AI Associate Data for AI Practice Test 13 — 15 Questions

Question 1

Refer to the exhibit. A data analyst receives an error when trying to use this model configuration for Einstein AI predictions. Which issue is most likely causing the error?

Accepted Answer

The feature field "Usage__c" does not exist in the data source.. The correct answer is A: the feature field "Usage__c" does not exist in the data source. In Einstein AI model configuration, every field referenced as a feature or target must exist in the underlying dataset; if "Usage__c" is missing, the model cannot build the feature vector and prediction fails with a field-not-found error. Option B is incorrect because a 0.8 split ratio is a standard, allowed value for classification models. Option C is incorrect because the prediction window is not required to be longer than the training window in that way, and this would not produce the described field error. Option D is incorrect because Einstein AI can handle text target fields for classification, so a text target alone would not cause this error.

Answer

The split ratio of 0.8 is not allowed for classification.

Answer

The prediction window is shorter than the training window.

Answer

The target field "Churn__c" is a text field instead of an integer.

Question 2

A Salesforce admin is preparing a dataset for an Einstein Discovery model that predicts customer churn. They notice that the 'Customer_Email__c' field contains multiple blank values across thousands of records. Which data preparation step should they take FIRST to address this issue?

Accepted Answer

Analyze the missingness pattern to determine if it is random or systematic.. Before deciding how to handle missing values, it is essential to understand the nature of the missingness. Analyzing whether the missing email addresses are random or follow a pattern helps determine the best course of action, such as imputation, removal, or fixing data collection processes. This diagnostic step ensures that subsequent data preparation choices are informed and appropriate.

Answer

Create a new field that flags whether the email is missing.

Answer

Impute missing values using the most frequent email address.

Answer

Remove records with missing email addresses from the dataset.

Question 3

A Salesforce admin is preparing a dataset for an Einstein Discovery model that predicts customer churn. The dataset contains a 'LastPurchaseDate' field, and the admin wants to transform it into a numeric feature representing the number of days since the last purchase relative to today. Which approach should the admin use to create this feature?

Accepted Answer

Create a formula field on the source object that calculates the difference between TODAY() and LastPurchaseDate, then include it in the dataset.. Creating a formula field that subtracts LastPurchaseDate from TODAY() produces a numeric feature representing recency, which is critical for churn prediction. This method is dynamic, automatically updated, and integrates directly with Einstein Discovery datasets. It avoids manual intervention and ensures the model always uses current data.

Answer

Configure a workflow rule to update a numeric field with the day difference whenever the record is edited.

Answer

Use a roll-up summary field to count the number of days between the last purchase and the current date.

Answer

Manually export the data, calculate the difference in a spreadsheet, and re-import it as a static field.

Question 4

A data engineer is building a dataset for an Einstein Discovery model that predicts opportunity win probability. The dataset includes a 'Close_Date__c' field. They want to create a feature that captures the number of days between opportunity creation and close. Which transformation should they apply?

Accepted Answer

Create a formula field that subtracts 'CreatedDate' from 'Close_Date__c'.. To capture the sales cycle duration, the engineer should calculate the difference between the opportunity creation date and close date. This derived feature directly measures how long the opportunity was open, which can be a key predictor of win probability. Other transformations like bucketing or timestamp conversion do not provide this duration.

Answer

Replace 'Close_Date__c' with the quarter of the year.

Answer

Convert 'Close_Date__c' to a numeric Unix timestamp.

Answer

Bucket the 'Close_Date__c' field into monthly intervals.

Question 5

A Salesforce admin is preparing a dataset for an Einstein Discovery model that predicts customer churn. The dataset contains a field named 'CustomerSince' with values like '2021-03-15'. The model treats this field as a categorical variable, which lowers accuracy. What should the admin do to improve the model?

Accepted Answer

Convert the 'CustomerSince' field to a numeric value representing the number of days since that date.. Dates stored as strings or treated as categories can mislead a model because the algorithm cannot interpret chronological order or duration. Transforming the date into a numeric value such as days since a reference date converts it into a continuous feature that captures tenure, which is directly relevant to churn. This is a standard feature engineering step in data preparation for AI.

