A data scientist is building a churn prediction model for a subscription service. The dataset has 500,000 customers, but only 2% churned. The model currently predicts 'no churn' for all customers. Which approach would most directly address the data issue causing this behavior?

Remove customers who churned to balance the dataset.

Use a larger test set to capture more churn cases.

Apply SMOTE to generate synthetic churn examples.

Apply z-score normalization to the churn indicator.

A data analyst is working with sales data recorded at the transaction level (each sale has a timestamp). The business wants to analyze daily sales trends. Which data preparation step is most appropriate?

Keep the transaction-level data and use it directly.

Remove the timestamp and keep only the sale amount.

Convert the timestamp to a categorical variable with one category per transaction.

Aggregate the transactions by date to get daily totals.

A retail company wants to forecast monthly sales. The data shows a clear pattern: sales peak every December and dip every January. Which modeling approach would best capture this pattern?

Include a seasonal dummy variable for each month.

Aggregate the data to yearly totals.

Remove December and January data as outliers.

Use a simple moving average of the last 3 months.

Which of the following is a common approach to handle sparse data in a dataset before training a machine learning model?

Increasing the number of features to add more dimensions

Imputing missing values with a suitable strategy

Converting all zeros to the maximum value in the column

Removing all rows that contain any zero value

CompTIADataX

Domain 2Objective 2

Data Issues DY0-001 Practice Questions (Page 3)

Part of the Modeling, analysis, and outcomes domain, which accounts for 24% of the DY0-001 exam. CompTIA does not publish an official question count, but from its 165-minute exam (~65–110 total, ~16–26 in this domain), expect 3–5 from this objective — we provide 28 practice questions to prepare you well beyond it. (estimate)

28questions here

6free pages

5concepts

24%of the exam

Questions 11–15

11application · medium
A data scientist is building a churn prediction model for a subscription service. The dataset has 500,000 customers, but only 2% churned. The model currently predicts 'no churn' for all customers. Which approach would most directly address the data issue causing this behavior?
Select an answer first
12application · medium
A data analyst is working with sales data recorded at the transaction level (each sale has a timestamp). The business wants to analyze daily sales trends. Which data preparation step is most appropriate?
Select an answer first
13application · medium
A retail company wants to forecast monthly sales. The data shows a clear pattern: sales peak every December and dip every January. Which modeling approach would best capture this pattern?
Select an answer first
14foundation · easy
A retail company observes that ice cream sales consistently increase every summer and decrease every winter. What type of pattern is this?
Select an answer first
15foundation · easy
Which of the following is a common approach to handle sparse data in a dataset before training a machine learning model?
Select an answer first

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