DA0-002 Data Analysis Practice Question
A data analyst needs to combine two datasets that have the same columns but different rows. Which operation should they use?
⚠ Common exam trap
Candidates often confuse 'concatenate' (which can mean row-wise or column-wise) with 'append' (which specifically means row-wise stacking), leading them to choose Option A when the question explicitly requires combining rows.
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
✓
Append
(Append) is correct because appending is the standard operation for combining two datasets with identical columns but different rows, stacking the rows from one dataset onto the other. In tools like SQL, this is achieved with the UNION or UNION ALL operator, and in Python pandas, it is done via the `append()` method or `pd.concat()` with axis=0. This operation preserves the column structure while extending the row count.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Concatenate
Why it's wrong here
Concatenate can be ambiguous; in pandas, it can combine along rows or columns, but append is more specific.
- ✓
Append
Why this is correct
Append adds rows from one dataset to another with same columns.
- ✗
Merge
Why it's wrong here
Merge is for combining on a common key, not simply adding rows.
- ✗
Aggregate
Why it's wrong here
Aggregate summarizes data, not combine rows.
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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.