Question 546 of 986
DA0-002 Data Acquisition and Preparation Practice Question
A data analyst is validating a dataset acquired from an external source. Which TWO actions are appropriate for data quality assessment?
⚠ Common exam trap
Many exam-takers confuse data cleaning (which includes deletion or transformation) with data quality assessment, which is the diagnostic step that should occur before any irreversible actions like deletion or production loading.
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
✓
Check for missing values in critical fields
Checking for missing values in critical fields is a fundamental data quality assessment step because missing data can indicate incomplete records, data corruption, or extraction errors. Identifying these gaps early allows the analyst to decide on appropriate handling strategies, such as imputation or rejection, before further processing. This aligns with data profiling best practices in the mining and acquisition phase.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Check for missing values in critical fields
Why this is correct
Missing value checks are fundamental to data quality.
- ✗
Delete any rows with null values without review
Why it's wrong here
Deleting rows without review can remove valid data.
- ✓
Validate data format against expected schema
Why this is correct
Format validation ensures data can be loaded correctly.
- ✗
Immediately load all data into production
Why it's wrong here
Data should be validated before loading.
- ✗
Transform data to match target system without verification
Why it's wrong here
Transformations should be verified to avoid corruption.
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Last reviewed: Jun 24, 2026
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