AI Associate Data for AI Practice Test 7 — 15 Questions

Question 1

You are an admin at a financial services firm. The firm wants to use Einstein Next Best Action to offer personalized product recommendations to customers on its service portal. The data includes customer profiles, transaction history, and support case history. The Einstein Next Best Action strategy is configured with a recommendation that shows a 'Savings Account' offer to customers who have a checking account. However, the recommendation is not appearing for any customers. You check the Data Flow and see that the 'Account' object data is flowing correctly. The recommendation's filter condition is: AND( Has_Checking_Account__c = true, Age__c > 18 ). You verify that many customers meet these conditions. What is the most likely reason the recommendation is not appearing?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

The recommendation is not activated or published. The most likely reason the recommendation is not appearing is that it has not been activated or published. In Einstein Next Best Action, recommendations must be explicitly activated or published to become available for serving to customers; configuration alone does not make them live. Since the data flow is correct and the filter conditions are valid, the missing activation step is the typical cause of a recommendation not showing.

Answer

The 'Account' object is not supported by Einstein Next Best Action

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The customer data is not being refreshed in real time

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The filter condition is syntactically incorrect

Question 2

You are a data scientist at a retail company. The company uses Einstein Discovery to analyze customer purchase patterns. The model is built on a dataset of 50,000 transactions. The model's R-squared is 0.85, but the predictions for new customers are consistently off by a large margin. The data includes features like 'Customer Age', 'Income', 'Previous Purchases', and 'Product Category'. The model was trained on data from the past two years. However, six months ago, the company launched a new loyalty program that significantly changed purchasing behavior. You suspect the model is not generalizing to new customers. What should you do to validate your hypothesis?

Accepted Answer

Create a holdout set of transactions from the last six months and compare model performance on it vs. older data. Creating a holdout set of transactions from the last six months directly tests whether the model's performance has degraded due to the loyalty program's impact on purchasing behavior. By comparing the R-squared or other metrics on this recent holdout set versus older data, you can quantify the drop in predictive accuracy and confirm that the model fails to generalize to the new data distribution. This approach is a standard method for detecting concept drift in machine learning models, especially when external changes (like a loyalty program) alter the underlying patterns.

Answer

Exclude new customers from the dataset entirely

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Increase the training data size to include older transactions

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Remove the 'Product Category' feature to simplify the model

Question 3

A company is preparing customer data for a predictive model. They notice that many records have missing values for the 'annual income' field. Which approach is best to handle this issue while minimizing bias?

Accepted Answer

Use model-based imputation considering other features.. Model-based imputation (Option B) is best because it uses relationships between features (e.g., education, job role) to predict missing 'annual income' values, preserving data distribution and minimizing bias. This approach avoids the distortion caused by simple mean/zero imputation and retains sample size better than deletion.

Answer

Remove all records with missing values.

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Replace missing values with the mean.

Answer

Set missing values to zero.

Question 4

For a real-time AI application that requires low-latency access to customer interaction data, which storage solution is most appropriate?

Accepted Answer

In-memory data store.. In-memory data stores (e.g., Redis, Memcached) store data in RAM rather than on disk, providing sub-millisecond read/write latencies essential for real-time AI applications that need immediate access to customer interaction data. This eliminates disk I/O bottlenecks and enables high-throughput, low-latency data retrieval for time-sensitive inference or decision-making.

Answer

Flat files on a network drive.

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Relational database with complex joins.

Answer

Data lake with batch processing.

Question 5

A company wants to use customer purchase history to train a recommendation model. Which action is essential to comply with data privacy regulations?

Accepted Answer

Obtain explicit consent from customers.. Data privacy regulations such as GDPR and CCPA require a lawful basis for processing personal data, and explicit consent is a primary lawful basis when using customer purchase history for training a recommendation model. Without obtaining explicit consent, the company would be processing personal data without a valid legal ground, violating regulations that mandate transparency and user control over their data.

Answer

Use only publicly available data.

Answer

Ignore regulations because data is internal.

Answer

Anonymize the data after training.

Question 6

Which data transformation is most appropriate for converting categorical variables into numerical format for a machine learning model?

Accepted Answer

One-hot encoding.. One-hot encoding is the correct transformation because it converts categorical variables into a binary vector representation, where each category becomes a separate column with a 1 or 0. This allows machine learning models to interpret categorical data without implying any ordinal relationship, which is essential for algorithms that rely on numerical distances or linear algebra.

Answer

Normalization.

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Principal component analysis.

Answer

Standardization.

Question 7

Which method is most suitable for ingesting streaming data from IoT sensors into a data lake?

Accepted Answer

Real-time streaming with Apache Kafka.. Apache Kafka is the most suitable option because it is a distributed streaming platform designed for high-throughput, fault-tolerant, real-time data ingestion. IoT sensors generate continuous, high-velocity data streams, and Kafka's publish-subscribe model allows data to be ingested into a data lake with low latency, ensuring near-real-time availability for analytics.

Answer

Copying data via FTP.

Answer

Batch ingestion every 24 hours.

Answer

Manual upload via web interface.

Question 8

A global company needs to ensure that customer data used for AI models complies with multiple regional regulations (GDPR, CCPA, LGPD). Which data governance practice is most effective?

