DEA-C01 Data Ingestion and Transformation Practice Question

A data engineer needs to transform JSON data from Amazon S3 into Parquet format using AWS Glue. The data contains nested fields. Which Glue feature should the engineer use to define the schema and handle the nested structure?

⚠ Common exam trap

DEA-C01 often tests the misconception that other transforms like DropFields or FindMatches can handle nested structures, when Relationalize is the specific tool for that purpose.

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 'Relationalize' transform in a Glue ETL script.

The Relationalize transform in AWS Glue is specifically designed to convert nested JSON data into a relational format. It flattens nested structures by creating separate tables for arrays and nested objects, which can then be written to Parquet. This is the appropriate feature to handle nested fields when transforming JSON to Parquet.

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 the 'FindMatches' transform to identify duplicates.
Why it's wrong here
FindMatches identifies duplicate records for deduplication, not schema definition. It cannot infer nested JSON structures or flatten arrays into Parquet columns. A crawler or classifier defines the schema; FindMatches would suit a scenario where duplicate customer records must be merged.
✗
Use the 'DropFields' transform to remove nested fields.
Why it's wrong here
DropFields removes columns from the DynamicFrame, so nested structures the engineer needs to flatten and convert into Parquet would be discarded rather than resolved. It is the right transform when deliberately pruning sensitive or unneeded attributes before writing output, not for schema definition.
✓
Use the 'Relationalize' transform in a Glue ETL script.
Why this is correct
Relationalize flattens nested JSON structures into separate relational tables linked by keys, letting Glue's DynamicFrame handle arrays and structs that a flat schema cannot represent. This satisfies the requirement to define schema and process nested fields before writing Parquet.
✗
Use the 'Spigot' transform to write sample data.
Why it's wrong here
Spigot writes sample records to a destination for inspection during job development; it neither defines a schema nor resolves nested JSON structures for Parquet output. It is the correct choice when debugging transform logic by sampling data mid-pipeline, not for handling nesting.

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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