AI Associate Data for AI Practice Test 5 — 15 Questions

Question 1

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.

Answer

Mix data from all lead sources without normalization

Answer

Use only the last 3 months of data for training

Answer

Remove all records with outliers in Number of Employees

Question 2

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. Option D is correct because 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.

Answer

The dataset has an imbalanced class distribution

Answer

The dataset contains many missing values

Answer

The model was trained on stale data from a different season

Question 3

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. Using diverse human labelers with clear guidelines ensures label consistency and reduces bias. Automated keyword matching is error-prone, a single expert may introduce personal bias, and using only positive labels would create an unbalanced dataset.

Answer

Ignore labeling and use unsupervised learning

Answer

Have a single domain expert label all data

Answer

Use automated keyword matching to assign sentiment

Question 4

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 5

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 6

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. Oversampling or undersampling addresses class imbalance, allowing the model to learn minority patterns. Using more features alone doesn't fix imbalance, setting a lower threshold may help but is less common than resampling, and removing minority class is counterproductive.

Answer

Remove the minority class to have balanced data

Answer

Set a lower classification threshold for fraud

Answer

Add more features like transaction location

Question 7

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.

Answer

The data volume was reduced by the transformation

Answer

The feature was normally distributed after transformation

Answer

The transformation introduced multicollinearity with other features

Question 8

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. Option C is correct because 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.

Answer

Ignore the discrepancy because postal code is not a protected attribute

Answer

Retrain the model using only recent loan data

Answer

Remove the postal code field from the model

Question 9

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.

Answer

Remove all outliers in sales amounts

Answer

Remove duplicate records

Answer

Scale all numeric fields to a 0-1 range

Question 10

A company is training a customer service chatbot using historical conversation logs. Which TWO data preparation practices should be followed to ensure data quality?

Accepted Answer

Include answers with varied phrasing to enhance language variety. Option B is correct because training a chatbot on varied phrasing (e.g., synonyms, different sentence structures) improves its ability to understand and generate natural language responses. This practice enhances the model's robustness and generalization, preventing overfitting to a narrow set of expressions and ensuring it can handle the diverse ways customers phrase their queries.

Answer

Exclude all user identifiers to protect privacy

Answer

Include only successful interactions that were resolved

Answer

Filter only English conversations for consistency

Question 11

Before training an Einstein Prediction model, a data analyst must perform data quality checks. Which THREE checks are most critical?

Accepted Answer

Remove duplicate records that could cause data leakage. Option B is correct because duplicate records can cause data leakage by allowing the model to see the same or highly similar data in both training and validation splits, leading to overfitting and inflated performance metrics. Removing duplicates ensures that the model generalizes to unseen data rather than memorizing repeated instances.

Answer

Confirm that label distribution matches the target baseline

Answer

Ensure all features follow a normal distribution

Question 12

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. Normalizing features to a 0-1 range (min-max scaling) ensures that all numeric features contribute equally to the model, preventing features with larger magnitudes from dominating the gradient descent optimization. This is especially important for distance-based algorithms like k-nearest neighbors or neural networks, where feature scale directly impacts convergence speed and model accuracy.

Answer

Remove outliers beyond 3 standard deviations

Answer

Convert numbers to string labels

Answer

Apply one-hot encoding

Question 13

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. Option C is correct because 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.

Answer

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

Answer

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

Answer

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

Question 14

Refer to the exhibit. A data file for click-through model training has the above content. Which data quality issue is most critical to address before training?

Accepted Answer

Missing value in the Conversions column for the third row. Option B is correct because missing values in the Conversions column directly impact the supervised learning target variable. If the label (conversion) is missing for a training instance, the model cannot learn the correct mapping from features to outcome, leading to biased or incomplete training. This is a critical data quality issue that must be addressed before training, typically via imputation or row removal.

Answer

The header row is missing a column name for the last field

Answer

Inconsistent date formats across rows

Answer

Clicks column is an integer but may need scaling

Question 15

Refer to the exhibit. A data analyst runs a profile on a dataset and sees these statistics. Based on best practices, which action should be taken first?

Accepted Answer

Remove the 200 duplicate records. Option B is correct because duplicate records introduce bias and redundancy, leading to overfitting or skewed model performance. Removing duplicates is a standard first step in data preprocessing to ensure data integrity before handling missing values or outliers. In the context of the AI Associate exam, best practices prioritize deduplication early in the data cleaning pipeline.

Answer

Impute the 500 missing values with the mean

Answer

Remove the 50 outliers in the Amount field

Answer

Skip all preprocessing and train the model directly