DEA-C01 Data Ingestion and Transformation Practice Question

A data engineer needs to transform JSON data from Amazon S3 into Parquet using AWS Glue. The JSON is nested and contains arrays. The engineer wants to flatten the nested structure and write the result to S3 partitioned by a 'region' field. Which combination of Glue transforms should the engineer use?

⚠ Common exam trap

Candidates often confuse Unbox, which extracts a single nested field, with Relationalize, which fully flattens nested structures and arrays into multiple relational tables.

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 to flatten the nested JSON, then write the resulting DynamicFrame with partition keys set to 'region'.

Relationalize is designed to flatten nested JSON and arrays into relational tables, producing a collection of DynamicFrames. The engineer can then select the relevant table, set the 'region' field as a partition key, and write Parquet to S3. This is the intended Glue transform for nested-to-relational conversion.

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 'ResolveChoice' transform to flatten nested data, then write with partition keys.
Why it's wrong here
ResolveChoice handles ambiguous data types (for example, a column that is sometimes int and sometimes string) by casting or creating separate columns. It does not flatten nested structures or arrays. Using it here would not address the nested JSON requirement and would leave the data in a nested shape that Parquet partitioning by region would not resolve.
✓
Use the 'Relationalize' transform to flatten the nested JSON, then write the resulting DynamicFrame with partition keys set to 'region'.
Why this is correct
Relationalize flattens nested structures and arrays into separate relational tables, producing a DynamicFrame collection. The engineer can select the desired table, set partition keys to 'region', and write to S3 in Parquet. This is the standard Glue approach for flattening nested JSON before writing partitioned output, and it avoids manual schema manipulation.
✗
Use the 'DropNullFields' transform to remove nulls, then write with partition keys.
Why it's wrong here
DropNullFields removes fields that are entirely null, which is useful for cleaning but does not flatten nested JSON. It would not transform arrays or nested objects into relational columns. Writing the result with partition keys would still leave nested structures, so this does not meet the flattening requirement.
✗
Use the 'Unbox' transform to extract nested fields, then use 'ApplyMapping' to rename columns, and write with partition keys.
Why it's wrong here
Unbox is used to extract a single nested field into a top-level column, not to flatten entire nested structures or arrays. ApplyMapping renames and casts fields but does not flatten arrays. For deeply nested JSON with arrays, Unbox alone would not produce a fully flattened table suitable for partitioned Parquet output. Relationalize is the appropriate transform.

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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