DA0-002 Data Acquisition and Preparation Practice Question
A healthcare analytics team is ingesting a nightly CSV extract of patient encounters. The extract occasionally contains rows where the 'discharge_date' is earlier than the 'admission_date'. The team wants the pipeline to flag these rows for review rather than silently load them. Which data preparation action best addresses this requirement?
⚠ Common exam trap
The trap here is treating a data quality exception as something to fix or delete automatically, when the stated requirement is to flag it for review.
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 a validation rule that compares admission_date and discharge_date and routes failing rows to a quarantine table with a reason code.
The requirement is to identify and isolate suspect records for human review. A validation rule comparing the two dates and routing failures to a quarantine table with a reason code achieves this without deleting data or contaminating the trusted layer. Dropping, passing through, or auto-correcting records all fail because they either lose information, hide the problem, or make unverified changes to clinical data.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Swap the values in admission_date and discharge_date whenever discharge_date is earlier.
Why it's wrong here
Automatically swapping dates assumes the only problem is transposition, but the cause could be a timezone offset, a data entry error, or a legitimate same-day discharge recorded incorrectly. Silently altering clinical timestamps corrupts the record of truth and may violate audit requirements. The team wants to review exceptions, not have the pipeline make unverified corrections to protected health information.
- ✗
Load the rows as-is and rely on downstream report filters to exclude invalid dates.
Why it's wrong here
Loading invalid temporal sequences without marking them allows bad records to contaminate aggregates, length-of-stay calculations, and quality metrics. Downstream filters are inconsistently applied and may be forgotten in new reports. The team explicitly wants flagged rows for review, so passing them through unchanged does not satisfy the governance and data quality requirement.
- ✓
Apply a validation rule that compares admission_date and discharge_date and routes failing rows to a quarantine table with a reason code.
Why this is correct
A validation rule that checks the chronological relationship and diverts violations to quarantine preserves the suspect records while preventing them from entering the trusted layer. Attaching a reason code supports triage and root-cause analysis. This matches the requirement to flag rather than silently load or delete, and it is a standard data quality control in healthcare ETL pipelines.
- ✗
Drop all rows where discharge_date is earlier than admission_date before loading.
Why it's wrong here
Dropping the rows removes potentially valid encounters that simply have a data entry or timezone issue, causing undercounting of patient events. The requirement is to flag rows for review, not to discard them. Silently deleting records also hides the underlying data quality problem from the clinical team, preventing root-cause correction at the source system.
Go deeper
Related to this question
About these practice questions
This DA0-002 question is part of Courseiva's 1,004-question bank — original exam-style content with full explanations and wrong-answer analysis, never real exam questions or exam dumps. 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.