AI Associate Data for AI Practice Test 5 — 15 Questions

Question 1

A company is deploying an AI model that recommends next best actions for sales reps. They notice that the model's recommendations are biased towards high-revenue opportunities. Which data-related action can help reduce this bias?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

Oversample the underrepresented segments in the training data. Oversampling underrepresented segments in the training data directly addresses the class imbalance that causes the model to favor high-revenue opportunities. By increasing the frequency of low-revenue examples, the model learns to treat all segments more equally, reducing bias in its recommendations. This is a standard data-level technique for mitigating bias in AI models.

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Use a larger neural network model

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Encrypt the data before training

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Remove all low-revenue opportunities from the training data

Question 2

A Salesforce admin wants to use Einstein Prediction Builder to predict case resolution time. What type of data is most critical for training this model?

Accepted Answer

Historical case records including resolution time. Einstein Prediction Builder requires historical data with known outcomes to train a supervised machine learning model. Historical case records containing actual resolution times provide the labeled examples needed for the model to learn patterns and predict future case resolution times. Without this ground truth data, the model cannot be trained to make accurate predictions.

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Customer satisfaction survey responses

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Product inventory levels

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Employee work schedules

Question 3

During the data preparation phase for an AI model, a data engineer discovers that the 'AnnualRevenue' field contains some negative values. What is the best course of action?

Accepted Answer

Investigate the data source to correct the negative values. Negative revenue values typically indicate data entry errors, system bugs, or incorrect data transformations. The best practice in data preparation is to investigate the source system to understand why negative values were generated and correct them at the origin, ensuring data integrity before any imputation or deletion. Simply deleting or imputing without root-cause analysis can introduce bias or mask underlying data quality issues.

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Delete all records with negative revenue

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Replace negative values with the mean of positive values

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Keep negative values as they might represent returns or refunds

Question 4

Which TWO techniques are commonly used to handle missing values in a dataset for AI training?

Accepted Answer

Deletion of rows with missing values. Deleting rows with missing values is a straightforward technique to handle missing data, especially when the missingness is random and the dataset is large enough that removing a few rows does not significantly impact model performance. This approach avoids introducing bias from imputation methods but can lead to loss of valuable information if too many rows are removed.

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L1 regularization

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One-hot encoding

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Min-max normalization

Question 5

Which THREE factors should be considered when selecting features for a predictive model in Salesforce?

Accepted Answer

Correlation between features to avoid multicollinearity. Multicollinearity occurs when two or more features are highly correlated, which can destabilize model coefficients and reduce interpretability. In Salesforce's predictive models, such as those built with Einstein Discovery, correlated features can inflate variance and lead to unreliable predictions. Avoiding multicollinearity ensures that the model's feature importance estimates are trustworthy and that the model generalizes well to new data.

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Volume of data available for each feature

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Compliance with data privacy regulations

Question 6

Which TWO are common data quality issues that can negatively impact AI model performance?

Accepted Answer

Missing values in critical fields. Missing values in critical fields (Option A) are a common data quality issue because many AI models, particularly those relying on statistical or gradient-based optimization, cannot handle null or NaN inputs without imputation or removal. If missing values are not addressed, the model may learn biased patterns or fail to converge, leading to degraded predictive performance.

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Low model accuracy during validation

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Insufficient storage space for data

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Inconsistent data governance policies

Question 7

A company wants to use its data from Salesforce to train an Einstein AI model. However, they need to exclude records where the customer has opted out of data use. Which field should they configure in the Data Manager?

Accepted Answer

A checkbox field named Data Use Opt-Out. The Data Manager in Einstein AI uses a standard checkbox field named 'Data Use Opt-Out' to identify records that should be excluded from model training. When this checkbox is true, the system automatically filters out those records during data preparation, ensuring compliance with data privacy requirements.

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The Record Type of the object

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A picklist field on the object

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A formula field that evaluates to true

Question 8

A large enterprise is using Einstein Lead Scoring and notices that the model score is not updating for leads created via a web-to-lead form. The leads have all required fields populated. The admin has verified that the model is active and the data source includes the Lead object. What could be causing the score to remain static?

Accepted Answer

The model has not scored enough leads to start scoring new ones. Einstein Lead Scoring requires a minimum number of scored leads (typically 500) before it begins scoring new leads. Until that threshold is met, the model remains in a 'training' or 'pending' state and will not update scores for any leads, including those from web-to-lead forms. The admin has confirmed the model is active and the Lead object is included, so the most likely cause is that the model has not yet processed enough leads to start scoring.

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The data source excludes leads created by web-to-lead

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Web-to-lead leads are not supported by Einstein AI

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The model is not yet activated

Question 9

A company wants to use Einstein Article Recommendations to surface relevant knowledge articles to its support agents. What two data components are required to set up this feature?

Accepted Answer

Knowledge Article View event data and Case records. Einstein Article Recommendations uses historical Knowledge Article View event data to understand which articles agents have found useful in the past, and Case records to provide context about the current issue. By analyzing patterns between case attributes and article views, the AI can predict and surface the most relevant articles for a given case. Without both data components, the recommendation engine cannot learn the association between case details and article usefulness.

