AI0-001 AI Models and Data Engineering Practice Question
A data engineer discovers that a dataset contains duplicate rows. Which data cleaning step is MOST appropriate?
⚠ Common exam trap
CompTIA often tests the misconception that 'keeping the first occurrence' is a valid deduplication strategy, but in data engineering, this is arbitrary and can lead to data loss or bias, whereas explicit removal is the standard practice.
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
✓
Remove duplicate rows.
Removing duplicate rows is the most appropriate data cleaning step because duplicate rows can bias statistical analyses and machine learning models by overrepresenting certain observations. In data engineering, deduplication is a standard preprocessing step to ensure data integrity and avoid skewed results. Option C directly addresses this by eliminating redundant entries without introducing artificial values or altering the data distribution.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Keep only the first occurrence.
Why it's wrong here
While this removes duplicates, it is less explicit than removal; both A and C are plausible but A is the direct action.
- ✗
Fill duplicates with the mean.
Why it's wrong here
Filling duplicates with mean does not resolve duplication.
- ✓
Remove duplicate rows.
Why this is correct
Removing duplicates ensures each observation is unique.
- ✗
Convert duplicates to categorical.
Why it's wrong here
Converting duplicates to categorical is not a data cleaning step for duplicates.
About these practice questions
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Same concept, more angles
1 more way this is tested on AI0-001
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A data pipeline processes customer data from multiple sources. The data quality check reveals duplicate records. Which step should the pipeline include to handle this?
medium- ✓ A.Data deduplication
- B.Data encryption
- C.Data transformation
- D.Data validation
Why A: Duplicate records in a data pipeline compromise data integrity and downstream analytics. Data deduplication (Option A) is the correct step because it identifies and removes redundant entries based on key fields or fuzzy matching, ensuring each customer record is unique. This is a core data quality operation in ETL pipelines, often implemented via hash-based comparison or SQL window functions like ROW_NUMBER().
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This AI0-001 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 AI0-001 exam.