MLS-C01 Exploratory Data Analysis Practice Question
A data scientist is working with a dataset containing customer transaction records stored in Amazon S3 as CSV files. The dataset has 500 columns and 2 million rows. The scientist wants to perform EDA to understand data types, missing values, and summary statistics for each column. They need to do this quickly and without writing custom code. The scientist has access to AWS Glue DataBrew and Amazon SageMaker Data Wrangler. Which approach should the scientist take?
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 create a profile job that outputs data quality reports
AWS Glue DataBrew provides a visual interface for data profiling and can handle large datasets without writing code. It automatically detects data types, missing values, and summary statistics. Option A is wrong because SageMaker Data Wrangler requires more manual setup and coding, and is not as straightforward for quick profiling. Option B is wrong because Amazon Athena requires writing SQL queries and is not a dedicated profiling tool. Option D is wrong because AWS Glue ETL with PySpark requires writing custom code.
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 SageMaker Data Wrangler to import the data and generate a report
Why it's wrong here
Amazon SageMaker Data Wrangler is excellent for data preparation and feature engineering, and it can generate data quality and insights reports, making it tempting for EDA. However, for quickly understanding data types, missing values, and summary statistics across 500 columns *without custom code*, AWS Glue DataBrew's automated data profiling capabilities are specifically designed for this initial exploratory analysis, offering a more direct and visual approach. Data Wrangler's reports are part of a broader ML data preparation workflow.
- ✗
Use Amazon Athena to run SELECT statements on each column
Why it's wrong here
Athena is for querying, not automated profiling; writing queries for 500 columns is tedious.
- ✓
Use AWS Glue DataBrew to create a profile job that outputs data quality reports
Why this is correct
DataBrew's profile job automatically computes statistics and detects missing values.
- ✗
Use AWS Glue ETL jobs with PySpark to compute statistics
Why it's wrong here
Writing custom PySpark code is not the quickest or code-free approach.
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
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