important but less critical than completeness and accuracy.
- D
Completeness
Missing data can bias the model.
- E
Timeliness
Why wrong: Timeliness matters for relevance, but not directly for accuracy.
Which TWO actions are required to prepare data for an Einstein Discovery model?
Trap 1: Remove all records with missing values in any field.
Einstein Discovery can handle missing values.
Trap 2: Select exactly 10 predictor fields manually.
Einstein can automatically select features.
Trap 3: Create a separate dataset for training and validation.
Einstein automatically splits data.
- A
Remove all records with missing values in any field.
Why wrong: Einstein Discovery can handle missing values.
- B
Select exactly 10 predictor fields manually.
Why wrong: Einstein can automatically select features.
- C
Ensure the data is stored in a Salesforce object or a connected data source.
Einstein Discovery requires data accessible via Salesforce.
- D
Create a separate dataset for training and validation.
Why wrong: Einstein automatically splits data.
- E
Define the outcome field that the model will predict.
The outcome is essential for supervised learning.
A sales manager wants to automatically prioritize leads based on their likelihood to convert. Which Einstein feature should the admin enable?
Trap 1: Einstein Activity Capture
Activity Capture logs emails and meetings but does not score leads.
Trap 2: Einstein Prediction Builder
Prediction Builder creates custom models but requires more setup and is not the standard lead scoring tool.
Trap 3: Einstein Bot
Einstein Bot is for conversational AI, not lead scoring.
- A
Einstein Lead Scoring
Einstein Lead Scoring directly scores leads based on conversion likelihood.
- B
Einstein Activity Capture
Why wrong: Activity Capture logs emails and meetings but does not score leads.
- C
Einstein Prediction Builder
Why wrong: Prediction Builder creates custom models but requires more setup and is not the standard lead scoring tool.
- D
Einstein Bot
Why wrong: Einstein Bot is for conversational AI, not lead scoring.
A marketing team wants to use Einstein Engagement Scoring to prioritize leads. What is the primary input for this AI feature?
Trap 1: Historical conversion data from closed opportunities.
That is used for lead scoring, not engagement.
Trap 2: Lead demographic information like industry and company size.
Demographics are not the primary input.
Trap 3: Social media posts and mentions of the company.
Social media is not part of Einstein Engagement Scoring.
- A
Lead interaction history with emails and web activity.
Engagement is measured by interactions.
- B
Historical conversion data from closed opportunities.
Why wrong: That is used for lead scoring, not engagement.
- C
Lead demographic information like industry and company size.
Why wrong: Demographics are not the primary input.
- D
Social media posts and mentions of the company.
Why wrong: Social media is not part of Einstein Engagement Scoring.
A sales manager wants to use Einstein Activity Capture to log emails automatically. Which prerequisite must be met?
Trap 1: The org must be on Enterprise Edition or higher.
It is available in Professional, Enterprise, and Unlimited editions.
Trap 2: The user must have an Einstein AI license.
Einstein Activity Capture is included in many Salesforce editions.
Trap 3: The user must manually enable email logging in personal settings.
It is configured by an admin, not the user.
- A
The org must be on Enterprise Edition or higher.
Why wrong: It is available in Professional, Enterprise, and Unlimited editions.
- B
The user's email must be hosted on a supported platform (Gmail, Outlook).
Einstein Activity Capture integrates with supported email providers.
- C
The user must have an Einstein AI license.
Why wrong: Einstein Activity Capture is included in many Salesforce editions.
- D
The user must manually enable email logging in personal settings.
Why wrong: It is configured by an admin, not the user.
A nonprofit uses Einstein Vision to classify images of disaster areas. What is the primary benefit of using AI for this task?
Trap 1: It requires less training data than manual methods.
AI typically needs substantial data.
Trap 2: It eliminates all classification errors.
AI models are not perfect.
Trap 3: It can only classify images of specific disaster types.
Models can be trained for various types.
- A
It requires less training data than manual methods.
Why wrong: AI typically needs substantial data.
- B
It eliminates all classification errors.
Why wrong: AI models are not perfect.
- C
It reduces manual effort and speeds up damage assessment.
Automation increases efficiency.
- D
It can only classify images of specific disaster types.
Why wrong: Models can be trained for various types.
A company deploys Einstein Recommendation Builder on its e-commerce site. The recommendations are not personalized. What is the most likely cause?
Trap 1: The company did not hire a data scientist to tune the model.
Einstein Recommendation Builder is designed for business users.
Trap 2: The recommendation engine is not syncing in real-time with the…
Real-time sync is not necessary for basic personalization.
