AI Associate Practice Test 8 — 10 Questions

Question 1

A large enterprise needs to integrate data from Salesforce CRM, an external ERP, and marketing automation to train an AI model for cross-sell recommendations. Which data storage strategy is most aligned with Salesforce's AI capabilities?

Accepted Answer

Use Salesforce Data Cloud to unify the datasets. Salesforce Data Cloud is designed to unify data from multiple sources into a single platform for AI and analytics. Exporting to a data lake adds complexity, using only Salesforce objects limits data scope, and storing flat files lacks governance.

Answer

Use only Salesforce CRM data and ignore external sources

Answer

Store each source separately in Data Cloud and train models on each

Answer

Export all data to an external data lake and build a custom model

Question 2

A company is using customer support tickets to train a model for auto-classifying issues. The dataset includes fields like 'Case Title', 'Description', 'Product', and 'Customer Name'. Which privacy concern is most critical to address before training?

Accepted Answer

Anonymize personal identifiable information (PII) in Description and Title. Anonymizing PII in the text fields is critical to avoid exposing customer information in model artifacts or predictions. Session tokens are irrelevant, and case numbers are not PII. Opt-in is a legal requirement but not directly about data preparation for AI.

Answer

Encrypt session tokens used in the support system

Answer

Remove all case numbers to prevent data leakage

Answer

Ensure all customers have opted in before using their data

Question 3

A company uses Einstein Forecasting for revenue prediction. The historical data shows seasonal spikes every quarter. The model consistently underestimates peak periods. What is the best data preparation step to improve accuracy?

Accepted Answer

Add a 'quarter' index field (1-4) to the dataset.. Einstein Forecasting can detect seasonality if the data contains enough history and a seasonality marker. Adding a 'quarter' feature explicitly helps the model capture recurring patterns.

Answer

Increase the forecast horizon to 12 months.

Answer

Remove the spike data points as outliers.

Answer

Use only the last 6 months of data to reduce noise.

Question 4

A company wants to train an AI model to predict customer churn using historical data that contains many missing values. What is the best practice for handling missing data?

Accepted Answer

Impute missing values using mean or median.. Option C is correct because imputing missing values using mean or median is a standard practice that preserves the dataset size and statistical properties, allowing the AI model to learn from all available features without introducing bias from data removal. This approach is particularly effective for numerical features in customer churn prediction, where missing values are often random and imputation maintains the distribution for algorithms like logistic regression or gradient boosting.

Answer

Use only features without missing values.

Answer

Ignore missing values as they do not affect AI training.

Answer

Remove all records with missing values.

Question 5

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 6

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 7

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 8

A news outlet wants to build an AI model that predicts article popularity using real-time social media mentions. Which data source type should they use to ingest tweets?

Accepted Answer

Data Stream with Ingestion API connector. Option A is correct because Data Stream with Ingestion API from Twitter allows real-time streaming. Option B is wrong because Data Lake Objects are for batch. Option C is wrong because Calculated Insights are for aggregates. Option D is wrong because Data Transformations process existing data.

Answer

Calculated Insight

Answer

Data Transform

Answer

Data Lake Object

Question 9

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 10

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