The answer is to use AWS Glue’s Relationalize transform, Python’s json.loads within a PySpark map function, and the unnest option in the AWS Glue DynamicFrame. These three approaches are valid because they each handle the core challenge of flattening nested JSON in Glue ETL: converting hierarchical structures with arrays into a tabular format suitable for machine learning feature engineering. The Relationalize transform automatically breaks nested JSON into separate tables linked by keys, while json.loads in a map function gives you fine-grained control over parsing streaming data from Kinesis, and the DynamicFrame’s unnest option directly expands nested fields into columns. On the AWS Certified Machine Learning Engineer Associate MLA-C01 exam, this question tests your ability to choose the right tool for data preprocessing in a streaming pipeline—a common scenario where candidates mistakenly rely on SQL-only solutions or forget that Glue’s native transforms handle arrays. A helpful memory tip: think “R-U-M” for Relationalize, Unnest, and Map—three ways to flatten nested JSON in Glue.
MLA-C01 Data Preparation for Machine Learning Practice Question
This MLA-C01 practice question tests your understanding of data preparation for machine learning. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
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.)
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
Option A is correct because 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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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.
Related concept
Read the scenario before looking for a memorised answer.
✗
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.
Related concept
Read the scenario before looking for a memorised answer.
✗
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.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often 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.
Detailed technical explanation
How to think about this question
Under the hood, AWS Glue ETL uses Apache Spark's DataFrame API, where the explode function (Option C) is a built-in transformation that expands array columns into multiple rows, effectively flattening nested structures. The Relationalize transform (Option E) is a Glue-specific feature that automatically converts complex nested JSON into a set of related tables by detecting arrays and creating foreign key relationships, which is particularly useful for highly nested schemas with multiple levels of arrays.
KKey Concepts to Remember
Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.
TExam Day Tips
→Watch for words such as best, first, most likely and least administrative effort.
→Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Data Preparation for Machine Learning — This question tests Data Preparation for Machine Learning — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Use Python's json.loads in a map function — Option A is correct because 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.
What should I do if I get this MLA-C01 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Question Discussion
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