AI Associate AI Fundamentals Practice Test 2 — 15 Questions

Question 1

A global manufacturing company uses Sales Cloud and has implemented Einstein Opportunity Scoring to prioritize deals. The scoring model was trained on historical data and initially performed well. Over the past month, the scores have become less accurate, with many high-scoring opportunities not closing and some low-scoring ones closing. The admin notices that the sales team has been using a new discounting strategy that heavily influences deal outcomes. The admin wants to improve model performance without manual intervention. Which action should the admin take?

Accepted Answer

Retrain the Einstein Opportunity Scoring model with the latest opportunity data including discount information.. Option B is correct because retraining the Einstein Opportunity Scoring model with the latest opportunity data, including discount information, allows the machine learning model to automatically learn the new patterns introduced by the sales team's discounting strategy. This aligns with the AI Associate principle that models must be retrained on current data to maintain accuracy when business processes change, without requiring manual intervention.

Answer

Manually adjust the field weights for discount-related fields in the model.

Answer

Run a data quality report to identify and clean missing discount data.

Answer

Create a custom field for discount percentage and add it to the model.

Question 2

A nonprofit organization uses Salesforce to manage donor relationships. They have implemented Einstein Prediction Builder to predict which donors are likely to upgrade their donation level in the next 90 days. The model was built using a custom object "Donation" with fields like Amount, Frequency, and Campaign. After deployment, the predictions seem random and do not correlate with donor engagement. The admin suspects the model is not trained on enough records. The organization has 500 donors with at least two donations each. What should the admin do to improve the model?

Accepted Answer

Ensure that at least 500 records exist where the donor actually upgraded, and retrain the model.. Option C is correct because Einstein Prediction Builder requires a minimum number of positive outcome records (upgrade events) to train a reliable model. With only 500 donors and likely far fewer upgrades, the model lacks sufficient signal. Ensuring at least 500 actual upgrade records provides the necessary positive examples for the algorithm to learn meaningful patterns, reducing randomness in predictions.

Answer

Increase the prediction window from 90 to 180 days to capture more upgrade events.

Answer

Use a different field as the prediction outcome, such as 'donation amount increase'.

Answer

Add more fields to the model, such as donor age and geographic location.

Question 3

A marketing manager uses Einstein recommendations on their website, but customers are receiving suggestions for products they already purchased. What is the most likely cause?

Accepted Answer

The recommendation model is not filtering out previously purchased items.. C is correct because the most likely cause is that the recommendation model is not configured to exclude previously purchased items. Einstein Recommendations uses customer purchase history to personalize suggestions, but if the model's filtering logic does not explicitly remove items the customer has already bought, those items will continue to appear in the recommendations. This is a common oversight in model configuration rather than a data sync or algorithm type issue.

Answer

The product catalog is not updated with purchase history.

Answer

The model is using real-time browsing data that includes past purchases.

Answer

The recommendations are based on collaborative filtering without personalization.

Question 4

A company wants to use Einstein Prediction Builder to predict customer churn. They have a dataset with 10,000 records and 50 features. What is the primary consideration for model accuracy?

Accepted Answer

The dataset must be balanced between churned and non-churned customers.. Option C is correct because Einstein Prediction Builder uses automated machine learning (AutoML) to train models, and class imbalance is a critical factor that directly impacts model accuracy. If the dataset is highly skewed (e.g., 95% non-churned, 5% churned), the model may achieve high accuracy by simply predicting the majority class, but it will fail to identify actual churners. Einstein Prediction Builder includes built-in handling for imbalanced data, but the user must ensure the dataset is reasonably balanced or use techniques like oversampling to improve predictive performance.

Answer

The dataset size is too small for reliable predictions.

Answer

All features must be numerical and normalized.

Answer

The model needs to be retrained daily.

Question 5

A Salesforce admin notices that Einstein Case Classification in Service Cloud is suggesting categories that frequently require manual correction. Which action should the admin take first?

Accepted Answer

Provide feedback by correcting the predictions to improve the model.. Option B is correct because Einstein models learn from user feedback; correcting predictions helps improve accuracy. Option A is wrong because routing before fixing classification may propagate errors. Option C is wrong because adding categories without tuning may degrade performance. Option D is wrong because starting over is unnecessary.

Answer

Enable Einstein Case Routing to automatically route based on predicted categories.

Answer

Delete the training data and start over with a new model.

Answer

Increase the number of categories in the classification model.

Question 6

A sales representative uses Einstein Activity Capture to log emails automatically. However, some critical emails are not being captured. What is the most likely reason?

Accepted Answer

The sender or recipient is not a Salesforce user with a license.. Einstein Activity Capture relies on Salesforce user licenses to associate email activities with records. If the sender or recipient is not a licensed Salesforce user, the system cannot link the email to a user identity, causing it to be skipped during capture. This is the most common reason for missing emails because the feature is designed to log activities only for licensed users.

Answer

The emails were sent from a non-Outlook or Gmail client.

Answer

The Einstein Activity Capture feature is disabled for that specific user.

Answer

The emails contain attachments that exceed the size limit.

Question 7

A data scientist is evaluating the performance of an Einstein Discovery model. They observe that the model has high accuracy but low precision for a specific prediction class. What does this indicate?

Accepted Answer

The model correctly predicts most instances but has many false positives for that class.. High accuracy with low precision for a specific class indicates that while the model correctly classifies the majority of instances overall, it produces a high number of false positives for that class. Precision measures the proportion of positive identifications that were actually correct, so low precision means many of the predicted positive cases are false alarms. In Einstein Discovery, this trade-off is critical when optimizing for business outcomes where false positives are costly.

