AI Associate Practice Test 11 — 15 Questions

Question 1

A marketing team wants to use Einstein Recommendations to personalize product offers on their e-commerce site. They have a dataset of 50,000 customers with purchase history. However, 40% of customers have no purchase history (new registrations). The model performs well for returning customers but gives generic recommendations for new ones. The team wants to improve recommendations for new customers. What data preparation step should they take?

Accepted Answer

Impute missing purchase history with the average purchase frequency across all customers.. Imputing missing purchase data with a sensible default (e.g., average purchase frequency) gives the model signal for new customers, improving recommendations without discarding data.

Answer

Remove all customers with missing purchase history from the training set.

Answer

Assign a random purchase frequency to each new customer to add variety.

Answer

Use only customers with complete purchase history to train a more accurate model.

Question 2

A data scientist needs to feed customer interaction data into Einstein Discovery for predictive analysis. Which data format is required?

Accepted Answer

CSV. Option C is correct because Einstein Discovery typically requires data in CSV format. Option A is wrong because JSON is not the standard input format for Einstein Discovery. Option B is wrong because XML is not commonly used. Option D is wrong because Parquet is a columnar storage format not directly supported.

Answer

XML

Answer

Parquet

Answer

JSON

Question 3

To ensure AI model fairness and avoid biased outcomes, which practice is most critical when preparing training data?

Accepted Answer

Use balanced training data. Option C is correct because using balanced training data across different groups helps prevent bias. Option A is wrong because adding more features can introduce bias. Option B is wrong because increasing model complexity may overfit. Option D is wrong because using only recent data may not represent all demographics.

Answer

Use only recent data

Answer

Increase model complexity

Answer

Use more features

Question 4

Which TWO are common data quality issues that negatively impact AI model performance? (Choose two.)

Accepted Answer

Missing values. Options A and B are correct. Missing values and outliers can skew model training. Option C is wrong because high dimensionality is more about feature count than quality. Option D is wrong because multicollinearity affects interpretability but not necessarily quality. Option E is wrong because data volume alone is not a quality issue.

Answer

Multicollinearity

Answer

Data volume

Answer

High dimensionality

Question 5

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 6

After deploying an AI model in Salesforce, the data scientist notices high accuracy on the training set but poor accuracy on new incoming data. What is this phenomenon called?

Accepted Answer

Overfitting. Option A is correct because overfitting occurs when the model learns noise instead of the underlying pattern, performing well on training but poorly on new data. Option B is wrong because underfitting would show poor performance on both. Option C is wrong because data leakage gives unrealistically high performance on training but does not cause poor generalization. Option D is wrong because concept drift refers to changing data distribution over time, not immediate poor generalization.

Answer

Data leakage

Answer

Underfitting

Answer

Concept drift

Question 7

Which THREE are key considerations for data privacy when using AI models that process customer data? (Choose three.)

Accepted Answer

Limit data access to authorized personnel only. Options A, B, and D are correct. Anonymizing PII protects individual identity, obtaining consent ensures legal compliance, and limiting data access reduces exposure risk. Option C is wrong because storing data indefinitely violates privacy principles. Option E, while good practice, is more about security than privacy specifically, and not always mandatory.

Answer

Store data indefinitely for future use

Answer

Encrypt data in transit and at rest

Question 8

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 9

A financial services company uses Salesforce AI to detect fraudulent transactions. The dataset has 1 million legitimate transactions and only 1,000 fraudulent ones. The model trained with default parameters achieves 99.9% accuracy but identifies no fraud (precision and recall of 0). The data scientist wants to maximize fraud detection (recall) while minimizing false positives. Which approach is most effective?

Accepted Answer

Use SMOTE to generate synthetic fraud samples to balance the dataset.. With extreme imbalance, oversampling the minority class (e.g., SMOTE) generates synthetic fraud examples, helping the model learn fraud patterns and improve recall without discarding legitimate data.

Answer

Increase the weight of the majority class in the loss function.

Answer

Train multiple models on different random subsets and average predictions.

Answer

Use a simpler model to avoid overfitting on the majority class.

Question 10

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 11

A company plans to train an AI model using data from Salesforce CRM and an external marketing automation platform. What is the first step to unify these data sources in Data Cloud?

Accepted Answer

Define a Data Model that maps fields from both sources to a unified customer object. Option A is correct because creating a data model that maps fields from both sources to a common object ensures consistency. Option B is wrong because data streams come after the model. Option C is wrong because Calculated Insights are for aggregations. Option D is wrong because data transformations are applied later.

Answer

Create two separate Data Streams to bring data in

Answer

Build a Calculated Insight to merge the data

Answer

Set up a Data Transformation to blend the sources

Question 12

A retailer's AI model for recommendation is producing poor results. Analysis shows that the customer entity has many duplicate records with slight variations. Which Data Cloud feature should be used to address this?

Accepted Answer

Create a Data Transformation to merge duplicates using rules. Option C is correct because Data Transformations can apply fuzzy matching to merge records. Option A is wrong because ignoring duplicates leaves poor data. Option B is wrong because Data Actions trigger external actions, not dedup. Option D is wrong because increasing stream frequency does not fix existing duplicates.

Answer

Increase the Data Stream frequency to get fresher data

Answer

Ignore the duplicates and use all records as-is

Answer

Set up a Data Action to deduplicate at the source

Question 13

A financial institution must ensure that customer data used for AI models does not expose personally identifiable information (PII) to unauthorized users. Which Data Cloud feature should be applied to the data model?

Accepted Answer

Apply data masking and field-level security on sensitive fields. Option B is correct because data masking and field-level security can obscure PII. Option A is wrong because deleting fields removes valuable predictors. Option C is wrong because user permissions alone are insufficient for field-level protection. Option D is wrong because aggregations don't hide underlying raw data.

Answer

Delete PII fields from the data model

Answer

Use Calculated Insights to aggregate sensitive data only

Answer

Rely on user permissions to restrict access to the entire object

Question 14

A data architect notices that a Data Stream from an external ERP system is failing intermittently with schema mismatch errors. The ERP team says the schema changes occasionally. What is the most effective long-term solution?

Accepted Answer

Implement Data Stream schema validation and flexible mapping. Option C is correct because adding schema validation and mapping checks can detect and handle changes gracefully. Option A is wrong because increasing retries doesn't fix the root cause. Option B is wrong because manual intervention is not sustainable. Option D is wrong because using a different primary key won't address schema changes.

Answer

Use a different primary key for the data model

Answer

Increase the number of retries for the Data Stream

Answer

Ask the ERP team to manually notify of changes

Question 15

A manufacturer wants to improve demand forecasting by enriching its CRM orders with external demographic data. The external data is available via a SOAP API. How should the data architect implement this?

Accepted Answer

Configure a Data Action to call the API on a schedule or trigger. Option D is correct because a Data Action can call an external API and store the response. Option A is wrong because Data Transform only works on data already in Data Cloud. Option B is wrong because Data Streams are for continuous ingest, not on-demand enrichment. Option C is wrong because a Calculated Insight cannot fetch external data.

Answer

Use a Data Transform to pull the external data

Answer

Set up a Data Stream to continuously ingest the external API

Answer

Create a Calculated Insight to reference the API