After identifying duplicate records in a dataset, what is the most common strategy to resolve them?

Keep all duplicates to preserve data volume

Replace duplicates with the mean value

Remove the duplicate records, keeping one instance

A data analyst discovers that a customer table has duplicate records where the same customer has two different 'customer_id' values but the same email address. The analyst needs to resolve this. What is the best approach?

Keep both records because they have different customer_ids.

Change the email address in one record to make them unique.

Delete both records and re-enter the customer from scratch.

Merge the two records into one, keeping one customer_id and consolidating other fields.

What is the primary impact of duplicate records on data quality?

They cause missing values in other columns

They increase the accuracy of analysis

They have no effect on data quality

They can skew analysis results and lead to incorrect conclusions

A data analyst is working with a sales dataset that includes 'product_id', 'product_name', 'product_category', and 'product_price'. The analyst notices that 'product_name' and 'product_category' are repeated for every sale of the same product. The goal is to reduce data redundancy. What should the analyst do?

Replace 'product_name' and 'product_category' with a single combined column.

Delete the 'product_name' and 'product_category' columns from the sales table.

Create a separate product lookup table with unique product details and link it via 'product_id'.

Keep the data as is because redundancy does not affect analysis.

After identifying that two columns are highly correlated and redundant, what is an appropriate technique to eliminate the redundancy?

Replace the redundant columns with their mean

Randomly shuffle the values in one column

Remove one of the redundant columns

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Domain 2Objective 2

Perform Data Exploration DA0-001 Practice Questions (Page 1)

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)

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Questions 1–5

1foundation · easy
After identifying duplicate records in a dataset, what is the most common strategy to resolve them?
Select an answer first
2application · medium
A data analyst discovers that a customer table has duplicate records where the same customer has two different 'customer_id' values but the same email address. The analyst needs to resolve this. What is the best approach?
Select an answer first
3foundation · easy
What is the primary impact of duplicate records on data quality?
Select an answer first
4application · medium
A data analyst is working with a sales dataset that includes 'product_id', 'product_name', 'product_category', and 'product_price'. The analyst notices that 'product_name' and 'product_category' are repeated for every sale of the same product. The goal is to reduce data redundancy. What should the analyst do?
Select an answer first
5foundation · easy
After identifying that two columns are highly correlated and redundant, what is an appropriate technique to eliminate the redundancy?
Select an answer first

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