AI Associate Data for AI Practice Test 11 — 15 Questions

Question 1

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. Concept drift (A) is the correct answer because the statistical properties of the target variable or its relationship with input features change over time, causing a model trained on last year's data to become less accurate as real-world patterns evolve. This is a classic scenario in production ML where temporal data shifts degrade model performance even though the model was valid at training time. Underfitting (B) refers to a model too simple to capture patterns in the training data, which would show poor performance from the start rather than degrading over time. Data leakage (C) is a training-time flaw where future or target information contaminates features, typically inflating validation metrics rather than causing gradual production decay. Overfitting (D) means the model memorized training data and fails to generalize, but that would also manifest immediately on new data, not as a slow degradation tied to temporal change.

Answer

Underfitting

Answer

Data leakage

Answer

Overfitting

Question 2

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

Accepted Answer

Salesforce Data Pipelines. Salesforce Data Pipelines (option C) is the recommended tool for building data pipelines to integrate external data into Salesforce for AI, as it provides a modern, low-code solution for ingesting, transforming, and harmonizing data from external sources. It is purpose-built for data integration and preparation, aligning with Salesforce's AI and Data Cloud strategy. Salesforce Connect (option A) is for real-time external object virtualization, not for building pipelines. Data Export Service (option B) is for extracting data out of Salesforce, and Apex Data Loader (option D) is a bulk data loading utility, neither of which is designed for building external data pipelines.

Answer

Salesforce Connect

Answer

Data Export Service

Answer

Apex Data Loader

Question 3

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 4

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. The correct answer is D: Use the Data Mask transformation in Data Pipelines. Data Pipelines provides a built-in Data Mask transformation that lets you obfuscate or anonymize sensitive customer fields (such as names, emails, or IDs) as data flows through the pipeline, which is the required step to protect privacy before feeding data into Einstein Next Best Action. Option A is wrong because a sandbox with scrambled data is a separate Salesforce environment feature, not a Data Pipelines anonymization step. Option B is wrong because Einstein does not automatically anonymize source data on its own. Option C is wrong because Permission Sets control user access to fields, but they do not anonymize or mask the underlying 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 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. The correct option is B: use frequency encoding or binning to reduce cardinality. High cardinality means a categorical field has too many distinct values, which can cause overfitting and poor model performance, so frequency encoding (replacing categories with their occurrence counts) or binning (grouping rare categories together) reduces the number of distinct levels while preserving useful signal. Option A is wrong because converting a categorical field to numeric type does not reduce the number of distinct values and can mislead the model into treating category codes as ordered quantities. Option C is overly aggressive since the field may still carry predictive value, and option D is unrelated because adding more trees increases model complexity rather than addressing the cardinality issue.

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

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

Accepted Answer

Data Manager (Data Prep Assistant). Data Manager (Data Prep Assistant) is correct because it is the Salesforce Data Cloud feature that profiles and analyzes ingested data, automatically surfacing data quality issues such as missing values, outliers, and inconsistencies before the data is used to train an AI model. Its built-in quality metrics and remediation suggestions let users clean and prepare data prior to model training. Flow is an automation tool for business processes, not a data quality profiler. Validation Rules enforce field-level data integrity at record save time in Salesforce objects, but they do not scan datasets for quality issues before AI training. Duplicate Matching identifies duplicate records but does not comprehensively flag broader data quality problems like nulls or format inconsistencies.

Answer

Flow

Answer

Validation Rules

Answer

Duplicate Matching

Question 7

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. The correct option is A: Use a Data Transform recipe to clean the data before ingestion. In Data Pipelines, a Data Transform recipe can apply transformations such as parsing, filtering, and field-level cleanup to handle malformed CSV rows with extra commas before the data is loaded, which directly resolves the parsing errors without manual intervention. Option B is not scalable or automatable and does not address the pipeline's ingestion logic. Option C is irrelevant because timeout settings do not fix CSV parsing errors. Option D is overly disruptive and unnecessary since the data can be cleaned programmatically.

Answer

Edit the CSV manually

Answer

Increase the pipeline timeout

Answer

Reject the entire file and request a corrected version

Question 8

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. Option B (User-item interactions) is correct because Einstein Recommendation Builder is a collaborative-filtering engine that learns latent relationships between users and items from historical interaction events such as views, clicks, purchases, and ratings; without these interaction records there is no signal to train the model. Option E (Item metadata) is correct because the item catalog with attributes (for example product name, category, description, and other item-level fields) is required to define the items being recommended and to enrich and filter recommendations. Option A (User profile data) is not required — profile attributes are optional enrichment, not a training prerequisite. Option C (Sales reports) is not required — reports are a presentation artifact, not a required training data source. Option D (External product prices) is not required — pricing is optional business data and is not part of the required training inputs.

