AI Associate Data for AI Practice Test 8 — 15 Questions

Question 1

A marketer wants to use Einstein Segment Creation to build a segment for a campaign. Which data source can be used?

Accepted Answer

Data Cloud unified profile data.. Einstein Segment Creation uses Data Cloud unified profile data as its primary data source to build segments. This allows marketers to leverage unified customer profiles for precise targeting.

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Standard report snapshots.

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External web analytics.

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Einstein Activity Capture data.

Question 2

A data analyst is evaluating data quality for an Einstein model. Which TWO dimensions are most critical for model accuracy?

Accepted Answer

Accuracy. Accuracy (B) is critical because Einstein models learn from the values in the training data, so if records contain incorrect or mislabeled values, the model will learn and predict wrong patterns, directly degrading model accuracy. Completeness (D) is equally critical because missing values in key fields reduce the effective training set and can bias or weaken the model's learned relationships, so having all required attributes populated is essential for reliable predictions. Uniqueness (A) matters mainly for deduplication and record identity rather than predictive correctness, consistency (C) concerns uniform formats/values across sources, and timeliness (E) concerns how current the data is — all useful data-quality dimensions, but not the two most directly tied to model accuracy in this scenario.

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Uniqueness

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Consistency

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Timeliness

Question 3

A company is ingesting data from multiple sources into Data Cloud for Einstein. Which THREE data preparation steps should be performed?

Accepted Answer

Normalization. Normalization (A) is correct because data arriving from multiple heterogeneous sources must be standardized into consistent formats, units, and value representations before it can be harmonized in Data Cloud. Field mapping (B) is correct because each source's fields must be mapped to the corresponding Data Cloud Data Model Object (DMO) fields so the ingested data lands in the right schema. Deduplication (E) is correct because combining records from multiple sources inevitably creates duplicate profiles/records, and identity resolution and matching rules require deduplication to produce a unified individual. Encryption (C) is not a data preparation step here — it is a security control applied to data at rest or in transit, not a transformation performed during ingestion preparation. Data labeling (D) is not one of the required preparation steps for ingesting multi-source data into Data Cloud; it is a governance/classification activity rather than a core ingestion-prep transformation.

Answer

Encryption

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Data labeling

Question 4

A company is preparing data for Einstein Prediction Builder to forecast lead conversion. They have historical data with fields like Lead Source, Industry, Number of Employees, and Converted (boolean). Which data preparation step is most critical?

Accepted Answer

Ensure data completeness by handling missing values in Lead Source. Handling missing values in Lead Source is critical because Einstein Prediction Builder requires complete, high-quality data to train accurate predictive models. Missing categorical fields like Lead Source can introduce bias or cause the model to ignore important patterns in lead conversion. Ensuring data completeness through imputation or removal of incomplete records is a standard data preparation step for AI/ML in Salesforce.

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Mix data from all lead sources without normalization

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Use only the last 3 months of data for training

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Remove all records with outliers in Number of Employees

Question 5

A data scientist notices that an Einstein model for predicting customer churn has unusually high accuracy on training data but performs poorly on validation data. Which data issue is the most likely cause?

Accepted Answer

A field containing future information (e.g., 'churn_date') was included in features. Including a field like 'churn_date' in the feature set introduces target leakage, where the model has access to information that would not be available at prediction time. This causes the model to appear highly accurate on training data (since it can directly 'see' the outcome) but fails to generalize to validation data where such future information is absent. In Salesforce Einstein, features must be strictly historical or static to avoid this data leakage issue.

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The dataset has an imbalanced class distribution

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The dataset contains many missing values

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The model was trained on stale data from a different season

Question 6

A company wants to build a sentiment analysis model using customer feedback. What is the best practice for labeling the training data?

Accepted Answer

Employ a diverse set of human labelers with clear guidelines. Option C is correct because employing a diverse set of human labelers with clear guidelines reduces individual bias and improves label consistency, which is a best practice for supervised sentiment analysis training data. Clear annotation guidelines ensure subjective sentiment judgments are applied uniformly, and multiple labelers allow measuring inter-annotator agreement to validate quality. Option A is wrong because sentiment analysis typically requires labeled examples for supervised training, and ignoring labels does not meet the stated goal. Option B is weaker than C because a single expert can introduce personal bias and does not provide agreement checks, while Option D is unreliable since keyword matching misses context, sarcasm, and nuanced sentiment.

Answer

Ignore labeling and use unsupervised learning

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Have a single domain expert label all data

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Use automated keyword matching to assign sentiment

Question 7

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 ingest, harmonize, and unify data from multiple sources — including CRM, external ERP, and marketing automation — into a single customer profile. Training AI models on this unified dataset aligns with Salesforce's AI (Einstein) capabilities, which rely on Data Cloud as the foundation for cross-sell recommendations.

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Use only Salesforce CRM data and ignore external sources

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

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

Question 8

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. Customer support tickets often contain personally identifiable information (PII) such as names, email addresses, phone numbers, or account details within the free-text Description and Title fields. Training a model on this data without anonymization risks exposing PII, violating privacy regulations like GDPR or CCPA, and potentially causing the model to memorize and regurgitate sensitive information. Anonymizing or redacting PII before training is the most critical privacy step.