Answer

The model is overfitted to the training data.

Answer

The model correctly predicts most instances but has many false negatives for that class.

Answer

The model rarely predicts that class, leading to high accuracy.

Question 8

A company wants to deploy an Einstein Prediction Builder model to predict lead conversion within 30 days. They have historical data from the past 12 months. Which data preprocessing step is most critical to ensure the model learns correctly?

Accepted Answer

Ensure the target variable is computed based on conversion status at exactly 30 days from creation.. Option D is correct because the target variable for a time-based prediction model like Einstein Prediction Builder must be computed at a precise, consistent point in time—in this case, exactly 30 days from lead creation. If the target is computed at varying intervals, the model will learn incorrect patterns, as it cannot distinguish between leads that converted at 31 days versus those that never converted, leading to biased or invalid predictions.

Answer

Normalize all numerical features to a 0-1 range.

Answer

Remove leads that converted after 30 days.

Answer

Include only leads that were assigned to a sales rep.

Question 9

A Salesforce admin is troubleshooting Einstein Object Detection in a custom object. The model is predicting values, but the confidence score remains below 80% for most predictions. What should the admin investigate first?

Accepted Answer

The model training data size and distribution of values.. Low confidence scores in Einstein Object Detection typically indicate that the model has insufficient or imbalanced training data. Einstein AI requires a minimum number of labeled records (e.g., at least 100 per class) and a balanced distribution across predicted values to learn effectively. Without adequate data size and diversity, the model cannot generalize well, resulting in confidence scores below 80%.

Answer

The number of fields used as input features.

Answer

The API version used in the integration.

Answer

The permission set for the Einstein AI feature.

Question 10

An organization uses Einstein Search to power a portal's search functionality. Users report that search results are not ranking relevant documents highly. Which configuration change is most likely to improve relevance?

Accepted Answer

Tune the search algorithm by promoting frequently accessed content.. Option B is correct because search relevance is improved by promoting popular or authoritative content. Option A is wrong because more fields may add noise. Option C is wrong because refresh frequency affects freshness but not ranking algorithm. Option D is wrong because stemming and synonyms help recall but not necessarily ranking.

Answer

Enable stemming and synonyms.

Answer

Increase the number of indexed fields.

Answer

Decrease the index refresh frequency.

Question 11

A nonprofit organization wants to use Einstein Bots to handle inquiries on their website. They are concerned that the bot may give incorrect or insensitive responses. Which feature should they prioritize to maintain trustworthy AI?

Accepted Answer

Implement a fallback to human agent for uncertain queries.. Option C is correct because implementing a fallback to a human agent for uncertain queries directly addresses the risk of incorrect or insensitive responses by ensuring that when the bot cannot confidently answer, the conversation is escalated to a human. This aligns with the principle of maintaining trustworthy AI, as it prevents the bot from generating potentially harmful or inaccurate responses and provides a safety net for complex or sensitive inquiries.

Answer

Use a larger training dataset from generic internet sources.

Answer

Allow the bot to generate responses only from pre-authored articles.

Answer

Disable sentiment analysis to avoid misinterpretation.

Question 12

A sales team is implementing Einstein Lead Scoring. Which two actions should they take to ensure the model is effective? (Choose 2)

Accepted Answer

Ensure the training data includes both converted and unconverted leads.. Option D is correct because Einstein Lead Scoring requires training data that includes both converted and unconverted leads to build a predictive model that can distinguish between leads likely to convert and those that are not. Without unconverted leads, the model cannot learn the negative patterns, leading to biased predictions and poor accuracy.

Answer

Include only demographic data for scoring.

Answer

Set a fixed score threshold for all users.

Answer

Disable the model for low-volume leads.

Question 13

A company is deploying Einstein Prediction Builder to predict equipment failure. Which three considerations are essential for building an accurate prediction model? (Choose 3)

Accepted Answer

Missing values in features should be handled appropriately.. Option A is correct because missing values in features can introduce bias or cause errors in the predictive model. Einstein Prediction Builder automatically handles missing data through imputation, but understanding how missing values are treated is essential for model accuracy, as inappropriate handling can distort relationships between features and the target outcome.

Answer

All input features must be numerical.

Answer

The model should be retrained only once after initial deployment.

Question 14

A data analyst is reviewing an Einstein Discovery story and notices that one input feature has a very high influence on the predicted outcome. Which two conclusions are justified based on this observation? (Choose 2)

Accepted Answer

The feature could be a surrogate for other correlated features.. Option D is correct because a feature with high influence in an Einstein Discovery story may be acting as a proxy for other correlated features, meaning its apparent importance could be due to shared variance with other predictors. This is a known phenomenon in machine learning where collinearity can inflate a feature's influence score without it being uniquely causal.

Answer

The feature has a causal relationship with the outcome.

Answer

Removing the feature will significantly improve model performance.

Answer

The model is likely overfitted to that feature.

Question 15

Refer to the exhibit. A Salesforce admin evaluates an Einstein Prediction Builder model for customer churn. What should be the admin's primary concern based on the exhibit?

Accepted Answer

The model has detected data drift, indicating the training data may no longer represent current patterns.. The exhibit shows a data drift alert from Einstein Prediction Builder, which indicates that the statistical properties of the input data have changed over time. This is the admin's primary concern because a model trained on outdated patterns will produce unreliable predictions, even if its accuracy or AUC were initially high. Data drift directly undermines the model's validity in production.

Answer

The model accuracy is too low for production use.

Answer

The AUC is low, so the model is not better than random.

Answer

The model uses too few features to be reliable.