AI Associate Data for AI Practice Test 4 — 15 Questions

Question 1

A data engineer is troubleshooting a predictive model that stopped updating. The data flow from Data Cloud shows 'Data Transform Failed' with error: 'Field Amount cannot be null'. What is the most likely cause?

Accepted Answer

The target field in the model requires a non-null value but source data has nulls.. The error 'Field Amount cannot be null' indicates that the target field in the predictive model is configured to require a non-null value. When the data flow attempts to write records with null Amount values into that field, the transform fails. This is a common schema constraint mismatch where the source data contains nulls that violate the target field's nullability requirement.

Answer

The data transform includes a filter that removes records with null Amount.

Answer

The source object has a validation rule.

Answer

The data flow schedule is incorrect.

Question 2

A company is preparing data for Einstein Article Recommendation. Which data source is most appropriate for training the model?

Accepted Answer

Historical article view and click data.. Einstein Article Recommendation uses supervised machine learning to predict which articles users are likely to find relevant. The model must be trained on historical user engagement signals—specifically article view and click data—to learn patterns of relevance. Without this behavioral data, the model cannot establish a correlation between user actions and article content.

Answer

Org metadata.

Answer

System debug logs.

Answer

User profile data only.

Question 3

A retail company uses Einstein Next Best Action with customer data from Data Cloud. The recommendations are not personalized. The admin checks the data quality dashboard and finds that the 'Customer_Profile' object has 40% records with missing 'PreferredChannel' field. What is the best course of action?

Accepted Answer

Impute the missing values using the mode of the field.. Option B is correct because imputing missing values using the mode (most frequent value) of the 'PreferredChannel' field is a standard data preprocessing technique that preserves the dataset size and statistical distribution. In Einstein Next Best Action, missing categorical data can degrade model personalization, and mode imputation is a simple, effective way to handle this without losing records or altering the model structure.

Answer

Remove the field from the model.

Answer

Increase the data refresh frequency.

Answer

Train the model with only records that have non-null PreferredChannel.

Question 4

An admin created a data stream to bring external customer data into Data Cloud for Einstein. The data stream fails with error 'Schema mismatch: expected 10 fields, got 8'. What is the likely cause?

Accepted Answer

The data stream definition expects more fields than the source provides.. The error 'Schema mismatch: expected 10 fields, got 8' indicates that the data stream definition in Data Cloud is configured to map 10 fields from the source, but the actual source file or API response only provides 8 fields. This mismatch occurs when the schema defined in the data stream does not match the source schema, typically because the source has fewer columns than expected. Option D correctly identifies this as the likely cause.

Answer

The data flow has a filter that drops fields.

Answer

The target object has validation rules.

Answer

The source file has extra columns.

Question 5

A company wants to use Einstein Reply Recommendations in Service Cloud. What data is required to train the model?

Accepted Answer

Case comments and chat transcripts.. Einstein Reply Recommendations in Service Cloud uses historical service interactions to suggest relevant replies to agents. The model is trained on case comments and chat transcripts because these contain the natural language patterns and resolutions that agents actually use in real-time service conversations, enabling the AI to learn effective response strategies.

Answer

Knowledge articles only.

Answer

Email templates.

Answer

Historical email replies and customer satisfaction ratings.

Question 6

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?

Accepted Answer

Group them into higher-level categories (e.g., region).. Grouping high-cardinality categories into broader categories reduces overfitting and improves model stability.

Answer

Encode them as one-hot vectors.

Answer

Exclude them from the model.

Answer

Use the raw values without transformation.

Question 7

A company uses Einstein Prediction Builder to predict customer churn. The model's accuracy is low. The admin reviews the training data and notices that only 2% of records are churned. What should the admin do to improve the model?

Accepted Answer

Use oversampling techniques.. Option C is correct because when a dataset has severe class imbalance (only 2% churned records), the model becomes biased toward predicting the majority class (non-churned), leading to low accuracy despite high apparent performance. Oversampling techniques, such as SMOTE or random oversampling, artificially increase the number of churned records in the training set to balance the classes, allowing Einstein Prediction Builder to learn patterns for the minority class more effectively.

