- A
Sort the data by transaction ID and then check consecutive rows for equality
Why wrong: This is more complex and less efficient than drop_duplicates.
- B
Use fuzzy matching to find similar transaction IDs
Why wrong: Fuzzy matching is for near duplicates, not exact duplicates.
- C
Group by all columns and aggregate with sum
Why wrong: This could incorrectly aggregate non-duplicate rows.
- D
Use the drop_duplicates method on the transaction ID column
drop_duplicates removes exact duplicate rows based on specified columns.
Quick Answer
The correct answer is to use the drop_duplicates method on the transaction ID column, as this directly removes duplicate rows based on a unique identifier without altering the original data structure. In pandas, the drop_duplicates function is the most efficient and straightforward approach for exact duplicate detection, allowing you to specify a subset of columns—like transaction ID—to identify and eliminate redundant entries while preserving the first occurrence. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this scenario tests your ability to choose the simplest, most performant data-cleaning method for structured tabular data, often appearing in pipeline or preprocessing questions where efficiency and data integrity are key. A common trap is overcomplicating the solution with groupby aggregations or fuzzy matching, which are unnecessary for exact duplicates and risk data loss or computational overhead. Memory tip: think “drop_dupes on ID” to keep it simple—exact duplicates need exact tools.
MLS-C01 Exploratory Data Analysis Practice Question
This MLS-C01 practice question tests your understanding of exploratory data analysis. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. 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 is building a data pipeline that aggregates customer transaction data. The engineer notices that some transactions have duplicate entries due to a system error. Which approach should the engineer use to identify and remove duplicates based on a unique transaction ID?
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
Use the drop_duplicates method on the transaction ID column
Option B is correct because dropping duplicates based on the transaction ID is straightforward and efficient. Option A is wrong because groupby with aggregation may lose information. Option C is wrong because fuzzy matching is for approximate matches, not exact duplicates. Option D is wrong because sorting then checking consecutive equals is more complex than needed.
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.
- ✗
Sort the data by transaction ID and then check consecutive rows for equality
Why it's wrong here
This is more complex and less efficient than drop_duplicates.
- ✗
Use fuzzy matching to find similar transaction IDs
Why it's wrong here
Fuzzy matching is for near duplicates, not exact duplicates.
- ✗
Group by all columns and aggregate with sum
Why it's wrong here
This could incorrectly aggregate non-duplicate rows.
- ✓
Use the drop_duplicates method on the transaction ID column
Why this is correct
drop_duplicates removes exact duplicate rows based on specified columns.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Detailed technical explanation
How to think about this question
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
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.
- Use explanations to understand the rule behind the answer.
TExam Day Tips
- Underline the problem statement mentally.
- 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 cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
What to study next
Got this wrong? Here's your next step.
Identify which MLS-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
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Exploratory Data Analysis — study guide chapter
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FAQ
Questions learners often ask
What does this MLS-C01 question test?
Exploratory Data Analysis — This question tests Exploratory Data Analysis — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Use the drop_duplicates method on the transaction ID column — Option B is correct because dropping duplicates based on the transaction ID is straightforward and efficient. Option A is wrong because groupby with aggregation may lose information. Option C is wrong because fuzzy matching is for approximate matches, not exact duplicates. Option D is wrong because sorting then checking consecutive equals is more complex than needed.
What should I do if I get this MLS-C01 question wrong?
Identify which MLS-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 20, 2026
This MLS-C01 practice question is part of Courseiva's free Amazon Web Services 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 MLS-C01 exam.
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