Question 42 of 835
MLA-C01 Data Preparation for Machine Learning Practice Question
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.)
⚠ Common exam trap
It's easy for candidates to 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.
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
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.
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.
- ✓
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.
- ✗
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.
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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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