Answer

Remove the churned records.

Answer

Increase the amount of training data.

Answer

Change the prediction field.

Question 8

A system administrator receives an error when running a Data Cloud data transform: 'Row-level security settings are preventing access to the source data.' The admin has appropriate permissions. What is the most likely cause?

Accepted Answer

The source object has sharing rules that restrict access for the data transform's running user.. Option D is correct because Data Cloud data transforms run under a specific running user context, and row-level security (RLS) settings on the source object can restrict that user's access to rows, even if the admin has broad permissions. The error indicates that the running user lacks visibility to certain source data rows due to sharing rules or RLS policies, which is a common cause of this specific error message.

Answer

The target object has field-level security.

Answer

The data stream is scheduled during maintenance.

Answer

The data transform is set to run as the admin's default user.

Question 9

A marketer wants to use Einstein Segment Creation to build a segment for a campaign. Which data source can be used?

Accepted Answer

Data Cloud unified profile data.. Einstein Segment Creation works with Data Cloud unified profiles.

Answer

Standard report snapshots.

Answer

External web analytics.

Answer

Einstein Activity Capture data.

Question 10

A data analyst is evaluating data quality for an Einstein model. Which TWO dimensions are most critical for model accuracy?

Accepted Answer

Accuracy. Completeness (no missing values) and accuracy (correct values) are fundamental to model performance.

Answer

Uniqueness

Answer

Consistency

Answer

Timeliness

Question 11

A company is ingesting data from multiple sources into Data Cloud for Einstein. Which THREE data preparation steps should be performed?

Accepted Answer

Normalization. Normalization is correct because Data Cloud requires data from multiple sources to be transformed into a consistent format, such as standardizing date formats, units, or naming conventions, to ensure the data can be unified and analyzed effectively. This step is critical for Einstein AI models to process data without inconsistencies that could skew predictions or insights.

Answer

Encryption

Answer

Data labeling

Question 12

A data scientist is using Einstein Discovery to analyze sales data. The model results show a high correlation between two predictor variables. Which TWO actions should the data scientist take?

Accepted Answer

Combine them into a single feature.. Removing one correlated variable or combining them reduces multicollinearity.

Answer

Apply regularization.

Answer

Include both to capture more information.

Answer

Increase the sample size.

Question 13

Refer to the exhibit. What effect does this masking policy have on the data used for training an Einstein model?

Accepted Answer

SSN and CreditCard fields are partially masked, showing only the last four characters.. The masking policy in Einstein applies a partial mask to sensitive fields like SSN and CreditCard, showing only the last four characters while obscuring the rest. This ensures that the data used for training retains its structural utility for model learning without exposing full sensitive values, which is why option D is correct.

Answer

Only SSN is masked.

Answer

SSN and CreditCard fields are encrypted.

Answer

SSN and CreditCard fields are completely removed from training data.

Question 14

Refer to the exhibit. What is the most likely cause of this error?

Accepted Answer

A division formula in the data transform is dividing by a field that contains zero values.. The error shown in the exhibit is a division-by-zero runtime error, which occurs when a formula in a data transform attempts to divide a value by a field that contains zero. In Pega, data transforms execute field-level calculations, and if a divisor field holds a zero, the system throws a 'Divide by zero' exception. Option D correctly identifies this as the most likely cause because the error message explicitly indicates a division operation failed due to a zero divisor.

Answer

The source data is missing required fields.

Answer

The data transform has a recursive formula.

Answer

The target field expects an integer but source provides null.

Question 15

Refer to the exhibit. What data quality issue does the exhibit reveal?

Accepted Answer

The Fall Clearance campaign has no response data.. The Fall Clearance campaign has zero non-null responses, indicating all response data is missing for that campaign.

Answer

The Summer Sale campaign has duplicate records.

Answer

The query syntax is incorrect.

Answer

The data is not normalized.