Accepted Answer

Use a unified data catalog with tagging and classification.. The correct option is B: a unified data catalog with tagging and classification is the most effective practice because it provides a centralized inventory of data assets with metadata that identifies which records are subject to GDPR, CCPA, or LGPD, enabling consistent policy enforcement, access control, and auditability across regions. Tagging and classification allow the company to apply region-specific rules to the same data set without duplicating or isolating storage, which is essential for AI models that may consume data across jurisdictions. Option A is overly blunt because applying the strictest regulation globally can impose unnecessary restrictions and does not by itself map data to each regulation's requirements. Option C is insufficient because consolidating data in one warehouse does not provide the metadata, lineage, or classification needed for regulatory compliance. Option D is also inadequate because separate regional management creates fragmented governance, inconsistent policies, and poor visibility for a global AI data supply chain.

Answer

Apply the strictest regulation globally.

Answer

Store all data in a single data warehouse.

Answer

Allow each region to manage its own data separately.

Question 9

Which TWO data preparation steps are critical for ensuring high-quality training data?

Accepted Answer

Removing duplicate records.. Option B (Removing duplicate records) is correct because duplicate rows bias the training set toward repeated examples, causing overfitting and skewing learned distributions, so deduplication is a critical data-cleaning step for high-quality training data. Option D (Handling missing values appropriately) is correct because nulls, NaNs, or empty fields break many algorithms and distort statistics; imputing, dropping, or flagging missing entries ensures the model learns from complete, reliable signals. Option A is wrong because adding noise increases dataset size but degrades label and feature integrity rather than improving quality. Option C is wrong because normalization is a feature-scaling step that helps optimization and convergence but is not strictly critical for data quality, and some algorithms (e.g., tree-based models) do not require it. Option E is wrong because unlabeled data is valid and valuable for semi-supervised and unsupervised training, so restricting to labeled data is not a critical quality requirement.

Answer

Increasing dataset size by adding noise.

Answer

Normalizing all features.

Answer

Using only labeled data.

Question 10

Which TWO considerations are important when labeling data for a supervised learning model?

Accepted Answer

Maintaining consistent guidelines.. Option A (Maintaining consistent guidelines) is correct because supervised learning models learn from the labels they are given, so a documented, uniformly applied labeling rubric ensures that the same input is labeled the same way across the dataset, preventing contradictory training signals that degrade model accuracy. Option B (Labeler expertise) is correct because accurate labels for domain-specific tasks (e.g., medical imaging or legal text) require annotators who understand the subject matter; unqualified labelers introduce systematic errors that the model will learn and reproduce. Option C is wrong because automated labeling is not appropriate for all tasks—it can propagate bias or errors and often needs human verification, especially for nuanced cases. Option D is wrong because inter-labeler agreement is a key quality metric; ignoring it hides ambiguity in the guidelines and reduces label reliability. Option E is wrong because labeling only a small sample limits training data volume and coverage, leading to underfitting and poor generalization.

Answer

Using automated labeling for all tasks.

Answer

Ignoring inter-labeler agreement.

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Labeling only a small sample.

Question 11

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.

Answer

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

Answer

The source object has a validation rule.

Answer

The data flow schedule is incorrect.

Question 12

A company is preparing data for Einstein Article Recommendation. Which data source is most appropriate for training the model?

Accepted Answer

Historical article view and click data.. Einstein Article Recommendation uses supervised machine learning to predict which articles users are likely to find relevant. The model must be trained on historical user engagement signals—specifically article view and click data—to learn patterns of relevance. Without this behavioral data, the model cannot establish a correlation between user actions and article content.

Answer

Org metadata.

Answer

System debug logs.

Answer

User profile data only.

Question 13

An admin created a data stream to bring external customer data into Data Cloud for Einstein. The data stream fails with error 'Schema mismatch: expected 10 fields, got 8'. What is the likely cause?

Accepted Answer

The data stream definition expects more fields than the source provides.. The error 'Schema mismatch: expected 10 fields, got 8' indicates that the data stream definition in Data Cloud is configured to map 10 fields from the source, but the actual source file or API response only provides 8 fields. This mismatch occurs when the schema defined in the data stream does not match the source schema, typically because the source has fewer columns than expected. Option D correctly identifies this as the likely cause.

Answer

The data flow has a filter that drops fields.

Answer

The target object has validation rules.

Answer

The source file has extra columns.

Question 14

A data architect is designing a data model for Einstein Discovery. The data includes categorical variables with high cardinality (e.g., postal codes). What is the best practice to handle such features?

Accepted Answer

Group them into higher-level categories (e.g., region).. High-cardinality categorical variables such as postal codes create sparse, noisy features that can cause overfitting and degrade model performance in Einstein Discovery. Grouping them into higher-level categories (e.g., region, state, or country) reduces cardinality while preserving meaningful geographic signal, which is the recommended best practice. This produces more generalizable models and improves the quality of the story and predictions.

Answer

Encode them as one-hot vectors.

Answer

Exclude them from the model.

Answer

Use the raw values without transformation.

Question 15

A system administrator receives an error when running a Data Cloud data transform: 'Row-level security settings are preventing access to the source data.' The admin has appropriate permissions. What is the most likely cause?

Accepted Answer

The source object has sharing rules that restrict access for the data transform's running user.. Data Cloud data transforms run under a specific running user context, and row-level security (RLS) settings on the source object can restrict that user's access to rows, even if the admin has broad permissions. The error indicates that the running user lacks visibility to certain source data rows due to sharing rules or RLS policies, which is a common cause of this specific error message.

Answer

The target object has field-level security.

Answer

The data stream is scheduled during maintenance.

Answer

The data transform is set to run as the admin's default user.