MLS-C01 Exploratory Data Analysis Practice Question
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 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 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 A (EMR with Apache Spark) is more complex and requires writing code. Option B (Athena with JSON SerDe) can query but requires SQL that handles arrays. Option D (QuickSight) is visualization only and cannot flatten or compute session duration.
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.
- ✗
Use Amazon QuickSight to visualize the raw data without flattening.
Why it's wrong here
QuickSight cannot flatten nested JSON.
Quick reference
AWS S3 Storage Class Comparison
| Storage Class | Min Duration | Retrieval | Use Case |
|---|---|---|---|
| S3 Standard | None | Immediate | Frequently accessed data |
| S3 Standard-IA | 30 days | Immediate | Infrequent access, rapid retrieval |
| S3 One Zone-IA | 30 days | Immediate | Non-critical infrequent data |
| S3 Intelligent-Tiering | None | Immediate–hours | Unknown or changing access patterns |
| S3 Glacier Instant | 90 days | Milliseconds | Archive with instant retrieval |
| S3 Glacier Flexible | 90 days | Minutes–hours | Archive, flexible retrieval |
| S3 Glacier Deep Archive | 180 days | Hours | Long-term compliance archive |
Go deeper
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.