AI Associate Data for AI Practice Test 14 — 15 Questions

Question 1

A Salesforce administrator is preparing data for an AI model that predicts customer lifetime value. The dataset contains a 'Country' field with values like 'USA', 'Canada', and 'Mexico'. The administrator wants to use this field in the model but knows that machine learning algorithms require numeric input. What should the administrator do to make the 'Country' field suitable for the model?

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

Accepted Answer

Apply one-hot encoding to create binary columns for each country.. One-hot encoding is the correct method for nominal categorical variables like Country. It creates binary features that do not impose ordinality, allowing the model to learn separate effects for each country. This is a best practice in data preparation for AI models, ensuring that the algorithm interprets the data correctly.

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Replace country names with arbitrary numeric codes (e.g., USA=1, Canada=2, Mexico=3).

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Leave the field as text; Einstein Discovery automatically handles categorical variables.

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Use a formula field to extract the country's ISO code and treat it as a number.

Question 2

A data engineer is preparing a dataset for an Einstein Discovery model that predicts customer churn. The dataset contains missing values in several fields. Which two methods are appropriate for handling missing values to maintain model accuracy? (Choose two.)

Accepted Answer

Impute missing numerical values with the mean of the column.. Imputing missing numerical values with the mean and missing categorical values with the mode are both standard, effective methods to handle missing data without discarding records. They preserve dataset size and maintain distributions, which supports model accuracy. Other methods like row deletion or arbitrary zero-filling can introduce bias or errors.

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Remove all rows with any missing values.

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Set all missing values to zero.

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Ignore missing values and proceed with model training.

Question 3

A Salesforce admin is preparing a dataset for an AI model that predicts customer satisfaction. The dataset includes a field 'Region' with values like 'North', 'South', 'East', and 'West'. The admin wants to ensure the model can use this field effectively. What should the admin do?

Accepted Answer

One-hot encode the 'Region' field into separate binary columns for each region.. One-hot encoding is the standard method for handling nominal categorical variables in machine learning. It creates binary columns for each category, allowing the model to treat each region independently without imposing an order. This is crucial for fields like 'Region' where no inherent ranking exists. Proper encoding improves model accuracy and avoids misleading the algorithm.

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Convert 'Region' to a numeric field by mapping each region to its average satisfaction score.

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Label encode the 'Region' field by assigning numeric values (e.g., North=1, South=2).

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Remove the 'Region' field because it is not a numeric feature.

Question 4

A Salesforce administrator is setting up a Data Cloud data stream to unify customer profiles from multiple sources for use in AI models. The sources include a CRM, a marketing automation platform, and a legacy system. The administrator needs to ensure that records from these sources are matched correctly to create a unified individual. Which Data Cloud feature should be used?

Accepted Answer

Identity Resolution ruleset. Identity Resolution is the Data Cloud feature that matches and unifies records from multiple sources into a single individual. It uses rulesets to define match criteria and reconciliation to merge attributes. This unified profile is essential for AI models that require a complete view of the customer to make accurate predictions.

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Calculated Insights

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Data Lake objects

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Data Model objects

Question 5

A data scientist is preparing a dataset for an Einstein Discovery model that predicts customer satisfaction scores. The dataset includes a field 'Region' with values like 'North', 'South', 'East', 'West', and 'Unknown'. The data scientist notices that 'Unknown' appears in 30% of records. What is the most appropriate action regarding this field?

Accepted Answer

Keep 'Unknown' as a separate category and let the model determine its impact.. Keeping 'Unknown' as a separate category allows the model to learn any predictive signal associated with missing region data. With 30% missing, imputation or removal could introduce bias and lose information. Einstein Discovery can handle categorical missing values effectively, making this the most appropriate action.

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Remove all records with 'Unknown' region to ensure data quality.

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Create a new binary field indicating whether the region is known or unknown, and drop the original 'Region' field.

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Replace 'Unknown' with the most frequent region to reduce missing data.

Question 6

A Salesforce admin is setting up a dataset for an Einstein Prediction Builder model to predict whether a lead will convert. The dataset includes a field 'Industry' that has many missing values. What is the recommended approach to handle missing values in this categorical field?

