DEA-C01 Data Ingestion and Transformation Practice Question
A data engineer is building an AWS Glue ETL job that reads from an Amazon S3 bucket containing nested JSON files and must flatten the nested structures before writing to Amazon Redshift. The job uses the Glue DynamicFrame API. The engineer wants the transformation to run as a single pass without an intermediate shuffle. Which operation should the engineer use?
⚠ Common exam trap
Candidates often confuse ResolveChoice, which resolves type ambiguity, with Relationalize, which unnest nested structures; only Relationalize performs the flattening required by the scenario.
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
Relationalize is the Glue DynamicFrame transform specifically designed to unnest nested JSON into multiple flat frames in one traversal. It produces a root frame and one frame per nested array, each with a join key back to the parent. This matches the requirement to flatten nested structures before loading into Redshift without an intermediate shuffle.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
ResolveChoice
Why it's wrong here
ResolveChoice handles ambiguous column types by casting or retaining them, but it does not flatten nested structures. It is used when a column contains mixed types, not when a column is a nested struct or array. Applying it here would not produce the flat columns needed for Redshift and would leave the nested schema unchanged.
- ✗
ApplyMapping
Why it's wrong here
ApplyMapping selects, renames, and casts top-level fields but does not flatten nested structures. It operates on the schema's flat column list, so nested structs remain nested after the transformation. It would not produce the flattened output the pipeline requires and would leave nested JSON intact in the target Redshift table.
- ✓
Relationalize
Why this is correct
Relationalize flattens nested DynamicFrames into a set of related flat DynamicFrames in a single pass, producing one frame per nested array or struct. It is designed exactly for unnesting JSON before loading into a relational target such as Redshift, and it avoids a full shuffle because it processes the frame as a single traversal.
- ✗
DropNullFields
Why it's wrong here
DropNullFields removes columns that contain only null values. It does not flatten nested JSON or restructure the schema. Using it in this scenario would simply drop empty columns and leave the nested arrays and structs in place, so the output would still not match the flat Redshift target schema.
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
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Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Amazon Web Services exam blueprint
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