DA0-002 Data Acquisition and Preparation Practice Question
A data scientist is merging retail transaction data from online and in-store sources. Which THREE steps are required to ensure data consistency?
⚠ Common exam trap
The trap is selecting data-cleaning actions (dropping null customer IDs) or weak join keys (store location) instead of the true consistency steps of standardizing IDs, currency, and time zones.
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
✓
Ensure product IDs are standardized across sources
Option A is correct because standardizing product IDs across online and in-store sources is essential for matching the same item across systems, preventing duplicate or mismatched records during the merge. Option B is correct because converting all monetary amounts to a common currency ensures that transaction values are comparable and can be aggregated without unit inconsistencies. Option D is correct because synchronizing timestamps to a single time zone aligns event times across sources, which is necessary for accurate chronological ordering and time-based joins. Option C is not required because removing transactions with missing customer IDs would discard valid sales data and is a data-quality choice, not a consistency requirement. Option E is not required because merging on store location alone would ignore online transactions and other key fields, producing an incomplete and inconsistent dataset.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Ensure product IDs are standardized across sources
Why this is correct
Standardising product IDs gives both sources a shared join key, satisfying the consistency requirement for merging online and in-store transactions. Without identical identifiers, the same product appears as distinct records, producing duplicate or unmatched rows during the merge.
- ✓
Convert all monetary amounts to a common currency
Why this is correct
Converting monetary amounts to a single currency removes the unit mismatch between online and in-store transactions, satisfying the consistency requirement that values be expressed on a comparable scale before merging. Without this normalisation, aggregations and comparisons across sources would be meaningless, since identical numeric values would represent different underlying amounts.
- ✗
Remove all transactions with missing customer ID
Why it's wrong here
Dropping rows with missing customer IDs discards valid transactions and shrinks the dataset, rather than reconciling schemas, keys or formats between the two sources. It is tempting because null handling is a genuine data-quality task, and deletion is the right choice when a field is mandatory for the downstream analysis.
- ✓
Synchronize timestamps to a single time zone
Why this is correct
Synchronising timestamps to a single time zone aligns temporal values across sources, satisfying the consistency requirement for merged transactions. Without this, identical events recorded in different zones appear misordered, breaking sequence-dependent analysis such as same-day sales comparisons.
- ✗
Merge data using only store location
Why it's wrong here
Store location is not a shared key between online and in-store records, so joining on it produces mismatched or duplicated rows instead of aligning transactions. It is tempting because location is a legitimate dimension for regional aggregation and reporting, where grouping by store is exactly the intended operation.
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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
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.