MLS-C01 Exploratory Data Analysis Practice Question
A data scientist is exploring a dataset stored in an Amazon S3 bucket. The dataset contains both numerical and categorical features. The scientist wants to compute summary statistics (mean, median, standard deviation) for all numerical features and count the distinct values for categorical features. Which AWS service is most appropriate for this task with minimal coding?
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 is the most appropriate service for this task because it provides a visual, no-code interface for data preparation and profiling. It can automatically compute summary statistics (mean, median, standard deviation) for numerical features and count distinct values for categorical features without writing any code. Amazon Athena requires writing SQL queries, which is not 'minimal coding' and is less suitable for profiling. AWS Glue ETL jobs require writing Python or Scala code, so it is more code-intensive. Amazon SageMaker Data Wrangler also requires some setup and integration with SageMaker, and while it can perform similar tasks, it is not as straightforward for simple profiling as DataBrew. Amazon EMR requires managing clusters and writing code, making it the least minimal coding 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.
- ✗
Amazon Athena
Why it's wrong here
Amazon Athena requires writing SQL queries, which involves coding and is not minimal for profiling tasks.
- ✗
AWS Glue ETL jobs
Why it's wrong here
AWS Glue ETL jobs require writing Python or Scala code, so it is more code-intensive than DataBrew.
- ✓
AWS Glue DataBrew
Why this is correct
AWS Glue DataBrew provides a visual, no-code interface for data profiling, making it ideal for minimal coding.
- ✗
Amazon SageMaker Data Wrangler
Why it's wrong here
Amazon SageMaker Data Wrangler requires some coding and setup, making it less straightforward for simple profiling.
- ✗
Amazon EMR
Why it's wrong here
Amazon EMR involves managing clusters and writing code, making it the least minimal coding option.
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.