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Encrypt session tokens used in the support system

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Remove all case numbers to prevent data leakage

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Ensure all customers have opted in before using their data

Question 9

A fraud detection model is being trained on transaction data where only 1% of transactions are fraudulent. The current model predicts 'non-fraud' for all transactions, achieving 99% accuracy. Which technique should be applied to improve model performance?

Accepted Answer

Oversample the minority class or undersample the majority class. The model achieves 99% accuracy by always predicting the majority class ('non-fraud'), which is a classic case of class imbalance. Accuracy is a misleading metric here because the model never identifies fraud. Resampling techniques—oversampling the minority class (e.g., SMOTE) or undersampling the majority class—rebalance the training data so the model learns to distinguish fraud from non-fraud. This directly addresses the root cause: the severe skew in class distribution.

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Remove the minority class to have balanced data

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Set a lower classification threshold for fraud

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Add more features like transaction location

Question 10

After applying a log transformation to a numeric feature, an Einstein model’s performance dropped significantly. What is the most likely cause?

Accepted Answer

The feature contained zero or negative values. Log transformation is undefined for zero or negative values because log(0) is negative infinity and log of a negative number is not a real number. In Salesforce Einstein, numeric features with such invalid transformed values can cause the model to fail or produce erratic results, leading to a significant drop in performance. This is the most likely cause given the symptom described.

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The data volume was reduced by the transformation

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The feature was normally distributed after transformation

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The transformation introduced multicollinearity with other features

Question 11

A bank uses Einstein Discovery to generate insights about loan approval decisions. After deployment, they notice the model denies loans to a higher percentage of applicants from a certain postal code. Which action should be taken to ensure responsible AI?

Accepted Answer

Audit model outcomes for fairness across demographic groups and retrain if needed. Responsible AI requires auditing model outcomes for fairness across demographic groups, even when the disparity correlates with a non-protected attribute like postal code. In Einstein Discovery, postal code can act as a proxy for protected attributes such as race or socioeconomic status, and ignoring this could lead to discriminatory lending practices. Auditing allows the team to detect and mitigate bias, and retraining with fairness constraints ensures the model aligns with ethical AI principles.

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Ignore the discrepancy because postal code is not a protected attribute

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Retrain the model using only recent loan data

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Remove the postal code field from the model

Question 12

A company plans to use Einstein Discovery to analyze sales data. Which data preparation step is essential for time-series forecasting?

Accepted Answer

Ensure date fields are properly formatted and contain sufficient historical range. For time-series forecasting in Einstein Discovery, the date field must be properly formatted (e.g., as a date or datetime data type) and contain a sufficient historical range to identify patterns like seasonality and trends. Without adequate historical data, the model cannot learn temporal dependencies, making this step essential.

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Remove all outliers in sales amounts

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Remove duplicate records

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Scale all numeric fields to a 0-1 range

Question 13

A data scientist is preparing numeric features for a regression model. Which TWO transformations are commonly applied to improve model performance?

Accepted Answer

Normalize to a 0-1 range. Option A (Normalize to a 0-1 range) is correct because min-max scaling rescales numeric features to a bounded [0,1] interval, which prevents features with large magnitudes from dominating distance- or gradient-based regression models and improves convergence. Option E (Standardize to mean 0 and variance 1) is correct because z-score standardization centers each feature at zero mean with unit variance, a standard preprocessing step that helps many regression algorithms (e.g., linear regression with regularization, SVMs, neural networks) learn stable coefficients. Option B is not a standard feature transformation for improving regression performance; outlier removal is a data-cleaning decision that can discard valid signal and is not a scaling/normalization technique. Option C is wrong because converting numeric values to string labels destroys the ordinal/numeric relationships the regression model needs. Option D is wrong because one-hot encoding is for categorical variables and would create sparse binary columns, not transform numeric features for regression.

Answer

Remove outliers beyond 3 standard deviations

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Convert numbers to string labels

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Apply one-hot encoding

Question 14

Refer to the exhibit. A data analyst has defined this field mapping for Einstein Prediction Builder. Which data issue would most likely arise from this mapping?

Accepted Answer

The 'Id' field should be excluded as it can cause data leakage and overfitting. Including the 'Id' field in Einstein Prediction Builder can cause data leakage and overfitting. The 'Id' field is a unique identifier that has no predictive value for the target outcome, but the model could learn to memorize specific records based on it, leading to poor generalization on unseen data.

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The 'LeadSource' field should be mapped to 'Text' instead of 'Category' to preserve verbatim values

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The 'Amount' field should be mapped to 'Category' to discretize the values

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The 'CloseDate' field should be mapped to 'Text' to avoid date parsing issues

Question 15

A data scientist notices that the model accuracy drops significantly after retraining with new data. Upon inspection, they find that many records have missing values for a key feature. Which data quality improvement should be prioritized first?

Accepted Answer

Implement imputation for missing feature values.. The core issue is that missing values in a key feature introduce noise and bias, directly degrading model performance. Imputation (option A) is the most direct and impactful first step because it preserves the dataset size and feature set, allowing the model to learn from complete patterns. Without addressing missing data first, other quality improvements like normalization or feature reduction would be applied to corrupted data, failing to resolve the root cause.

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Normalize the feature range.

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Reduce the number of features.

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Remove duplicate records.