MLA-C01 Data Preparation for Machine Learning Practice Question

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?

⚠ Common exam trap

Test-takers frequently 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.

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

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.

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.
✗
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.

Quick reference

AWS S3 Storage Class Comparison

Storage Class	Min Duration	Retrieval	Use Case
S3 Standard	None	Immediate	Frequently accessed data
S3 Standard-IA	30 days	Immediate	Infrequent access, rapid retrieval
S3 One Zone-IA	30 days	Immediate	Non-critical infrequent data
S3 Intelligent-Tiering	None	Immediate–hours	Unknown or changing access patterns
S3 Glacier Instant	90 days	Milliseconds	Archive with instant retrieval
S3 Glacier Flexible	90 days	Minutes–hours	Archive, flexible retrieval
S3 Glacier Deep Archive	180 days	Hours	Long-term compliance archive

Go deeper

MLA-C01 Data Preparation for Machine Learning Practice Question

Why each option matters

Option-by-option breakdown

AWS S3 Storage Class Comparison

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