DA0-002 Data Acquisition and Preparation Practice Question
A retail company's data analytics team needs to acquire point-of-sale (POS) transaction data from 200 stores daily. Each store sends a CSV file via email at the end of the day. The files often arrive late, have inconsistent column names (e.g., "StoreID", "Store_ID", "store_id"), and occasionally contain corrupted rows. The team manually processes these files, leading to frequent errors and delays. The company wants to automate the acquisition process to ensure data is available by 9 AM the next business day with high quality. Which approach best addresses these issues?
⚠ Common exam trap
Candidates often choose Option C or D because they seem more 'modern' or 'direct,' but they fail to recognize that the question specifically requires handling existing CSV files and late arrivals, which a script-based ETL approach (Option A) directly solves without requiring stores to change their behavior or infrastructure.
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
✓
Create a script to automatically download email attachments, validate and standardize columns, and flag corrupted rows for review
It directly addresses all three issues: automating the retrieval of email attachments (handling late arrivals), standardizing inconsistent column names via a script (e.g., mapping 'StoreID', 'Store_ID', 'store_id' to a canonical schema), and implementing validation logic to flag corrupted rows for manual review. This approach ensures data is processed reliably by 9 AM without manual intervention, meeting the automation and quality requirements.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Create a script to automatically download email attachments, validate and standardize columns, and flag corrupted rows for review
Why this is correct
Automated download, column standardisation and corrupted-row flagging directly address the three stated problems: late manual handling, inconsistent column names such as StoreID versus store_id, and corrupted rows. Scripted validation enforces quality and delivers data by 9 AM without manual intervention.
- ✗
Hire a data entry contractor to manually check and re-enter data
Why it's wrong here
Manual re-entry reproduces the human error and delay the team is trying to eliminate, and cannot scale to 200 stores within the overnight window. Contractors suit one-off remediation or low-volume exception handling, not recurring automated acquisition requiring consistent column mapping and row validation.
- ✗
Ask stores to use a standardized web form to enter data directly into a cloud database
Why it's wrong here
Direct web-form entry shifts effort to store staff and still permits inconsistent values and no validation of corrupted rows, while removing the file-based pipeline the team can automate. Web forms suit capturing new structured records at source, not ingesting existing POS exports with schema harmonisation.
- ✗
Implement a VPN so stores can connect to the central database and write transactions in real time
Why it's wrong here
Real-time VPN writes bypass the daily batch window entirely, so no file-arrival or schema-normalisation step exists to guarantee 9 AM availability; stores would also need direct database credentials. VPNs suit continuous, low-latency transactional replication, not scheduled CSV ingestion with cleansing.
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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.