A logistics company has a dataset with columns: 'order_id', 'customer_id', 'customer_name', 'customer_address', 'product_id', 'product_name', and 'order_date'. The analyst notices that 'customer_name' and 'customer_address' are repeated for every order by the same customer. The goal is to reduce redundancy and improve processing efficiency. What is the most effective action?

Replace 'customer_name' and 'customer_address' with a single concatenated column.

Keep the columns but compress the dataset to reduce storage size.

Create a separate customer dimension table with unique customer_id, customer_name, and customer_address, and link it via customer_id.

Remove the 'customer_name' and 'customer_address' columns from the dataset.

What is a common technique to identify redundant columns in a dataset?

Calculating the correlation between columns

Creating a bar chart of a categorical column

A marketing analyst receives a customer survey dataset. The 'income' column has several blank cells. The analyst needs to understand the extent of the missing data before deciding how to proceed. Which technique should the analyst use first?

Calculate the percentage of missing values in the 'income' column.

Delete all rows with missing income values.

Sort the dataset by income to see the blank cells at the bottom.

Replace all blank cells with the average income.

What is a common technique to manage outliers that are legitimate extreme values, such as income data?

Replace outliers with the median

Remove all outliers to simplify the dataset

Apply a transformation, such as a log transformation

A dataset contains a column with 5% missing values in a numeric field. The analyst wants to preserve as much data as possible for analysis. Which method is most appropriate to handle the missing values?

Impute the missing values with the mean of the column

Delete all rows with missing values

Replace missing values with zeros

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Domain 2Objective 2

Perform Data Exploration DA0-001 Practice Questions (Page 6)

Part of the Data acquisition and preparation domain, which accounts for 22% of the DA0-001 exam. CompTIA does not publish an official question count, but from its 90-minute exam (~35–60 total, ~8–13 in this domain), expect 3–4 from this objective — we provide 31 practice questions to prepare you well beyond it. (estimate)

31questions here

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22%of the exam

Questions 26–30

26expert · hard
A logistics company has a dataset with columns: 'order_id', 'customer_id', 'customer_name', 'customer_address', 'product_id', 'product_name', and 'order_date'. The analyst notices that 'customer_name' and 'customer_address' are repeated for every order by the same customer. The goal is to reduce redundancy and improve processing efficiency. What is the most effective action?
Select an answer first
27foundation · easy
What is a common technique to identify redundant columns in a dataset?
Select an answer first
28application · medium
A marketing analyst receives a customer survey dataset. The 'income' column has several blank cells. The analyst needs to understand the extent of the missing data before deciding how to proceed. Which technique should the analyst use first?
Select an answer first
29foundation · easy
What is a common technique to manage outliers that are legitimate extreme values, such as income data?
Select an answer first
30foundation · easy
A dataset contains a column with 5% missing values in a numeric field. The analyst wants to preserve as much data as possible for analysis. Which method is most appropriate to handle the missing values?
Select an answer first

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