MLA-C01 Data Preparation for Machine Learning Practice Question
A data engineer is building a feature engineering pipeline in AWS Glue ETL to process streaming data from Amazon Kinesis. The data includes a nested JSON structure with arrays. The engineer needs to flatten the nested structures into a tabular format for machine learning. Which THREE approaches are valid for this task? (Choose 3.)
⚠ Common exam trap
It's easy for candidates to confuse Athena's UNNEST (a query-time SQL function for static data) with a streaming transform, or assume SageMaker Processing can handle real-time streaming data, when in fact Glue ETL's native transforms are required for Kinesis streams.
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 Python's json.loads in a map function
Python's json.loads can be used within a PySpark map function to parse nested JSON strings from streaming data in AWS Glue ETL. This allows you to extract and flatten nested fields into a tabular structure by iterating over each record and converting the JSON into a flat dictionary, which can then be mapped to DataFrame columns.
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 Python's json.loads in a map function
Why this is correct
You can parse JSON strings and flatten them manually.
- ✗
Use Athena's UNNEST function on the raw data
Why it's wrong here
Athena is not part of AWS Glue ETL.
- ✓
Use PySpark's explode function on array columns
Why this is correct
explode converts array elements into separate rows.
- ✗
Use Amazon SageMaker Processing with scikit-learn
Why it's wrong here
SageMaker Processing is separate from Glue ETL.
- ✓
Use AWS Glue's Relationalize transform
Why this is correct
Relationalize converts nested JSON into relational form.
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