Trap 3: The product catalog is too large for the model to process.
The model can handle large catalogs.
- A
The model has not been trained with enough user behavior data.
Personalization requires sufficient historical data.
- B
The company did not hire a data scientist to tune the model.
Why wrong: Einstein Recommendation Builder is designed for business users.
- C
The recommendation engine is not syncing in real-time with the website.
Why wrong: Real-time sync is not necessary for basic personalization.
- D
The product catalog is too large for the model to process.
Why wrong: The model can handle large catalogs.
A sales team is using Einstein Lead Scoring, but the scores for new leads seem inconsistent and not reflecting recent conversion patterns. The admin checks the model and finds it was trained three months ago. Which action should the admin take to improve model accuracy?
Trap 1: Manually override the lead scores for a sample of leads.
Manual overrides are not a best practice for improving model accuracy.
Trap 2: Increase the field history retention period for lead fields.
Field history retention does not retrain the model.
Trap 3: Adjust field-level security to allow the model to access more…
Field-level security does not affect model training.
- A
Retrain the Einstein Lead Scoring model with the latest lead data.
Retraining with recent data improves model accuracy.
- B
Manually override the lead scores for a sample of leads.
Why wrong: Manual overrides are not a best practice for improving model accuracy.
- C
Increase the field history retention period for lead fields.
Why wrong: Field history retention does not retrain the model.
- D
Adjust field-level security to allow the model to access more fields.
Why wrong: Field-level security does not affect model training.
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?
Trap 1: Einstein Case Classification
Case Classification automatically categorizes cases, but does not handle escalation.
Trap 2: Einstein Reply Recommendations
Reply Recommendations suggest pre-written responses.
Trap 3: Einstein Article Recommendations
Article Recommendations suggest knowledge articles to agents.
- A
Einstein Case Classification
Why wrong: Case Classification automatically categorizes cases, but does not handle escalation.
- B
Einstein Reply Recommendations
Why wrong: Reply Recommendations suggest pre-written responses.
- C
Omni-Channel Flow
Omni-Channel Flow can route unresolved bot conversations to live agents.
- D
Einstein Article Recommendations
Why wrong: Article Recommendations suggest knowledge articles to agents.
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?
Trap 1: Run a holdout test to check prediction accuracy.
Accuracy test does not directly address bias.
Trap 2: Retrain the model with balanced data.
Retraining is a fix, not a validation step.
Trap 3: Review the model's confidence intervals.
Confidence intervals measure uncertainty, not bias.
- A
Run a holdout test to check prediction accuracy.
Why wrong: Accuracy test does not directly address bias.
- B
Retrain the model with balanced data.
Why wrong: Retraining is a fix, not a validation step.
- C
Review the model's confidence intervals.
Why wrong: Confidence intervals measure uncertainty, not bias.
- D
Analyze the distribution of scores across industry segments.
This reveals if certain groups are systematically scored lower.
A credit scoring AI uses 50 features including zip code, age, and income. The model has high accuracy but denies credit disproportionately to a protected group. An audit reveals that zip code is a proxy for race. What is the best course of action?
Trap 1: Remove zip code from the feature set and retrain.
Other features may still correlate with race.
Trap 2: Keep zip code but add a fairness penalty to the loss function.
Retaining proxy may still cause disparate impact.
Trap 3: Increase transparency by publishing the model's decision criteria.
Transparency does not correct bias.
- A
Remove zip code from the feature set and retrain.
Why wrong: Other features may still correlate with race.
- B
Replace zip code with more relevant non-discriminatory features and retrain with fairness constraints.
Targeted feature engineering and fairness constraints mitigate bias.
- C
Keep zip code but add a fairness penalty to the loss function.
Why wrong: Retaining proxy may still cause disparate impact.
- D
Increase transparency by publishing the model's decision criteria.
Why wrong: Transparency does not correct bias.
A company deploys an AI recommender system that personalizes content. The system is trained on user click data. After deployment, the company notices that the system increasingly recommends sensationalist content, leading to user polarization. Which principle is being violated?
Trap 1: Accuracy
The system may be accurate in predicting clicks.
Trap 2: Privacy
Privacy may not be violated if data is properly handled.
Trap 3: Transparency
Transparency alone does not prevent harm.
- A
Accuracy
Why wrong: The system may be accurate in predicting clicks.
- B
Privacy
Why wrong: Privacy may not be violated if data is properly handled.
- C
Beneficence
The system should promote well-being and avoid harm.
- D
Transparency
Why wrong: Transparency alone does not prevent harm.
An AI Associate reviews the bot configuration and test results. Which action best addresses the ethical issue?
