- A
Use the RemoveDuplicates built-in feature in Amazon QuickSight
Why wrong: QuickSight is a visualization tool; it can prepare data but not at scale for large datasets.
- B
Use the DistinctRows transform in Amazon SageMaker Data Wrangler
Why wrong: SageMaker Data Wrangler has a 'Drop Duplicates' step, but the name 'DistinctRows' is not standard to the service.
- C
Use the DropDuplicates transform in AWS Glue
Glue's DropDuplicates removes duplicate rows in a distributed manner.
- D
Use a SQL query with SELECT DISTINCT in Amazon Athena to create a deduplicated table
Athena's SELECT DISTINCT creates a deduplicated result set, which can be written to a new table.
- E
Use the pandas drop_duplicates() method in a SageMaker notebook
Why wrong: Pandas runs on a single instance and is not scalable for large datasets.
Quick Answer
The correct approaches are using a SQL query with SELECT DISTINCT in Amazon Athena and applying the DropDuplicates transform in AWS Glue. These two methods are appropriate because they leverage native AWS capabilities for deduplication at different stages of data preparation: Athena’s SELECT DISTINCT operates directly on data stored in S3 via SQL queries, making it ideal for ad-hoc analysis, while AWS Glue’s DropDuplicates transform works within its DynamicFrame API to remove duplicate rows during ETL pipelines, comparing all columns or a specified subset for scalable processing. On the AWS Certified Machine Learning Engineer Associate MLA-C01 exam, this question tests your understanding of when to use serverless querying versus managed ETL for data cleaning—a common trap is assuming only one tool can handle duplicates, but both Athena and Glue are valid depending on the workflow. Remember the memory tip: “Distinct in Athena, Drop in Glue” to pair each tool with its primary deduplication method.
MLA-C01 Data Preparation for Machine Learning Practice Question
This MLA-C01 practice question tests your understanding of data preparation for machine learning. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. 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 preparing a dataset for a classification model. The dataset contains duplicate rows. Which TWO approaches are appropriate to handle duplicates in AWS? (Choose 2.)
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 DropDuplicates transform in AWS Glue
Option C is correct because AWS Glue provides a DropDuplicates transform within its DynamicFrame API, which is designed for ETL operations on large-scale datasets. This transform efficiently removes duplicate rows by comparing all columns or a specified subset, making it a native and scalable solution for deduplication in AWS.
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.
- ✗
Use the RemoveDuplicates built-in feature in Amazon QuickSight
Why it's wrong here
QuickSight is a visualization tool; it can prepare data but not at scale for large datasets.
- ✗
Use the DistinctRows transform in Amazon SageMaker Data Wrangler
Why it's wrong here
SageMaker Data Wrangler has a 'Drop Duplicates' step, but the name 'DistinctRows' is not standard to the service.
- ✓
Use the DropDuplicates transform in AWS Glue
Why this is correct
Glue's DropDuplicates removes duplicate rows in a distributed manner.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Use a SQL query with SELECT DISTINCT in Amazon Athena to create a deduplicated table
Why this is correct
Athena's SELECT DISTINCT creates a deduplicated result set, which can be written to a new table.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use the pandas drop_duplicates() method in a SageMaker notebook
Why it's wrong here
Pandas runs on a single instance and is not scalable for large datasets.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse the existence of a feature name (e.g., 'DistinctRows' in Data Wrangler) with the actual available transform, or they incorrectly assume that any Python code in a SageMaker notebook qualifies as an 'AWS approach' rather than a custom script.
Detailed technical explanation
How to think about this question
Under the hood, AWS Glue's DropDuplicates transform leverages Apache Spark's dropDuplicates() method on DataFrames, which uses hash-based partitioning to identify and eliminate duplicate rows across distributed data. In real-world scenarios, this is critical when ingesting streaming data from sources like Kinesis or Kafka, where duplicates can arise from retries or network issues, and using Glue's native transform avoids the overhead of writing custom Spark code.
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
An e-commerce site experiences heavy traffic on Black Friday and near-zero traffic during off-peak weeks. Rather than provisioning permanent large VMs, the team uses auto-scaling groups that add capacity automatically under load and reduce it overnight. Questions like this test whether you understand elasticity, availability zones, and cloud compute scaling patterns.
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.
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Data Preparation for Machine Learning — study guide chapter
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FAQ
Questions learners often ask
What does this MLA-C01 question test?
Data Preparation for Machine Learning — This question tests Data Preparation for Machine Learning — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Use the DropDuplicates transform in AWS Glue — Option C is correct because AWS Glue provides a DropDuplicates transform within its DynamicFrame API, which is designed for ETL operations on large-scale datasets. This transform efficiently removes duplicate rows by comparing all columns or a specified subset, making it a native and scalable solution for deduplication in AWS.
What should I do if I get this MLA-C01 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
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Last reviewed: Jun 24, 2026
This MLA-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 MLA-C01 exam.
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