DA0-002 Data Acquisition and Preparation Practice Question
A retail analytics team loads a nightly CSV export into their warehouse. During validation, the analyst notices that the 'order_date' column, defined as DATE in the target schema, contains values like '2023-13-45' and 'N/A' in several rows. The ETL job currently fails silently on these rows. Which data acquisition and preparation action BEST addresses the root cause while preserving as much data as possible?
⚠ Common exam trap
The trap here is assuming that making the load succeed — by widening the column type or imputing values — is the same as fixing the data quality problem.
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
✓
Apply validation rules that flag invalid dates for quarantine while loading conforming rows, and log rejected records.
Validating at ingestion, quarantining malformed dates, and logging rejects keeps the pipeline resilient while surfacing the true defect. It loads every trustworthy row, gives the team evidence to fix the upstream export, and stops the silent-failure pattern. Changing types or imputing values merely relocates or amplifies the error.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Change the target column type from DATE to VARCHAR so all incoming values load without error.
Why it's wrong here
Loosening the column type hides the problem rather than solving it. Downstream date arithmetic, sorting, and time-intelligence calculations would break or silently misbehave because strings like 'N/A' and '2023-13-45' cannot be interpreted as real dates. It also removes the schema contract that protects consumers, pushing the failure later into the analytics layer where it is harder to trace.
- ✗
Reject the entire nightly file and request a corrected export from the source system.
Why it's wrong here
Rejecting the whole file discards valid rows that are perfectly usable, which is wasteful when only a small subset is malformed. This approach also does not fix the recurring silent-failure behavior in the ETL job, so the same problem will reappear the next night. It treats a data-quality symptom as a source-system failure without confirming where the invalid values originate.
- ✗
Impute today's date for any row where the order_date value cannot be parsed.
Why it's wrong here
Substituting the current date fabricates order timing that never occurred, corrupting any trend, seasonality, or fulfillment-latency analysis built on that column. Imputation is appropriate for genuinely missing values under a documented statistical method, not for values that are structurally invalid. This choice also leaves the ETL job still failing silently, so the underlying ingestion defect remains.
- ✓
Apply validation rules that flag invalid dates for quarantine while loading conforming rows, and log rejected records.
Why this is correct
This preserves valid records, isolates the malformed dates into a quarantine set for review, and creates an audit trail that makes the previously silent failures visible. It directly addresses the root cause by enforcing type and range validation at ingestion instead of letting bad values pass through. The conforming rows still load, so downstream reporting is not blocked while the source issue is investigated.
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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
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