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
Option A is correct because checking for missing values in critical fields is a core data quality assessment step that identifies nulls or gaps in essential attributes, which can skew analysis or break downstream processing. Option C is correct because validating data format against the expected schema confirms that each column's data type, structure, and constraints (e.g., date formats, numeric ranges, string lengths) match requirements, catching inconsistencies from the external source. Option B is not appropriate because deleting rows with null values without review can silently discard valid records and hide data quality issues rather than assessing them. Option D is wrong because loading unvalidated data directly into production risks propagating errors and corrupting downstream systems. Option E is wrong because transforming data to match the target system without verification skips the assessment step and can mask or introduce quality problems.
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
Checking for missing values in critical fields directly satisfies the requirement to assess completeness, one of the core data quality dimensions. Nulls in mandatory columns, such as customer identifiers or transaction dates, invalidate downstream aggregation and reporting, so quantifying them during validation exposes gaps before analysis begins.
- ✗
Delete any rows with null values without review
Why it's wrong here
Deleting rows with nulls unreviewed destroys valid records and hides systematic gaps, so the assessment never measures completeness or bias. It tempts because nulls often signal defects, yet removal belongs to cleaning after profiling; assessment first quantifies how many values are missing and why.
- ✓
Validate data format against expected schema
Why this is correct
Validating data format against the expected schema catches structural defects—wrong data types, malformed dates, unexpected nulls—before analysis begins. For externally acquired data, where the provider's conventions are unknown, this enforces the agreed contract and satisfies the stem's requirement to assess quality at ingestion.
- ✗
Immediately load all data into production
Why it's wrong here
Loading unvalidated data into production propagates errors downstream before any profiling, so quality issues reach consumers unchecked. It tempts as the end goal of a pipeline, yet production loading is correct only once assessment has confirmed the dataset meets defined accuracy, completeness and consistency thresholds.
- ✗
Transform data to match target system without verification
Why it's wrong here
Transforming data before verifying it propagates errors and destroys the ability to trace discrepancies back to the source, so it defeats the assessment. Transformation is tempting because it moves the work toward the target system. It would be appropriate only after profiling and validation confirm the source data's accuracy and completeness.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.