Accepted Answer

Replace missing values with the most frequent industry in the dataset.. For categorical fields with missing values, imputing with the most frequent category (mode) is a recommended practice. It retains data and maintains distribution. Deleting records or leaving blanks can cause bias or errors, while arbitrary numeric conversion introduces false relationships. This approach ensures the model can use the 'Industry' field effectively.

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Delete all records with missing 'Industry' values to ensure data completeness.

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Convert the 'Industry' field to a numeric field by assigning arbitrary numbers to each industry.

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Leave the missing values as blank because Einstein Prediction Builder automatically handles nulls.

Question 7

A data engineer at a retail company is preparing a dataset in Salesforce Data Cloud to train a propensity model. The dataset contains a field named 'Preferred_Contact_Time' with values such as 'Morning', 'Afternoon', 'Evening', and 'Weekend'. The model requires numerical input, but the field is stored as text. The engineer wants to transform this field into a format suitable for the model. Which approach should the engineer take?

Accepted Answer

Use one-hot encoding to create binary columns for each category.. Categorical text data must be encoded into numerical representations before being used in most AI models. One-hot encoding creates separate binary columns for each category, avoiding any implied order and allowing the model to treat each category independently. This is the recommended method for nominal data like preferred contact time, ensuring the model can learn without bias from arbitrary numeric assignments.

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Apply label encoding to assign integer values (e.g., Morning=1, Afternoon=2).

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Replace the text values with their frequency counts in the dataset.

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Convert the text to lowercase and use it directly as a numerical feature.

Question 8

A Salesforce admin is using Einstein Discovery to build a model predicting opportunity win probability. The dataset includes a 'CloseDate' field. The admin notices that the model's performance is poor and suspects data leakage. Which action should be taken to prevent leakage from the 'CloseDate' field?

Accepted Answer

Exclude 'CloseDate' from the model because it is not known at prediction time.. Data leakage occurs when a feature used in training is not available at prediction time. CloseDate is typically set when an opportunity is closed, so it cannot be used to predict win probability for open opportunities. Excluding it prevents the model from learning spurious correlations that won't generalize.

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Convert 'CloseDate' to a numeric value (e.g., days since epoch) and include it as a feature.

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Create a formula field that calculates the number of days until CloseDate and use that instead.

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Leave 'CloseDate' in the model but monitor for overfitting using cross-validation.

Question 9

A data engineer is preparing a dataset for an Einstein Discovery model to predict customer lifetime value. The dataset includes a field 'TotalSpend' that is highly skewed with a few extreme outliers. Which transformation should the engineer apply to improve model performance?

Accepted Answer

Apply a logarithmic transformation to the 'TotalSpend' field.. A logarithmic transformation is effective for highly skewed data with extreme outliers. It reduces skewness, making the distribution more symmetric and improving model performance. Standardization, binning, or removal do not address the underlying skewness and can lead to information loss or bias.

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Bin the 'TotalSpend' values into equal-width bins.

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Standardize the 'TotalSpend' field by subtracting the mean and dividing by the standard deviation.

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Remove all records with 'TotalSpend' values beyond three standard deviations from the mean.

Question 10

A data scientist is preparing a dataset for an Einstein Discovery model to predict customer churn. The dataset includes a 'CustomerSince' date field, a 'TotalSpend' numeric field, and a 'SupportTickets' numeric field. The data scientist wants to create new features that capture customer tenure and engagement. Which two feature engineering techniques should be applied? (Choose two.)

Accepted Answer

Compute the ratio of TotalSpend to SupportTickets to create a 'SpendPerTicket' feature.. Creating a tenure feature from CustomerSince and a spend-per-ticket ratio from TotalSpend and SupportTickets are both effective feature engineering techniques. They transform raw data into meaningful predictors that can help the model identify churn patterns. These features capture customer loyalty and engagement, which are key drivers of churn.

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Bin the TotalSpend field into quartiles and treat as categorical.

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Create a polynomial feature by squaring SupportTickets.

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Apply one-hot encoding to the CustomerSince date field.

Question 11

A Salesforce admin is preparing a dataset for an Einstein Discovery model that predicts customer churn. The dataset contains a 'LastPurchaseDate' field with dates in multiple formats (e.g., '2023-01-15', '01/15/2023', '15-Jan-2023'). The admin wants to ensure the field is usable for modeling. What should the admin do first?

