DEA-C01 Data Ingestion and Transformation Practice Question
A data engineer is using AWS Glue to transform data from Amazon S3 and load it into Amazon S3 in Parquet format. The source data is in JSON format and contains nested structures. The engineer needs to flatten the nested data and write it to Parquet. Which AWS Glue transform should the engineer use to flatten the nested structure?
⚠ Common exam trap
The trap here is assuming that a generic transform like Map can flatten nested data with custom code, but Relationalize is the specialized transform for this 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
✓
Relationalize
The Relationalize transform in AWS Glue is designed to flatten nested JSON structures into a relational format. It automatically creates multiple DynamicFrames for nested arrays and objects, generating keys to join them. This makes it the correct choice for the scenario, as it simplifies the flattening process without custom code.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Map
Why it's wrong here
The Map transform applies a custom function to each record, allowing for complex transformations. While it can be used to flatten nested data by writing custom code, it is not a built-in flattening transform. The Relationalize transform is purpose-built for this task and is more efficient and simpler to use.
- ✓
Relationalize
Why this is correct
The Relationalize transform in AWS Glue flattens nested JSON structures into a relational schema, producing multiple tables that can be joined. It is specifically designed to handle nested data and is the correct choice for flattening. It preserves the relationships between the flattened tables through generated keys.
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ApplyMapping
Why it's wrong here
ApplyMapping is used to rename, change data types, or drop fields in a DynamicFrame. It does not flatten nested structures. While it can select nested fields, it does not break them into separate columns or tables. It is not suitable for flattening complex nested JSON.
- ✗
Filter
Why it's wrong here
The Filter transform is used to select a subset of records based on a condition. It does not alter the structure of the data or flatten nested fields. It is used for row-level filtering, not for schema transformation. Therefore, it cannot flatten nested JSON.
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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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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