The answer is to use Amazon SageMaker Data Wrangler’s built-in outlier detection transform and to implement custom statistical filtering with AWS Glue DynamicFrames. SageMaker Data Wrangler provides a no-code visual interface for applying pre-built outlier detection algorithms, such as the interquartile range (IQR) or z-score, directly to your dataset at scale, while AWS Glue DynamicFrames leverage Apache Spark’s distributed computing to compute these same statistical measures programmatically across large datasets. On the AWS Certified Machine Learning Engineer Associate MLA-C01 exam, this question tests your ability to distinguish between fully managed visual tools and programmatic Spark-based approaches for scalable data preparation—a common trap is assuming only one method works, when in fact both SageMaker Data Wrangler and Glue DynamicFrames offer scalable outlier detection, just at different levels of abstraction. Remember the mnemonic “Wrangler for the click, Glue for the code” to recall that SageMaker Data Wrangler handles outliers with visual transforms, while Glue DynamicFrames give you Spark’s parallel power for custom IQR logic.
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 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.)
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.
Option D is correct because 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.
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.
✗
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.
Related concept
Read the scenario before looking for a memorised answer.
✓
Use Amazon SageMaker Data Wrangler to apply an outlier detection transform.
Why this is correct
Data Wrangler has built-in transforms for outlier detection.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may 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.
Detailed technical explanation
How to think about this question
The interquartile range (IQR) method defines outliers as data points falling below Q1 - 1.5*IQR or above Q3 + 1.5*IQR, which is a robust, non-parametric approach that does not assume a normal distribution. In AWS Glue, DynamicFrames can apply this logic using Spark SQL or DataFrame operations, enabling distributed computation across partitions. Amazon SageMaker Data Wrangler offers a visual interface to apply built-in transforms like 'Handle Outliers' using IQR or z-score, which automatically generates a processing script that can be executed at scale on SageMaker Processing jobs.
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 media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
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.
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 AWS Glue DynamicFrame with Apache Spark to compute interquartile range (IQR) and filter outliers. — Option D is correct because 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.
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.
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Question Discussion
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