Accepted Answer

Standardize the date format to a consistent ISO 8601 format (YYYY-MM-DD) using a data preparation tool or formula field.. Inconsistent date formats prevent Einstein Discovery from recognizing the field as a valid date type, leading to errors or poor model performance. Standardizing to a uniform format like ISO 8601 ensures the model can correctly parse and use the temporal information. Other options either skip this critical step or remove valuable data.

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Convert the date field to a numeric value by calculating the number of days since a fixed reference date.

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Create a new text field that concatenates the date with a customer ID to create a unique identifier.

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Remove the 'LastPurchaseDate' field entirely because date fields are not supported by Einstein Discovery.

Question 12

A data scientist is building a propensity-to-buy model in Salesforce using Einstein Discovery. The dataset includes a field 'ProductCategory' with 50 distinct values. After training, the model shows high accuracy but performs poorly on new data. What is the most likely cause and the best action to take?

Accepted Answer

The model is overfitting due to high cardinality; reduce the number of categories by grouping rare ones into an 'Other' category.. High cardinality in a categorical feature like 'ProductCategory' with 50 values can lead to overfitting, as the model may memorize rare categories. Grouping infrequent categories into an 'Other' bucket reduces the number of levels, improving generalization. This action directly addresses the discrepancy between training and new data performance without discarding the feature's predictive potential.

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The model is overfitting due to too many features; apply feature selection to remove other variables.

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The model is suffering from data leakage; ensure that 'ProductCategory' is not derived from the target variable.

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The model is underfitting because the feature is not informative; remove 'ProductCategory' entirely.

Question 13

A Salesforce administrator is preparing data for an AI model that predicts customer lifetime value. The dataset contains a 'Country' field with values like 'USA', 'Canada', and 'Mexico'. The administrator wants to use this field in the model but knows that machine learning algorithms require numeric input. What should the administrator do to make the 'Country' field suitable for the model?

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

Accepted Answer

Validity. Validity is the data quality dimension that ensures values conform to expected formats and domains. In this scenario, the email field contains placeholder strings that are not valid email addresses. Filtering or transforming these values before feeding the data stream to Einstein Studio prevents the model from learning incorrect patterns, thereby improving prediction accuracy and data readiness for AI.

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Completeness

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Uniqueness

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Timeliness

Question 14

A data engineer is building a Data Cloud data model for an Einstein Discovery model that predicts whether a customer will renew a subscription. The source system sends the renewal date in the format MM/DD/YYYY as a text string. The engineer notices that Einstein Discovery is treating this field as a categorical variable instead of a date. What should the engineer do to correct this?

Accepted Answer

In Data Cloud, edit the data model object field mapping and set the field's data type to Date, then refresh the data stream.. Changing the field's data type to Date on the Data Cloud data model object is the correct fix because it controls how Einstein Discovery interprets the variable. Refreshing the stream after the mapping change ensures the model uses the corrected type. Source-side conversions and calculated insights are indirect workarounds that do not resolve the semantic type at the data model layer.

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Use a Data Cloud calculated insight to cast the text field to a date and then create a new model using the insight output.

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Add a filter in Einstein Discovery to exclude the renewal date field from the model because text fields cannot be used in predictions.

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Create a formula field in the source system that converts the text to a date, then re-ingest the data into Data Cloud.

Question 15

A data scientist is building an Einstein Discovery model to predict opportunity win rates. The dataset includes a 'Probability' field that is manually entered by sales reps and often left blank or set to default values. The data scientist wants to improve model accuracy. What is the most appropriate action regarding the 'Probability' field?

Accepted Answer

Exclude the 'Probability' field from the model because it is manually entered and unreliable.. The 'Probability' field is manually entered, often blank, and set to defaults, making it unreliable and potentially biased. Excluding it prevents the model from learning from noisy data and encourages use of more objective predictors. While imputation or replacement could be considered, they are not the most appropriate first step given the field's issues.

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Impute missing values with the mean probability and keep the field as a predictor.

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Keep the field but create a binary indicator for whether it was manually entered or defaulted.

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Replace the 'Probability' field with a calculated field that uses historical win rates by stage.