This DA0-001 practice question tests your understanding of comparing and contrasting data concepts. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
Which TWO of the following are examples of data transformation? (Choose TWO.)
Normalizing data to eliminate redundancy
Normalization is a transformation.
Creating a backup of the database
Why wrong: Backup is a data protection activity.
Converting string dates to date format
Type conversion is transformation.
Generating summary statistics
Why wrong: This is data summarization.
Removing duplicate records
Why wrong: This is data cleansing.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Normalizing data to eliminate redundancy
Option A is correct because data normalization is a transformation process that reorganizes data to reduce redundancy and improve integrity, typically by decomposing tables into smaller, related tables (e.g., achieving 3NF in relational databases). This changes the structure and representation of the data, which is a core example of data transformation.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Normalizing data to eliminate redundancy
Why this is correct
Normalization is a transformation.
Related concept
Read the scenario before looking for a memorised answer.
Creating a backup of the database
Why it's wrong here
Backup is a data protection activity.
Converting string dates to date format
Why this is correct
Type conversion is transformation.
Related concept
Read the scenario before looking for a memorised answer.
Generating summary statistics
Why it's wrong here
This is data summarization.
Removing duplicate records
Why it's wrong here
This is data cleansing.
Common exam traps
CompTIA often tests the distinction between data transformation (changing format/structure) and data cleansing (removing errors/duplicates) or data analysis (generating summaries), leading candidates to mistakenly select removal of duplicates or summary statistics as transformations.
Detailed technical explanation
Data transformation involves converting data from one format or structure to another, such as ETL (Extract, Transform, Load) processes in data warehousing. For example, converting string dates to date format (Option C) changes the data type and enables date-specific functions like sorting or date arithmetic, which is a classic transformation. Under the hood, this may involve parsing ISO 8601 strings into a datetime object, altering the binary representation in memory or on disk.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
A small business has 20 workstations on the 192.168.1.0/24 network and one public IP from its ISP. The router uses PAT (NAT overload) so all 20 devices share one public address using different source ports. NAT questions test whether you understand the four address terms and which direction each translation applies.
What to study next
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FAQ
Comparing and Contrasting Data Concepts — This question tests Comparing and Contrasting Data Concepts — Read the scenario before looking for a memorised answer..
The correct answer is: Normalizing data to eliminate redundancy — Option A is correct because data normalization is a transformation process that reorganizes data to reduce redundancy and improve integrity, typically by decomposing tables into smaller, related tables (e.g., achieving 3NF in relational databases). This changes the structure and representation of the data, which is a core example of data transformation.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 30, 2026
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