DA0-002 Data Analysis Practice Question
Which THREE of the following are common steps in data cleaning?
⚠ Common exam trap
CompTIA often tests the distinction between data cleaning steps and data collection or preprocessing steps, so the trap here is confusing 'increasing sample size' (a data augmentation or collection activity) with actual cleaning tasks like imputation, standardization, and deduplication.
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
✓
Imputing missing values
Imputing missing values is a common data cleaning step because real-world datasets often have gaps due to data collection errors or system failures. Techniques like mean/median imputation, regression imputation, or using algorithms like k-NN help preserve sample size and avoid bias that would result from simply dropping rows. This ensures the dataset remains usable for analysis without introducing significant distortion.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Removing outliers without justification
Why it's wrong here
Outliers should only be removed with a valid reason, not automatically.
- ✓
Imputing missing values
Why this is correct
Missing values are often imputed to maintain dataset completeness.
- ✓
Standardizing data formats
Why this is correct
Standardizing formats ensures consistency across data (e.g., date formats).
- ✓
Removing duplicate records
Why this is correct
Duplicates distort analysis and are removed during cleaning.
- ✗
Increasing sample size
Why it's wrong here
Increasing sample size is a data collection strategy, not a cleaning step.
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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.