The answer is to remove duplicate rows, as this is the most appropriate data cleaning step when a dataset contains duplicate entries. Deduplication in data pipelines is a critical preprocessing task because duplicate rows artificially inflate the weight of certain observations, leading to biased statistical analyses and skewed machine learning model training. By eliminating redundant records, you preserve the true data distribution and ensure data integrity without introducing artificial values. On the CompTIA AI+ AI0-001 exam, this question tests your understanding of fundamental data cleaning techniques within the broader context of preparing data for AI workflows. A common trap is confusing deduplication with imputation or normalization—remember that duplicates are about overrepresentation, not missing or mis-scaled values. A helpful memory tip: “Duplicates double down on bias—drop them to keep your data honest.”
AI0-001 AI Models and Data Engineering Practice Question
This AI0-001 practice question tests your understanding of ai models and data engineering. Compare every option against the stated constraints before choosing — the best answer satisfies all requirements, not just the most obvious one. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
A data engineer discovers that a dataset contains duplicate rows. Which data cleaning step is MOST appropriate?
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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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.
Related concept
Read the scenario before looking for a memorised answer.
✗
Convert duplicates to categorical.
Why it's wrong here
Converting duplicates to categorical is not a data cleaning step for duplicates.
Common exam traps
Common exam trap: answer the scenario, not the keyword
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.
Detailed technical explanation
How to think about this question
Under the hood, deduplication often relies on hashing row values or using a unique key to identify exact duplicates. In distributed systems like Apache Spark, the `dropDuplicates()` method performs a shuffle to group identical rows and retain only one, which can be computationally expensive but ensures consistency. A real-world scenario is in customer transaction logs where duplicate entries from retries or system glitches must be removed to avoid inflating revenue metrics.
KKey Concepts to Remember
Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.
TExam Day Tips
→Watch for words such as best, first, most likely and least administrative effort.
→Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A small business has 20 workstations on the 192.168.1.0/24 network and one public IP from its ISP. The router uses PAT (NAT overload) so all 20 devices share one public address using different source ports. NAT questions test whether you understand the four address terms and which direction each translation applies.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
AI Models and Data Engineering — This question tests AI Models and Data Engineering — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: 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.
What should I do if I get this AI0-001 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
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: Deduplication is the process of identifying and removing duplicate records to ensure data quality. Data validation checks for schema or format errors. Data transformation changes data structure or values. Data encryption ensures security but does not address duplicates.
Last reviewed: Jun 30, 2026
Question Discussion
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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.
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