Answer

Replace the date with the customer's age to provide a more stable numeric feature.

Answer

Change the field type to 'Text' in the dataset so the model treats it as a category.

Answer

Remove the 'CustomerSince' field entirely because dates are not useful for churn prediction.

Question 6

A Salesforce admin is preparing a dataset for an Einstein Discovery model that predicts customer churn. The dataset includes a field named 'CustomerID' that uniquely identifies each customer, plus several numeric and categorical fields. The admin wants to ensure the model learns meaningful patterns without memorizing individual customers. What should the admin do with the 'CustomerID' field?

Accepted Answer

Exclude the 'CustomerID' field from the model because it is an identifier and not a predictive feature.. Unique identifiers like CustomerID do not contain predictive information and can cause a model to memorize individual records, which harms generalization. Excluding such fields ensures the model learns from actual behavioral and demographic attributes that correlate with churn. This practice improves model robustness and avoids overfitting.

Answer

Use 'CustomerID' as the outcome variable to predict which customers will churn.

Answer

Convert 'CustomerID' to a numeric value and include it as a feature to help the model differentiate customers.

Answer

Keep 'CustomerID' in the dataset but exclude it during model training only.

Question 7

A data scientist is preparing data for an Einstein Discovery model to predict customer lifetime value. They have a dataset with 50,000 records and 200 features. They notice that one feature, 'Total_Purchases__c', has a highly skewed distribution with a few extreme outliers. Which data preparation technique should they consider to reduce the impact of outliers?

Accepted Answer

Apply a logarithmic transformation to 'Total_Purchases__c'.. For highly skewed numerical features with outliers, a logarithmic transformation is often effective. It reduces the leverage of extreme values, making the distribution more symmetric and improving the performance of models that assume normality or are sensitive to scale. Other techniques like standardization or removal do not address the skewness directly and may introduce other issues.

Answer

Standardize 'Total_Purchases__c' by subtracting the mean and dividing by the standard deviation.

Answer

Replace outliers with the median value of 'Total_Purchases__c'.

Answer

Remove all records where 'Total_Purchases__c' is above the 99th percentile.

Question 8

A Salesforce administrator is setting up a Data Cloud data stream to ingest customer order data from an external system. The external system sends records with a field 'OrderDate' in the format 'MM/DD/YYYY'. The administrator needs to ensure the data is correctly mapped and usable for AI models. What should the administrator do first?

Accepted Answer

Create a data stream and map the 'OrderDate' field to a Date data type in Data Cloud.. When ingesting data into Data Cloud, the first step is to map fields to appropriate data types. Mapping 'OrderDate' to a Date type ensures that Data Cloud can correctly parse and use the field for temporal calculations and AI features. This foundational step prevents downstream issues such as incorrect sorting or failed date functions.

Answer

Use Data Prep to split 'OrderDate' into separate month, day, and year fields.

Answer

Transform the 'OrderDate' field to a text string to avoid parsing errors.

Answer

Create a calculated insight to convert 'OrderDate' to a Unix timestamp before ingestion.

Question 9

A Salesforce administrator is preparing data for an AI model that predicts which leads are most likely to convert. They need to ensure the data is high quality. Which two data quality dimensions are most critical to address before training the model? (Choose two.)

Accepted Answer

Consistency. Completeness and consistency are fundamental data quality dimensions for AI. Missing values can skew predictions, while inconsistent data formats can lead to incorrect feature interpretation. Addressing these ensures the model learns from reliable, standardized data, improving its ability to predict lead conversion accurately.

Answer

Timeliness

Answer

Precision

Answer

Accessibility

Question 10

A data analyst is preparing a dataset for an Einstein Prediction Builder model that predicts whether a customer will make a repeat purchase. The dataset contains a field 'LastPurchaseDate' with some missing values. The analyst wants to handle these missing values appropriately. What is the recommended approach?

Accepted Answer

Leave missing values as null and let Einstein Prediction Builder handle them automatically.. Einstein Prediction Builder automatically handles missing values, so leaving them as null is appropriate. Imputing with arbitrary values or removing records can introduce bias or reduce data volume. Allowing the platform to manage missing data ensures the model can use all available information and learn from patterns of missingness.