Exhibit
Refer to the exhibit. ``` Einstein Bot Configuration: - Bot Name: CustomerSupportBot - Language: English - Sentiment Model: Default (English) - Fallback: Route to human agent - Intent Classification: Custom trained on 10,000 English utterances Test Results: - English utterances: 95% accuracy - Spanish utterances: 60% accuracy, 30% routed to fallback ```
Trap 1: Increase the fallback threshold for all languages.
This would increase fallback for English too, not fair.
Trap 2: Collect more data from Spanish-speaking customers and retrain the…
Retraining English model on Spanish data will not fix English model's language limitation.
Trap 3: Disable sentiment analysis for non-English conversations.
Disabling loses functionality; better to support.
- A
Increase the fallback threshold for all languages.
Why wrong: This would increase fallback for English too, not fair.
- B
Collect more data from Spanish-speaking customers and retrain the English model.
Why wrong: Retraining English model on Spanish data will not fix English model's language limitation.
- C
Disable sentiment analysis for non-English conversations.
Why wrong: Disabling loses functionality; better to support.
- D
Add Spanish language support with separate sentiment model and intents.
This directly addresses the language gap.
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?
Trap 1: Remove race and age features entirely to ensure fairness.
Age may be clinically relevant; removal could reduce accuracy unfairly.
Trap 2: Replace age with an age group bucket to reduce granularity.
This does not address bias from race; still problematic.
Trap 3: Use the model as is because predictions are accurate.
Accuracy does not justify ethical or legal risk.
- A
Retain the features but monitor for disparate impact and ensure compliance with regulations.
Ethical AI allows use if monitored and regulated.
- B
Remove race and age features entirely to ensure fairness.
Why wrong: Age may be clinically relevant; removal could reduce accuracy unfairly.
- C
Replace age with an age group bucket to reduce granularity.
Why wrong: This does not address bias from race; still problematic.
- D
Use the model as is because predictions are accurate.
Why wrong: Accuracy does not justify ethical or legal risk.
An AI system recommends job candidates to recruiters. The system was trained on resumes of past successful hires, most of whom were male. As a result, it consistently ranks female candidates lower. What is the most appropriate mitigation?
Trap 1: Add a post-processing adjustment to increase female candidates'…
Post-processing may not address model's internal bias.
Trap 2: Accept the bias as a reflection of historical data.
Historical bias should be actively mitigated, not accepted.
Trap 3: Remove the gender feature from the model.
Removing the feature does not prevent correlation with other features.
- A
Re-sample the training data to include more female candidates and use fairness-aware algorithms.
Balancing data and using fairness techniques reduce bias.
- B
Add a post-processing adjustment to increase female candidates' scores.
Why wrong: Post-processing may not address model's internal bias.
- C
Accept the bias as a reflection of historical data.
Why wrong: Historical bias should be actively mitigated, not accepted.
- D
Remove the gender feature from the model.
Why wrong: Removing the feature does not prevent correlation with other features.
Refer to the exhibit. What is the most likely cause of the fairness issue?
Exhibit
Refer to the exhibit. ``` Model: Churn Predictor v2 Training Data: 80% male, 20% female Accuracy: 85% overall, 90% male, 60% female Fairness Metric: Equal Opportunity Difference = 0.3 ```
Trap 1: The model overfits to the male group.
Overfitting is not indicated by lower accuracy on minority.
Trap 2: The overall accuracy is too low.
Overall accuracy is 85%, which is acceptable.
Trap 3: The model is inherently biased against females.
Bias stems from data, not model itself.
- A
The model overfits to the male group.
Why wrong: Overfitting is not indicated by lower accuracy on minority.
- B
The training data is imbalanced, causing the model to perform better on the majority group.
Imbalanced data leads to unequal performance.
- C
The overall accuracy is too low.
Why wrong: Overall accuracy is 85%, which is acceptable.
- D
The model is inherently biased against females.
Why wrong: Bias stems from data, not model itself.
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?
Trap 1: The data stream object is not a standard Salesforce object.
Custom objects can be used as data streams.
Trap 2: The data stream is not activated for identity resolution.
Activation is for using the data, not for unification.
Trap 3: The data source is not from Salesforce, so it cannot be unified.
Data Cloud supports multiple external sources.
- A
The data stream object is not a standard Salesforce object.
Why wrong: Custom objects can be used as data streams.
- B
The data stream is not activated for identity resolution.
Why wrong: Activation is for using the data, not for unification.
- C
The data source is not from Salesforce, so it cannot be unified.
Why wrong: Data Cloud supports multiple external sources.
