AI Associate Practice Test 34 — 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.

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

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The source object has a validation rule.

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The data flow schedule is incorrect.

Question 2

After applying a log transformation to a numeric feature, an Einstein model’s performance dropped significantly. What is the most likely cause?

Accepted Answer

The feature contained zero or negative values. Log transformation is undefined for zero or negative values because log(0) is negative infinity and log of a negative number is not a real number. In Salesforce Einstein, numeric features with such invalid transformed values can cause the model to fail or produce erratic results, leading to a significant drop in performance. This is the most likely cause given the symptom described.

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The data volume was reduced by the transformation

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The feature was normally distributed after transformation

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The transformation introduced multicollinearity with other features

Question 3

Refer to the exhibit. A data access policy is defined for a customer data set. Which statement best describes this policy?

Accepted Answer

Only users in the EU region can read customer data, with emails and phones partially masked.. Option B is correct because the policy shown in the exhibit applies a row-level security filter that restricts access to customer data based on the user's region, and a column-level masking rule that partially masks email and phone fields. Only users whose region attribute is set to 'EU' can view the rows, and even then, the email and phone columns are obfuscated (e.g., j***@example.com, ***-***-1234). This matches the behavior of a data masking policy combined with a regional access filter, which is a common pattern in data platforms like Snowflake or AWS Lake Formation.

Answer

All users can read customer data.

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All users can read customer data but only those in the EU can see unmasked emails.

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Only users in the EU region can read customer data, with emails and phones fully hidden.

Question 4

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.

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Increase the data refresh frequency.

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

Question 5

An organization uses Salesforce Data Cloud to unify customer data from multiple sources. They want to ensure that data lineage is tracked for AI models. Which practice supports data lineage?

Accepted Answer

Maintain metadata that records source, transformations, and dependencies.. Maintaining metadata that records source, transformations, and dependencies is the correct practice because data lineage for AI models requires a complete audit trail of where data originated, how it was transformed, and its dependencies. In Salesforce Data Cloud, this metadata is captured through the Data Catalog and Data Lineage feature, which tracks the flow of data from source objects through calculated insights and segments to AI model inputs, ensuring transparency and reproducibility.

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Use data partitioning to improve query performance.

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Implement role-based access control on datasets.

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Regularly run data profiling to check completeness.

Question 6

An organization is preparing data for Einstein Next Best Action. They have multiple action types (discounts, product suggestions, content). Which data model approach best ensures accurate recommendations?

Accepted Answer

Use a single model that includes all action types in the training data.. Option D is correct because Einstein Next Best Action is designed to learn from all action types simultaneously within a single model. By including all action types (discounts, product suggestions, content) in the training data, the model can capture cross-action patterns and relative effectiveness, leading to more accurate and contextually relevant recommendations. A unified model avoids fragmentation and ensures consistent scoring across actions.

Answer

Train a separate model per customer segment and then merge.

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Create a separate model for each action type and combine results manually.

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Build an ensemble of models and average their outputs.

Question 7

Refer to the exhibit. The data pipeline is failing. What is the most likely cause?

Accepted Answer

Schema mismatch between source and target.. Option D is correct because a schema mismatch between source and target is the most common cause of pipeline failures in data integration workflows. When the source data structure (e.g., column names, data types, or nested fields) does not match the target schema, the pipeline cannot map or transform the data correctly, leading to errors during ingestion or transformation stages. This is especially relevant in tools like Apache NiFi, AWS Glue, or Azure Data Factory, where schema validation is enforced at runtime.

Answer

Network timeout.

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Missing required field in source data.

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Insufficient memory.

Question 8

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.

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The data transform has a recursive formula.

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The target field expects an integer but source provides null.

Question 9

Refer to the exhibit. A data file for click-through model training has the above content. Which data quality issue is most critical to address before training?

Accepted Answer

Missing value in the Conversions column for the third row. Option B is correct because missing values in the Conversions column directly impact the supervised learning target variable. If the label (conversion) is missing for a training instance, the model cannot learn the correct mapping from features to outcome, leading to biased or incomplete training. This is a critical data quality issue that must be addressed before training, typically via imputation or row removal.

