AI Associate Data for AI Practice Test 9 — 15 Questions

Question 1

A Salesforce admin is preparing a dataset for Einstein Prediction Builder. The dataset contains a field "Income" with many missing values. The admin wants to minimize bias in the model. What is the best practice?

Accepted Answer

Review the pattern of missingness and document reasons, then decide on imputation. Option C is correct because reviewing missingness patterns and documenting reasons helps uncover systemic biases. Option A (fill with mean) may distort relationships; Option B (delete rows) reduces sample and may introduce selection bias; Option D (replace with 0) is arbitrary and can skew results.

Answer

Delete all rows where Income is missing

Answer

Fill missing values with the mean of Income

Answer

Replace missing values with 0

Question 2

When training an Einstein Discovery model, which data type is not supported as a predictor field?

Accepted Answer

Multi-select picklist. Option A is correct because multi-select picklists are not supported as predictors in Einstein Discovery. Numeric, picklist, and date fields are supported.

Answer

Numeric

Answer

Picklist

Answer

Date

Question 3

A data scientist notices that a Salesforce Einstein model's performance degrades over time. The model was trained on data from the last year. What is the most likely cause?

Accepted Answer

Concept drift. Option D is correct because concept drift occurs when the underlying data distribution changes over time, causing model degradation. Overfitting and underfitting are training issues, not time-dependent; data leakage would cause overestimation initially.

Answer

Underfitting

Answer

Data leakage

Answer

Overfitting

Question 4

To integrate external data into Salesforce for AI, which tool is recommended by Salesforce for building data pipelines?

Accepted Answer

Salesforce Data Pipelines. Option B is correct because Salesforce Data Pipelines is the recommended tool for creating and managing data integration workflows for AI and analytics. The other options are not designed for this purpose.

Answer

Salesforce Connect

Answer

Data Export Service

Answer

Apex Data Loader

Question 5

In Salesforce CRM Analytics (formerly Einstein Analytics), what is the primary purpose of a dataset?

Accepted Answer

To prepare data for AI and analytics. In Salesforce CRM Analytics, a dataset is the foundational data structure that transforms raw data into an optimized, columnar format for analytics and AI features like Einstein Discovery. It is created by extracting, cleaning, and aggregating data from sources such as Salesforce objects or external connectors, enabling efficient querying, dashboarding, and machine learning model training. This makes option A correct because the primary purpose is to prepare data specifically for AI and analytics workloads.

Answer

To run SQL queries directly

Answer

To store raw, unprocessed records

Answer

To create dashboards only

Question 6

A company wants to use Einstein Next Best Action but needs to ensure data privacy. What is the required step for anonymizing customer data in Data Pipelines?

Accepted Answer

Use the Data Mask transformation in Data Pipelines. Option A is correct because Data Pipelines includes a Data Mask transformation that can anonymize PII fields. Permission Sets control access but do not anonymize; sandbox scrambling is for testing only; Einstein does not automatically anonymize data.

Answer

Create a sandbox with scrambled data

Answer

Rely on Einstein's built-in anonymization

Answer

Use Permission Sets to hide fields from users

Question 7

While building a prediction model in Einstein Studio, the system warns about "high cardinality" for a categorical field. What should the admin do?

Accepted Answer

Use frequency encoding or binning to reduce cardinality. Option C is correct because high cardinality (many unique values) can hurt model performance. Frequency encoding or binning reduces cardinality while retaining information. Removing the field or converting to numeric may lose information; increasing model complexity is not recommended.

Answer

Convert the field to a numeric type

Answer

Remove the field from the model

Answer

Increase the model complexity by adding more trees

Question 8

Which Salesforce feature automatically flags data quality issues before training an AI model?

Accepted Answer

Data Manager (Data Prep Assistant). Option B is correct because the Data Manager (or Data Prep Assistant) in Salesforce provides data quality checks and recommendations. Validation Rules are for entry-time; Duplicate Matching is for deduplication; Flow is for automation.

Answer

Flow

Answer

Validation Rules

Answer

Duplicate Matching

Question 9

A data integration specialist is using Data Pipelines to combine Salesforce data with an external CSV file. The CSV has a header row but some rows have extra commas, causing parsing errors. What should the specialist do?