- D
The reconciliation rule is not configured for the data source.
Reconciliation rules are needed to match records across sources.
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?
Trap 1: Remove the field from the model to reduce complexity.
Removing an important feature reduces model accuracy.
Trap 2: Create interaction terms between Case_Origin and other fields.
Einstein Discovery automatically detects interactions.
Trap 3: Increase the data quality threshold for Case_Origin records.
Data quality thresholds are set before training.
- A
Remove the field from the model to reduce complexity.
Why wrong: Removing an important feature reduces model accuracy.
- B
Create interaction terms between Case_Origin and other fields.
Why wrong: Einstein Discovery automatically detects interactions.
- C
Increase the data quality threshold for Case_Origin records.
Why wrong: Data quality thresholds are set before training.
- D
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?
Trap 1: Event data from the website tracked via Google Analytics.
Event data must be brought into Salesforce to be used.
Trap 2: Static profile fields like customer age and location.
These do not reflect recent behavior.
Trap 3: Historical data from a data warehouse updated daily.
Daily updates may not be timely enough for real-time personalization.
- A
Event data from the website tracked via Google Analytics.
Why wrong: Event data must be brought into Salesforce to be used.
- B
Streaming data from Data Cloud that includes recent website interactions.
Data Cloud can ingest streaming events and make them available for real-time decisions.
- C
Static profile fields like customer age and location.
Why wrong: These do not reflect recent behavior.
- D
Historical data from a data warehouse updated daily.
Why wrong: Daily updates may not be timely enough for real-time personalization.
A data scientist is building a predictive model for customer churn using Salesforce data. The dataset has 20 features, and the target variable is highly imbalanced (5% churn, 95% non-churn). Which technique should be applied to handle the class imbalance before training?
Trap 1: Apply Principal Component Analysis (PCA) for dimensionality…
PCA does not handle class imbalance.
Trap 2: Create interaction features between existing variables
Interaction features do not fix imbalance.
Trap 3: Use accuracy as the evaluation metric
Accuracy is misleading for imbalanced data.
- A
Apply Principal Component Analysis (PCA) for dimensionality reduction
Why wrong: PCA does not handle class imbalance.
- B
Create interaction features between existing variables
Why wrong: Interaction features do not fix imbalance.
- C
Use accuracy as the evaluation metric
Why wrong: Accuracy is misleading for imbalanced data.
- D
Use Synthetic Minority Over-sampling Technique (SMOTE)
SMOTE creates synthetic examples of the minority class.
A company is implementing Einstein Prediction Builder to predict whether a support case will escalate. Which TWO data preparation steps should the admin take to improve model accuracy?
Trap 1: Include as many fields as possible to provide more context
Including irrelevant fields can reduce model accuracy.
Trap 2: Encrypt all fields containing personally identifiable information
Encryption is a security measure, not a data preparation step for model accuracy.
Trap 3: Exclude cases that were closed without escalation
Excluding non-escalated cases would remove the negative examples needed for training.
- A
Include as many fields as possible to provide more context
Why wrong: Including irrelevant fields can reduce model accuracy.
- B
Ensure missing values are handled appropriately (e.g., imputed or excluded)
Missing values can bias the model; proper handling improves accuracy.
- C
Encrypt all fields containing personally identifiable information
Why wrong: Encryption is a security measure, not a data preparation step for model accuracy.
- D
Exclude cases that were closed without escalation
Why wrong: Excluding non-escalated cases would remove the negative examples needed for training.
- E
Remove fields that have a one-to-one relationship with the outcome
Fields like Case Number or Created Date are unique and not predictive.
A Salesforce admin notices that Einstein Account Scoring is not generating scores for all accounts. Some accounts have no score even though they meet the data requirements. What is the most likely cause?
Trap 1: Einstein features are not enabled in the org
Einstein is enabled by default for Professional and Enterprise editions with Sales Cloud.
Trap 2: Users do not have the 'View Scores' permission
Permission affects visibility, not score generation.
Trap 3: The accounts lack custom fields for scoring
Scoring uses standard and custom fields; lack of custom fields doesn't prevent scoring.
- A
The org has fewer than 100 account records
Einstein requires a minimum data volume (e.g., 100 records) to generate scores.
- B
Einstein features are not enabled in the org
Why wrong: Einstein is enabled by default for Professional and Enterprise editions with Sales Cloud.
- C
Users do not have the 'View Scores' permission
Why wrong: Permission affects visibility, not score generation.
- D
The accounts lack custom fields for scoring
Why wrong: Scoring uses standard and custom fields; lack of custom fields doesn't prevent scoring.
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