DEA-C01 Data Ingestion and Transformation Practice Question

A data engineer is building an AWS Glue ETL job that reads a large Amazon S3 dataset of nested JSON files and must flatten the nested arrays into separate rows for downstream analytics. The engineer needs the most efficient, code-free way to apply this transformation within the Glue job. Which approach should the engineer use?

⚠ Common exam trap

The trap here is assuming a crawler or filter can reshape nested data, when only a dedicated Flatten transformation un-nests arrays into rows.

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 the AWS Glue Studio visual editor and add a Flatten transformation to the job.

The Flatten transformation in AWS Glue Studio is purpose-built to un-nest arrays and structs into rows without custom code, directly satisfying the need for an efficient, code-free flattening step in a Glue ETL job on nested JSON.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

✗
Configure the Glue crawler to automatically flatten nested JSON during cataloging.
Why it's wrong here
A Glue crawler only infers schema and populates the Data Catalog; it does not transform data or flatten arrays into rows. Crawling nested JSON would still leave the data nested, so this approach cannot deliver the required flattened output.
✓
Use the AWS Glue Studio visual editor and add a Flatten transformation to the job.
Why this is correct
The Flatten transformation in AWS Glue Studio un-nests nested structures such as arrays and structs into separate rows without writing custom code, which matches the requirement for a code-free, efficient transformation of nested JSON. It is designed exactly for this scenario and integrates with the visual job editor.
✗
Write a custom Python shell script in Glue to recursively flatten the JSON.
Why it's wrong here
A Python shell job is not the most efficient or code-free option for large-scale nested JSON flattening compared to the built-in Flatten transformation. It requires custom code and may not scale as well as Spark-based transformations within Glue Studio.
✗
Use an Apache Spark filter transformation to explode the nested arrays.
Why it's wrong here
A filter transformation only removes rows based on a condition; it does not un-nest arrays. Using it here would not produce the required flattened rows and would leave nested structures intact, so it fails to meet the flattening requirement within the Glue job.

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

DEA-C01 Data Ingestion and Transformation Practice Question

Why each option matters

Option-by-option breakdown

AWS S3 Storage Class Comparison

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