Question 57 of 1,711
DEA-C01 Data Ingestion and Transformation Practice Question
A data engineer needs to transform JSON data from an S3 bucket using AWS Glue. The JSON contains nested arrays and objects. Which Glue transform is best suited for flattening nested structures?
⚠ Common exam trap
Many candidates confuse the generic Spark SQL function `explode` (or the concept of 'unnesting') with a named AWS Glue transform, leading them to select 'Unnest' even though it does not exist as a Glue transform and would require manual handling of multiple nesting levels.
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
✓
Relationalize
The Relationalize transform is specifically designed to flatten nested JSON structures (arrays and objects) into a set of related tables, making it ideal for this use case. It automatically handles complex nesting by creating separate DataFrames for each nested level and linking them via foreign keys, which is exactly what is needed when ingesting JSON with nested arrays and objects into a relational format.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Unnest
Why it's wrong here
Unnest is not a standard Glue transform.
- ✗
ResolveChoice
Why it's wrong here
ResolveChoice handles schema ambiguities, not nesting.
- ✓
Relationalize
Why this is correct
Relationalize flattens nested structures into separate DynamicFrames.
- ✗
Map
Why it's wrong here
Map applies a function to each row, not for unnesting.
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 |
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Last reviewed: Jun 11, 2026
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