Quick Answer
The Relationalize transform is the best choice for flattening nested JSON in AWS Glue streaming data because it is purpose-built to convert complex, semi-structured fields like arrays and structs into a flat, relational format suitable for downstream ML training. This transform automatically breaks nested structures into separate tables or columns, handling the inherent variability of streaming data from Amazon Kinesis without requiring manual schema mapping. On the AWS Certified Machine Learning Engineer Associate MLA-C01 exam, this question tests your ability to distinguish between Glue’s built-in transforms—Relationalize is often the correct answer when the scenario involves semi-structured streaming data, while options like the ResolveChoice transform or custom Spark code are traps for simpler schema conflicts. A common memory tip is to think of Relationalize as “unfolding” nested JSON into a flat table, much like flattening a crumpled piece of paper into a single sheet. Remember the mnemonic “R for Relationalize, R for Relational output” to quickly recall its purpose when you see nested streaming data in a 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 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?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "best"
Why it matters: Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
Relationalize transform
Relationalize recursively flattens nested data into separate tables or columns.
Spigot transform
Why wrong: Spigot is used for writing sample data for debugging.
ResolveChoice transform
Why wrong: ResolveChoice handles data type ambiguity, not flattening.
ApplyMapping transform
Why wrong: ApplyMapping maps columns but does not flatten nested structures.
Answer choices
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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
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.
Clue confirmation
The clue word "best" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
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.
Common exam traps
The trap here is that 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.
Detailed technical explanation
Under the hood, the Relationalize transform uses a recursive algorithm to detect nested fields (arrays and structs) and generates a relational schema by creating separate DynamicFrames for each nesting level, with foreign keys linking them. This is particularly useful in real-world scenarios where streaming data from Kinesis often contains deeply nested JSON from IoT devices or clickstream logs, and flattening is required for feature engineering in ML pipelines. The transform also handles null values and schema evolution gracefully, which is critical for streaming data with varying structures.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
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
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FAQ
Data Preparation for Machine Learning — This question tests Data Preparation for Machine Learning — Read the scenario before looking for a memorised answer..
The correct answer is: 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.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Yes — watch for: "best". Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 24, 2026
This MLA-C01 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the MLA-C01 exam.
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