DA0-002 Data Acquisition and Preparation Practice Question
A data analyst is preparing a dataset for analysis and notices that the 'age' column has a significant number of missing values. The analyst decides to impute the missing values using the mean age. Which data preparation technique is being applied?
⚠ Common exam trap
Many exam-takers confuse imputation with normalization or other data preparation steps, but the key is that missing values are being filled with a calculated value.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Data imputation
Imputing missing values with the mean is a standard data preparation technique known as data imputation. It allows the analyst to retain records that would otherwise be excluded, enabling more complete analysis. This method is simple but should be used with caution as it can affect statistical properties.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Data discretization
Why it's wrong here
Data discretization converts continuous data into discrete bins or intervals, such as grouping ages into ranges. It does not handle missing values. The analyst is imputing missing ages with a summary statistic, not transforming the distribution into categories.
- ✗
Data deduplication
Why it's wrong here
Data deduplication identifies and removes duplicate records to ensure each entity appears once. It is unrelated to missing values. The scenario describes filling in missing ages, not removing repeated rows, so this technique does not apply.
- ✓
Data imputation
Why this is correct
Data imputation is the process of replacing missing values with substituted values. Using the mean age to fill missing entries is a common imputation method. This technique helps maintain dataset size and can reduce bias if the missingness is random, though it may underestimate variability.
- ✗
Data normalization
Why it's wrong here
Data normalization scales numerical values to a common range, such as 0 to 1, to prevent attributes with larger scales from dominating distance calculations. It does not address missing values. Imputing with the mean is a separate technique focused on handling incomplete data, not rescaling.
Go deeper
Related to this question
About these practice questions
One of 1,004 original DA0-002 practice questions on Courseiva, each with a full explanation and wrong-answer analysis — not exam dumps or protected exam content. Learn why practice questions differ from exam dumps →
JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official CompTIA exam blueprint
This DA0-002 practice question is part of Courseiva's free CompTIA certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the DA0-002 exam.