Answer

The header row is missing a column name for the last field

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Inconsistent date formats across rows

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Clicks column is an integer but may need scaling

Question 10

A machine learning team is preparing a dataset for a supervised learning task. They have 100,000 labeled samples. Which data preparation step is essential before splitting into train/test sets?

Accepted Answer

Shuffle the dataset randomly.. Option C is correct because shuffling the dataset randomly before splitting into train/test sets ensures that the data distribution is similar across both subsets. Without shuffling, the split might inadvertently separate ordered or grouped data (e.g., time-series or batches), leading to biased model evaluation. This step is essential for supervised learning to prevent data leakage and ensure the test set is representative of the overall population.

Answer

Normalize all features to the same scale.

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Remove all outliers from the dataset.

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Visualize the data distribution for each feature.

Question 11

Refer to the exhibit. A data analyst runs a profile on a dataset and sees these statistics. Based on best practices, which action should be taken first?

Accepted Answer

Remove the 200 duplicate records. Option B is correct because duplicate records introduce bias and redundancy, leading to overfitting or skewed model performance. Removing duplicates is a standard first step in data preprocessing to ensure data integrity before handling missing values or outliers. In the context of the AI Associate exam, best practices prioritize deduplication early in the data cleaning pipeline.

Answer

Impute the 500 missing values with the mean

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Remove the 50 outliers in the Amount field

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Skip all preprocessing and train the model directly

Question 12

Refer to the exhibit. A data transformation configuration is shown. Which of the following describes the outcome of applying this transformation?

Accepted Answer

'color' is one-hot encoded into multiple binary columns; 'price' and 'weight' are standardized to have mean 0 and variance 1.. Option C is correct because the transformation configuration applies a one-hot encoder to the 'color' categorical column, creating multiple binary columns, and applies a standard scaler to the 'price' and 'weight' numerical columns, centering them to mean 0 and scaling to unit variance. This is a common preprocessing pipeline that handles mixed data types appropriately.

Answer

The transformation is invalid because one-hot encoding cannot be combined with scaling.

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Only 'color' is transformed; 'price' and 'weight' are unchanged.

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'color' is scaled to [0,1] and 'price', 'weight' are one-hot encoded.

Question 13

Before training an Einstein Prediction model, a data analyst must perform data quality checks. Which THREE checks are most critical?

Accepted Answer

Remove duplicate records that could cause data leakage. Option B is correct because duplicate records can cause data leakage by allowing the model to see the same or highly similar data in both training and validation splits, leading to overfitting and inflated performance metrics. Removing duplicates ensures that the model generalizes to unseen data rather than memorizing repeated instances.

Answer

Confirm that label distribution matches the target baseline

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Ensure all features follow a normal distribution

Question 14

During data transformation, a data scientist applies one-hot encoding to a categorical feature with 50 unique values. The resulting dataset has 50 new columns. What is a potential drawback of this transformation?

Accepted Answer

High cardinality leading to sparse data and overfitting. One-hot encoding a categorical feature with 50 unique values creates 50 binary columns, each representing one category. This high cardinality leads to a very sparse matrix (most entries are 0), which can cause the model to overfit by learning noise from rare categories, especially when the dataset is not large enough to support such dimensionality.

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Reduction in training time

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Increased interpretability of the model

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Loss of ordinal information in categories

Question 15

Which THREE types of data sources are commonly integrated into Salesforce Data Cloud for AI use cases?

Accepted Answer

Third-party demographic data. Option A is correct because Salesforce Data Cloud can ingest third-party demographic data from external sources (e.g., data enrichment providers) to enrich customer profiles. This data, when combined with first-party data, enables AI models to generate more accurate predictions and segmentations. Data Cloud’s Data Streams and Data Lake objects support structured ingestion of such external datasets.

Answer

Model training logs

Answer

Data transformation scripts