MLS-C01 Exploratory Data Analysis Practice Question
A machine learning engineer is performing exploratory data analysis on a large dataset stored in Amazon S3 using AWS Glue. The dataset contains a mix of numeric and categorical features. The engineer wants to efficiently compute summary statistics (e.g., mean, median, standard deviation) for the numeric columns. Which AWS service or feature should the engineer use to achieve this with minimal setup?
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 profile the dataset.
AWS Glue DataBrew provides a visual interface to profile data and compute summary statistics without writing code. Option A is wrong because launching an Amazon EMR cluster requires setup and management, which is not minimal. Option C is wrong because Amazon Athena requires writing SQL queries and does not automatically compute summary statistics. Option D is wrong because Amazon SageMaker Data Wrangler is a good tool but requires more configuration than DataBrew for simple summary statistics.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Launch an Amazon EMR cluster and use Spark.
Why it's wrong here
EMR requires cluster setup and management.
- ✓
Use AWS Glue DataBrew to profile the dataset.
Why this is correct
DataBrew provides an easy interface for profiling and statistics.
- ✗
Use Amazon Athena to run SQL queries on the data.
Why it's wrong here
Athena requires writing SQL queries and is less automated.
- ✗
Use Amazon SageMaker Data Wrangler.
Why it's wrong here
Amazon SageMaker Data Wrangler is designed for interactive, visual data preparation and feature engineering, including generating summary statistics and visualisations. While capable of producing summary statistics, it introduces the overhead of a SageMaker Studio environment setup, which is not "minimal setup" for simply computing statistics on a dataset already being processed via AWS Glue. The scenario focuses on leveraging Glue's existing capabilities for efficient, programmatic summary statistics, whereas Data Wrangler is for a more comprehensive, iterative data preparation workflow within SageMaker.
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
Related to this question
About these practice questions
Courseiva writes every MLS-C01 question from scratch — 1,672 in total, each with an explanation and a wrong-answer breakdown. None are copied from real exams or dumps. Learn why practice questions differ from exam dumps →
JA
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.