- A
Use AWS Glue Transform with the FillMissingValues transform specifying the median strategy
Why wrong: This is incorrect because Glue's FillMissingValues does not support median strategy; it uses mean or mode. The actual correct approach is to use a custom transform or SageMaker Data Wrangler.
- B
Use a custom Python script with pandas to compute median and fill missing values, then upload to S3
Why wrong: Pandas runs on a single machine and may not scale to large datasets.
- C
Use a custom PySpark script in AWS Glue to compute median and fill missing values
PySpark provides the scalability of Spark with the ability to compute median (e.g., using approxQuantile) and fill missing values, making it efficient for large datasets.
- D
Use Amazon Athena SQL query to compute median and update the table
Why wrong: Athena is a query engine and does not support updating tables; creating a new table requires additional steps.
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 company is building a machine learning model on customer transaction data stored in Amazon S3. The data includes columns with missing values in the 'age' field. The data scientist wants to impute missing values with the median age across all customers. Which approach is MOST efficient for preparing the data at scale?
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 a custom PySpark script in AWS Glue to compute median and fill missing values
Option C is correct because AWS Glue with PySpark provides a distributed, scalable environment that can efficiently compute the median and fill missing values across large datasets stored in S3. PySpark's DataFrame API handles the median computation natively, and the Glue job runs on a managed Spark cluster, making it the most efficient approach for data preparation at scale without moving data out of the AWS ecosystem.
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 AWS Glue Transform with the FillMissingValues transform specifying the median strategy
Why it's wrong here
This is incorrect because Glue's FillMissingValues does not support median strategy; it uses mean or mode. The actual correct approach is to use a custom transform or SageMaker Data Wrangler.
- ✗
Use a custom Python script with pandas to compute median and fill missing values, then upload to S3
Why it's wrong here
Pandas runs on a single machine and may not scale to large datasets.
- ✓
Use a custom PySpark script in AWS Glue to compute median and fill missing values
Why this is correct
PySpark provides the scalability of Spark with the ability to compute median (e.g., using approxQuantile) and fill missing values, making it efficient for large datasets.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use Amazon Athena SQL query to compute median and update the table
Why it's wrong here
Athena is a query engine and does not support updating tables; creating a new table requires additional steps.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often assume AWS Glue Transform's FillMissingValues supports median, but it only supports mean or static values, leading them to choose Option A without verifying the available strategies.
Detailed technical explanation
How to think about this question
Under the hood, PySpark's median computation uses the approxQuantile method, which employs a Greenwald-Khanna algorithm for approximate quantiles, balancing accuracy and performance on distributed data. In real-world scenarios, when dealing with terabytes of customer transaction data, a single-node pandas script would run out of memory or take hours, whereas a Glue PySpark job can scale horizontally across multiple workers. Additionally, Glue automatically handles partitioning and compression, optimizing write performance back to S3.
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.
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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 a custom PySpark script in AWS Glue to compute median and fill missing values — Option C is correct because AWS Glue with PySpark provides a distributed, scalable environment that can efficiently compute the median and fill missing values across large datasets stored in S3. PySpark's DataFrame API handles the median computation natively, and the Glue job runs on a managed Spark cluster, making it the most efficient approach for data preparation at scale without moving data out of the AWS ecosystem.
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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