The answer is AWS Glue DataBrew, as it provides the most efficient approach for flattening nested JSON and computing user session durations during exploratory data analysis. DataBrew’s visual interface handles nested arrays directly, allowing you to unnest complex JSON structures and derive new columns—like session duration—without writing any code, which is ideal for rapid EDA on clickstream data stored in S3. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your ability to choose the right tool for data preparation versus querying or processing; a common trap is selecting Athena with JSON SerDe, which requires manual SQL for array manipulation, or EMR with Spark, which adds unnecessary complexity for a visual, code-free task. Remember: when the goal is quick, no-code flattening and metric derivation for EDA, DataBrew is the go-to—think “brew your JSON flat.”
MLS-C01 Exploratory Data Analysis Practice Question
This MLS-C01 practice question tests your understanding of exploratory data analysis. 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 scientist is analyzing clickstream data from a website. The data is stored in Amazon S3 as JSON files, each containing nested arrays. The scientist needs to flatten the nested structures and compute user session durations. Which approach is most efficient for this EDA task?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use AWS Glue DataBrew to flatten the JSON and create new columns for session duration.
AWS Glue DataBrew provides a visual interface to flatten nested JSON and compute derived metrics like session duration without writing code. Option B (Athena with JSON SerDe) can query but requires SQL that handles arrays. Option C (EMR with Spark) is more complex. Option D (QuickSight) is visualization only.
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 Amazon EMR with Apache Spark to process the data.
Why it's wrong here
EMR/Spark is powerful but overkill for this EDA task.
✗
Use Amazon Athena with JSON SerDe to query the data and compute session duration with SQL.
Why it's wrong here
Athena can query but SQL for nested arrays is complex and less efficient for large datasets.
✓
Use AWS Glue DataBrew to flatten the JSON and create new columns for session duration.
Why this is correct
DataBrew is built for data preparation and can handle nested JSON visually.
Related concept
Read the scenario before looking for a memorised answer.
✗
Use Amazon QuickSight to visualize the raw data without flattening.
Common exam trap: answer the scenario, not the keyword
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Detailed technical explanation
How to think about this question
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
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.
Use explanations to understand the rule behind the answer.
TExam Day Tips
→Underline the problem statement mentally.
→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 media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
What to study next
Got this wrong? Here's your next step.
Identify which MLS-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
Exploratory Data Analysis — This question tests Exploratory Data Analysis — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Use AWS Glue DataBrew to flatten the JSON and create new columns for session duration. — AWS Glue DataBrew provides a visual interface to flatten nested JSON and compute derived metrics like session duration without writing code. Option B (Athena with JSON SerDe) can query but requires SQL that handles arrays. Option C (EMR with Spark) is more complex. Option D (QuickSight) is visualization only.
What should I do if I get this MLS-C01 question wrong?
Identify which MLS-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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This MLS-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 MLS-C01 exam.
Question Discussion
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