Answer

User profile data

Answer

Sales reports

Answer

External product prices

Question 9

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 Data Cloud lets you monitor data freshness and other quality metrics so AI models are trained on current, reliable data. Option D is correct because scheduling regular data audits proactively identifies stale, incomplete, or inconsistent records before they degrade model accuracy. Option E is correct because validation rules on critical fields enforce data integrity at the point of entry, preventing invalid or malformed values from reaching AI models. Option B does not belong because disabling duplicate matching rules increases duplicate records and harms data quality rather than improving it. Option C does not belong because manual Excel updates are error-prone, unscalable, and bypass Salesforce validation and governance controls.

Answer

Disable duplicate matching rules for faster load

Answer

Use Excel for manual data updates

Question 10

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. Data privacy regulations such as GDPR and CCPA require that organizations track and honor the consent status of individuals before using their data to generate recommendations, so Einstein Next Best Action must respect consent flags when evaluating recommendation strategies. Option E (Data masking of personally identifiable information (PII)) is correct because PII must be protected or obfuscated before it is processed or displayed in recommendation outputs, ensuring that sensitive fields like names, emails, or phone numbers are not exposed to unauthorized users. The unmarked options do not belong: data compression (A) is a storage/performance technique with no bearing on privacy compliance, color coding fields (B) is purely a visualization choice, and indexing speed for real-time recommendations (C) is a performance consideration rather than a regulatory requirement.

Answer

Data compression

Answer

Color coding fields in the dataset

Answer

Indexing speed for real-time recommendations

Question 11

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

Accepted Answer

Excluding rows with missing Stage. Excluding rows with missing Stage is correct because dropping records based on a field that may be systematically absent for certain groups can remove underrepresented cases and skew the dataset, introducing selection bias. Ignoring missing Description is less likely to bias the target because Description is often free text and not essential to the model. Filling missing Amount with the median is a standard imputation that preserves row count and reduces bias from data loss. Using a default for missing CreatedDate is also an imputation choice and does not inherently exclude any subgroup.

Answer

Ignoring missing Description

Answer

Filling missing Amount with median

Answer

Using default for missing CreatedDate

Question 12

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. The question cannot be answered as written because the referenced exhibit is missing. The stem says 'Refer to the exhibit' but no exhibit (error message, log, or screenshot) is included, so there is no evidence that the failure is caused by a picklist value missing from the target picklist field. Without the exhibit, none of the options can be confirmed as the most likely cause. The exhibit must be supplied (e.g., a validation error such as 'bad value for restricted picklist field' or 'picklist value does not exist') before option D can be justified.

Answer

Data type mismatch between source and target

Answer

Insufficient permissions to access the field

Answer

Connection timeout during data transfer

Question 13

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 the bias stems from two data-quality issues: missing Product_Usage__c values concentrated in EMEA and severe class imbalance across regions. Imputing missing values with region-specific averages preserves regional patterns instead of distorting them with a global mean, and stratified sampling ensures each region's churned/non-churned proportions are represented fairly during retraining. This directly addresses the root causes of the biased predictions rather than masking them. Option A is wrong because oversampling/undersampling without fixing the missing values still leaves EMEA records with incomplete features, and it can amplify noise or discard useful data. Option B is wrong because adding synthetic EMEA data without addressing the missing values and imbalance may reinforce the existing bias. Option C is wrong because removing Region__c does not fix the missing Product_Usage__c values and may hide legitimate regional effects rather than reduce bias.

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

Question 14

A marketing agency needs to ingest real-time social media mentions for a sentiment analysis AI model. Which Data Cloud object type should they use to set up the ingestion?

Accepted Answer

Data Stream Object with Event type. The correct option is C, a Data Stream Object with Event type, because real-time social media mentions are time-stamped, event-driven data that must be ingested continuously into Data Cloud, and the Event stream type is designed exactly for high-volume, real-time ingestion of streaming data. Data Stream Objects are the ingestion mechanism in Data Cloud, and the Event type supports streaming sources rather than batch loads. Option A, Data Lake Object, is a storage/mapping target for ingested data, not the ingestion setup itself. Option B, Calculated Insight, is used to compute metrics and aggregations for activation, not to ingest raw data. Option D, Data Transform, is used to transform already-ingested data (e.g., via SQL-based recipes), not to establish the real-time ingestion stream.

Answer

Data Lake Object

Answer

Calculated Insight

Answer

Data Transform

Question 15

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. The correct answer is A: Create a Data Transformation to merge duplicates using rules. In Data Cloud, Data Transformations (specifically the Merge operation within a transformation) allow you to define match rules and consolidation logic to unify duplicate customer records with slight variations into a single golden record, which directly improves the quality of the data feeding the AI recommendation model. Option B does not fit because increasing Data Stream frequency only affects how often data is ingested, not the duplication problem. Option C is wrong because leaving duplicates as-is perpetuates the poor model results. Option D is incorrect because a Data Action triggers downstream workflows or activations and does not perform deduplication, and deduplication at the source is outside Data Cloud's feature set here.

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