MLS-C01 Exploratory Data Analysis Practice Question
A data scientist is performing EDA on a large dataset (10 TB) stored in S3. They need to compute summary statistics for each column. Which approach is most cost-effective and efficient?
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 Amazon Athena with SQL queries
Amazon Athena is a serverless query service that allows you to run SQL queries directly on data stored in S3, charging only for the data scanned per query. This makes it highly cost-effective and efficient for computing summary statistics on large datasets without needing to provision or manage infrastructure. Option A is wrong because AWS Glue ETL jobs with PySpark have startup overhead and are better suited for complex data transformation tasks rather than simple ad-hoc analysis. Option C is wrong because downloading 10 TB to a SageMaker Studio notebook incurs high data transfer costs and requires substantial local storage, which is neither efficient nor cost-effective. Option D is wrong because launching an Amazon EMR cluster involves provisioning and managing compute resources, leading to higher costs and complexity for a straightforward statistical analysis that Athena can handle more simply.
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 an AWS Glue ETL job with PySpark to compute statistics
Why it's wrong here
Glue jobs have overhead and are more costly for simple aggregations.
- ✓
Use Amazon Athena with SQL queries
Why this is correct
Athena is serverless, cost-effective, and efficient for ad-hoc queries.
- ✗
Download the dataset to an Amazon SageMaker Studio notebook and use pandas
Why it's wrong here
Downloading 10 TB is expensive and time-consuming.
- ✗
Launch an Amazon EMR cluster and use Spark SQL
Why it's wrong here
EMR requires cluster management and is more expensive than serverless.
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 |
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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.