Answer

Impute missing values with a default date such as January 1, 1900, to indicate missingness.

Answer

Remove records with missing 'LastPurchaseDate' from the dataset before training.

Answer

Replace missing values with the average purchase date across all customers.

Question 11

A data engineer is preparing a dataset for an Einstein Discovery model to predict customer churn. The dataset has a field 'TotalSpend' that is highly skewed, with a few customers having extremely high values. The engineer wants to reduce the impact of these outliers on the model. Which data transformation should the engineer apply?

Accepted Answer

Log transformation. Log transformation is effective for highly skewed data because it compresses the range of extreme values, making the distribution more symmetric. This reduces the leverage of outliers, allowing the model to learn from the majority of the data without being dominated by a few extreme points. It is a standard preprocessing step for skewed numerical predictors.

Answer

Binning

Answer

Min-max normalization

Answer

Z-score standardization

Question 12

A Salesforce administrator is configuring a Data Cloud data stream to ingest customer profiles from an external CRM. The source sends a 'LastModifiedDate' field in the format 'MM/DD/YYYY', but Data Cloud expects ISO 8601 ('YYYY-MM-DD'). Which Data Cloud feature should be used to transform the date format during ingestion?

Accepted Answer

Data Cloud Data Transform. Data Cloud Data Transform is specifically designed to modify data during ingestion, including date format conversions. Using it ensures the incoming 'MM/DD/YYYY' values are converted to ISO 8601 before being stored in Data Cloud, which is critical for accurate AI model training and consistency.

Answer

Salesforce Flow

Answer

CRM Analytics Data Prep Recipe

Answer

Einstein Prediction Builder

Question 13

A data scientist is building an Einstein Discovery model to predict which customers are likely to default on a loan. The dataset has a field 'Income' with many missing values. The scientist decides to impute missing values using the median income. After training, the model performs well on the training data but poorly on new data. What is the most likely cause of this issue?

Accepted Answer

The median imputation introduced bias because missing values were not missing at random.. When missing values are not missing at random, median imputation can introduce bias because the imputed values do not reflect the true underlying distribution for those records. This can cause the model to learn spurious patterns that do not generalize to new data. The poor performance on new data despite good training performance suggests that the imputation method created an unrealistic representation of the data.

Answer

The median imputation reduced the variance of the 'Income' field, causing underfitting.

Answer

The model is overfitting because the dataset is too small.

Answer

The 'Income' field should have been removed entirely instead of imputed.

Question 14

A data engineer is preparing a dataset for an Einstein Discovery model to predict loan default risk. The dataset includes a field 'Income' with a skewed distribution and several outliers. The engineer wants to improve model performance by transforming this field. Which transformation is most appropriate?

Accepted Answer

Apply a logarithmic transformation to 'Income' to reduce skewness and the impact of outliers.. Logarithmic transformation is ideal for right-skewed data with outliers, as it reduces skewness and compresses extreme values. This allows the model to better capture the relationship between income and default risk without being dominated by a few high-income records. Other methods either do not address skewness or introduce bias.

Answer

Replace outliers with the median income value.

Answer

Bin 'Income' into deciles and use the decile rank as a feature.

Answer

Standardize 'Income' by subtracting the mean and dividing by the standard deviation.

Question 15

A Salesforce admin is preparing a dataset for an Einstein Discovery model that predicts customer churn. The dataset contains a field named 'LastPurchaseDate' with values that include future dates (e.g., 2025-12-31) for some customers. What should the admin do to address this data quality issue?

Accepted Answer

Investigate the source of future dates and correct or exclude invalid values after validation.. Future dates in a 'LastPurchaseDate' field indicate potential data quality issues. The correct approach is to investigate and correct or exclude invalid values after validation. This ensures the model trains on accurate data, improving churn prediction. Simply removing, replacing, or ignoring the issue can introduce bias or errors.

Answer

Remove all records with future dates from the dataset to avoid skewing the model.

Answer

Leave the future dates as-is because Einstein Discovery automatically handles outliers.

Answer

Replace future dates with the current date to maintain consistency.