MLA-C01 AWS Glue ETL Practice Question

A company has 10 TB of log data in compressed JSON format stored in Amazon S3. The data needs to be processed and transformed into a structured format for machine learning. The processing requires complex transformations, including parsing nested JSON and joining with a reference table. The company wants to minimize infrastructure management. Which approach should the company use?

⚠ Common exam trap

Candidates may assume that large-scale data (10 TB) requires a provisioned cluster like EMR, but AWS Glue can scale to petabyte-scale workloads and is fully serverless, aligning with the goal of minimizing infrastructure management.

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 ETL with PySpark.

AWS Glue ETL with PySpark (Option D) is the best choice because it provides a fully serverless environment, minimizing infrastructure management. Glue can handle complex transformations like parsing nested JSON and joining with reference tables using PySpark, and it scales automatically for large datasets (10 TB). Amazon EMR (Option C) requires cluster management and provisioning, which contradicts the goal of minimizing management overhead.

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 SageMaker Processing jobs to run custom scripts.
Why it's wrong here
SageMaker Processing jobs run custom scripts on managed instances, but parsing nested JSON and joining a reference table across 10 TB demands distributed compute that single-instance processing handles poorly. AWS Glue with Spark is the managed choice for large-scale ETL with complex transformations.
✗
Use Amazon Athena to query and transform the data.
Why it's wrong here
Athena queries data in place and returns results, but it does not orchestrate multi-step transformations such as nested JSON parsing joined to a reference table, nor write a structured dataset. It is tempting because Athena is serverless and reads S3 directly, which suits ad hoc SQL querying of logs.
✗
Use Amazon EMR with Apache Spark.
Why it's wrong here
Amazon EMR with Apache Spark handles nested JSON parsing and reference-table joins, but it requires you to provision and manage clusters, contradicting the minimise-infrastructure requirement. It is tempting because Spark excels at exactly these complex transformations; EMR would be the right choice when cluster-level tuning, custom libraries, or very large-scale Spark workloads justify that operational overhead.
✓
Use AWS Glue ETL with PySpark.
Why this is correct
AWS Glue ETL with PySpark handles nested JSON parsing and reference-table joins at scale, while remaining serverless. This satisfies the 10 TB dataset and the requirement to minimise infrastructure management, since Glue provisions and scales the Spark environment automatically.

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 AWS Glue ETL Practice Question

Why each option matters

Option-by-option breakdown

AWS S3 Storage Class Comparison

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