MLS-C01 Exploratory Data Analysis Practice Question
A data scientist needs to analyze a dataset stored in Amazon S3 as CSV files. The dataset contains 100 columns, and the data scientist wants to quickly understand the distribution of each column, including missing values, data types, and basic statistics. Which AWS service is best suited for this 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
✓
AWS Glue DataBrew
AWS Glue DataBrew (Option A) is correct because it provides visual data profiling and preparation without writing code, allowing users to quickly understand distributions, missing values, data types, and basic statistics. Option B (Amazon SageMaker Data Wrangler) is designed for data preparation and feature engineering within the SageMaker ecosystem, but it is more focused on transforming data for machine learning rather than initial exploratory analysis. Option C (Amazon QuickSight) is a business intelligence tool for creating visualizations and dashboards, not for profiling raw datasets. Option D (Amazon Athena) is an interactive query service that can analyze data in S3 using SQL, but it does not offer built-in data profiling capabilities for quick exploration.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
AWS Glue DataBrew
Why this is correct
AWS Glue DataBrew provides visual data profiling and preparation without coding, making it ideal for quickly understanding dataset characteristics.
- ✗
Amazon SageMaker Data Wrangler
Why it's wrong here
Amazon SageMaker Data Wrangler is used for data preparation and feature engineering within SageMaker, not primarily for initial data profiling.
- ✗
Amazon QuickSight
Why it's wrong here
Amazon QuickSight is a BI tool for visualization and dashboarding, not a data profiling service.
- ✗
Amazon Athena
Why it's wrong here
Amazon Athena is an interactive query service for SQL-based analysis, but it lacks built-in profiling features for automated column statistics.
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.