MLA-C01 Data Preparation for Machine Learning Practice Question
A data engineer is using AWS Glue to prepare a dataset for machine learning. The dataset has several columns with outliers. The engineer wants to detect and handle outliers in a scalable manner. Which TWO approaches should the engineer consider? (Select TWO.)
⚠ Common exam trap
It's easy for candidates to assume that only a single AWS service can handle outlier detection at scale, but the question requires selecting two approaches, and both Glue DynamicFrames and SageMaker Data Wrangler are valid, scalable, and managed AWS solutions for this task.
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 AWS Glue DynamicFrame with Apache Spark to compute interquartile range (IQR) and filter outliers.
AWS Glue DynamicFrames, built on Apache Spark, provide a scalable, distributed computing environment to compute statistical measures like the interquartile range (IQR) across large datasets. This allows the engineer to programmatically filter outliers without manual intervention, leveraging Spark's parallel processing for efficient handling of data at scale.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Manually remove outliers by inspecting the data in Amazon S3.
Why it's wrong here
Manual removal is not scalable.
- ✗
Train a neural network to identify anomalies and remove them.
Why it's wrong here
Using neural networks for outlier detection is complex and not necessary for basic outlier handling.
- ✗
Use pandas in a SageMaker notebook to calculate z-scores and filter outliers.
Why it's wrong here
pandas is not scalable for large datasets.
- ✓
Use AWS Glue DynamicFrame with Apache Spark to compute interquartile range (IQR) and filter outliers.
Why this is correct
Spark can handle large-scale data and IQR is a standard method.
- ✓
Use Amazon SageMaker Data Wrangler to apply an outlier detection transform.
Why this is correct
Data Wrangler has built-in transforms for outlier detection.
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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.