Question 971 of 835
MLA-C01 Data Preparation for Machine Learning Practice Question
A team is using AWS Glue to process streaming data from Amazon Kinesis. The streaming data contains both structured and semi-structured fields. The team needs to flatten the semi-structured fields into columns for downstream ML training. Which Glue feature is BEST suited?
⚠ Common exam trap
A common mix-up: candidates confuse 'flattening semi-structured data' with simple schema operations like type resolution or column mapping, leading them to choose ResolveChoice or ApplyMapping instead of the specialized Relationalize transform.
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 transform
The Relationalize transform is specifically designed to flatten nested JSON or semi-structured fields into a relational structure, making it ideal for converting complex streaming data from Kinesis into flat columns for ML training. It automatically handles arrays and structs by creating separate tables or columns, which is exactly what the team needs for downstream processing.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Relationalize transform
Why this is correct
Relationalize recursively flattens nested data into separate tables or columns.
- ✗
Spigot transform
Why it's wrong here
Spigot is used for writing sample data for debugging.
- ✗
ResolveChoice transform
Why it's wrong here
ResolveChoice handles data type ambiguity, not flattening.
- ✗
ApplyMapping transform
Why it's wrong here
ApplyMapping maps columns but does not flatten nested structures.
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Last reviewed: Jun 24, 2026
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