Accepted Answer

Use a Data Transform recipe to clean the data before ingestion. Option B is correct because a Data Transform recipe can handle malformed rows by stripping extra commas or parsing with a delimiter that accommodates quoted fields. Manual editing is inefficient; rejecting the whole file loses data; increasing timeout does not fix parsing.

Answer

Edit the CSV manually

Answer

Increase the pipeline timeout

Answer

Reject the entire file and request a corrected version

Question 10

A Salesforce admin is reviewing data sources for Einstein Recommendation Builder. Which two data types are required for training? (Choose two.)

Accepted Answer

User-item interactions. Options B and D are correct. Item metadata (B) and user-item interactions (D) are essential for recommendation models. User profile data (A) is optional; external product prices (C) are not required; sales reports (E) are not a standard input.

Answer

User profile data

Answer

Sales reports

Answer

External product prices

Question 11

Which three practices help maintain data quality for AI models in Salesforce? (Choose three.)

Accepted Answer

Monitor data freshness with Data Check. Option A is correct because Data Check in Salesforce monitors data freshness by tracking when records were last updated, ensuring that AI models use current data. Stale data can degrade model accuracy, so this practice directly supports data quality for AI.

Answer

Disable duplicate matching rules for faster load

Answer

Use Excel for manual data updates

Question 12

When preparing data for Einstein Next Best Action, which two aspects must be considered for compliance with data privacy regulations? (Choose two.)

Accepted Answer

Consent management. Options A and D are correct. Consent management (A) ensures legal basis; data masking of PII (D) protects sensitive data. Data compression (B) and color coding (C) are not privacy measures; indexing speed (E) is performance related.

Answer

Data compression

Answer

Color coding fields in the dataset

Answer

Indexing speed for real-time recommendations

Question 13

Refer to the exhibit. In the JSON configuration above, which data preparation step could introduce bias?

Accepted Answer

Excluding rows with missing Stage. Option B is correct because excluding rows with missing Stage (a picklist that may correlate with outcome) can introduce selection bias. Filling with median (A) or default (C) are common imputation methods; ignoring Description (D) is generally safe as it treats missing as information.

Answer

Ignoring missing Description

Answer

Filling missing Amount with median

Answer

Using default for missing CreatedDate

Question 14

Refer to the exhibit. What is the most likely cause of the pipeline failure?

Accepted Answer

Picklist value does not exist in the target picklist field. Option C is correct because the error explicitly states "value not found in picklist values" for CustomLeadField__c. This indicates a picklist value mismatch. Insufficient permissions (A) would generate a different error; data type mismatch (B) would show conversion error; connection timeout (D) would show timeout.

Answer

Data type mismatch between source and target

Answer

Insufficient permissions to access the field

Answer

Connection timeout during data transfer

Question 15

A global company uses Salesforce Einstein Discovery to predict customer churn. They have a dataset with fields: Customer_Since__c (date), Last_Interaction_Date__c (date), Support_Cases__c (number), Product_Usage__c (percentage), Region__c (picklist), and Churned__c (boolean target). The model was trained and deployed, but predictions show bias against customers in the "EMEA" region. The data scientist notices that in the training data, 80% of EMEA customers are labeled as churned, while only 20% of other regions. Additionally, the Product_Usage__c field has many missing values for EMEA customers. The company wants to retrain the model to reduce bias. What is the best course of action?

Accepted Answer

Preprocess the data to impute missing Product_Usage__c values using region-specific averages, and then rebalance the dataset using stratified sampling. Option D is correct because it addresses both the missing data (impute using region-specific averages to preserve regional patterns) and the class imbalance (stratified sampling ensures balanced representation across regions during training). Option A removes Region, losing valuable information; Option B uses synthetic data which may introduce artificial patterns; Option C only rebalances but does not fix missing data which could still bias the model.

Answer

Oversample EMEA churned customers and undersample non-churned from other regions

Answer

Increase the sample size of EMEA customers by adding synthetic data

Answer

Remove the Region__c field from the model and retrain