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Email-to-case logs and Knowledge Article feedback

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Knowledge Article categories and Case priority

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Community user activity and Knowledge Article ratings

Question 10

A company is using Einstein Discovery to predict customer churn. The model was created six months ago and has been making predictions. Recently, the model's accuracy has dropped significantly. The data scientist confirms that the data schema has not changed. What is the most likely reason for the drop in accuracy?

Accepted Answer

The model is suffering from concept drift. Concept drift occurs when the statistical properties of the target variable change over time, causing the model's predictions to become less accurate even though the data schema remains unchanged. In Einstein Discovery, models are trained on historical data, and if the underlying patterns of customer churn evolve (e.g., due to market shifts or new competitor behavior), the model's learned relationships become stale. Since the data schema is confirmed unchanged, concept drift is the most likely cause of the accuracy drop.

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The data source is not being refreshed daily

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The model's features have become irrelevant

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The model needs to be retrained weekly instead of monthly

Question 11

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?

Accepted Answer

Ensure missing values are handled appropriately (e.g., imputed or excluded). Correct: Removing redundant fields (like record IDs) and handling missing values are crucial for model accuracy. Option A is wrong because more fields can introduce noise. Option C is wrong because data encryption is about security, not accuracy. Option D is wrong because all cases should be included to represent the full pattern.

Answer

Include as many fields as possible to provide more context

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Encrypt all fields containing personally identifiable information

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Exclude cases that were closed without escalation

Question 12

You are a Salesforce AI Specialist at a mid-sized manufacturing company. The company uses Einstein Lead Scoring to prioritize leads. The model was trained on historical lead data and has been in production for three months. Recently, the sales team reports that high-scoring leads are not converting as expected. You investigate and find that the model's data source includes leads from the past 18 months. However, six months ago, the company changed its lead qualification process: they started requiring a demo before scoring leads as 'qualified.' As a result, the definition of a converted lead changed. What is the best course of action to improve model performance?

Accepted Answer

Retrain the model using only leads from the last six months after the process change. The change in lead qualification process six months ago introduced a data distribution shift (concept drift), making older leads no longer representative of the current conversion behavior. Retraining the model on only the last six months of data aligns the training set with the new definition of a 'converted lead,' allowing Einstein Lead Scoring to learn the updated patterns and improve prediction accuracy.

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Manually adjust the model's prediction threshold to account for the new process

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Remove the 'Demo Scheduled' field from the model to avoid bias

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Add more historical leads from before the process change to increase data volume

Question 13

You are an admin at a financial services firm. The firm wants to use Einstein Next Best Action to offer personalized product recommendations to customers on its service portal. The data includes customer profiles, transaction history, and support case history. The Einstein Next Best Action strategy is configured with a recommendation that shows a 'Savings Account' offer to customers who have a checking account. However, the recommendation is not appearing for any customers. You check the Data Flow and see that the 'Account' object data is flowing correctly. The recommendation's filter condition is: AND( Has_Checking_Account__c = true, Age__c > 18 ). You verify that many customers meet these conditions. What is the most likely reason the recommendation is not appearing?

Accepted Answer

The recommendation is not activated or published. The most likely reason the recommendation is not appearing is that it has not been activated or published. In Einstein Next Best Action, recommendations must be explicitly activated or published to become available for serving to customers; configuration alone does not make them live. Since the data flow is correct and the filter conditions are valid, the missing activation step is the typical cause of a recommendation not showing.

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The 'Account' object is not supported by Einstein Next Best Action

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The customer data is not being refreshed in real time

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The filter condition is syntactically incorrect

Question 14

You are a data scientist at a retail company. The company uses Einstein Discovery to analyze customer purchase patterns. The model is built on a dataset of 50,000 transactions. The model's R-squared is 0.85, but the predictions for new customers are consistently off by a large margin. The data includes features like 'Customer Age', 'Income', 'Previous Purchases', and 'Product Category'. The model was trained on data from the past two years. However, six months ago, the company launched a new loyalty program that significantly changed purchasing behavior. You suspect the model is not generalizing to new customers. What should you do to validate your hypothesis?

Accepted Answer

Create a holdout set of transactions from the last six months and compare model performance on it vs. older data. Creating a holdout set of transactions from the last six months directly tests whether the model's performance has degraded due to the loyalty program's impact on purchasing behavior. By comparing the R-squared or other metrics on this recent holdout set versus older data, you can quantify the drop in predictive accuracy and confirm that the model fails to generalize to the new data distribution. This approach is a standard method for detecting concept drift in machine learning models, especially when external changes (like a loyalty program) alter the underlying patterns.

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Exclude new customers from the dataset entirely

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Increase the training data size to include older transactions

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Remove the 'Product Category' feature to simplify the model

Question 15

A company is preparing customer data for a predictive model. They notice that many records have missing values for the 'annual income' field. Which approach is best to handle this issue while minimizing bias?

Accepted Answer

Use model-based imputation considering other features.. Model-based imputation (Option B) is best because it uses relationships between features (e.g., education, job role) to predict missing 'annual income' values, preserving data distribution and minimizing bias. This approach avoids the distortion caused by simple mean/zero imputation and retains sample size better than deletion.

Answer

Remove all records with missing values.

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Replace missing values with the mean.

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Set missing values to zero.