MLS-C01 Exploratory Data Analysis Practice Question
A data scientist is exploring a large dataset (10 TB) stored in Amazon S3. The dataset is in CSV format and has many columns. The scientist wants to quickly compute summary statistics (mean, min, max, count) for each column without moving the data. 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 without moving it. It is cost-effective because you pay only for the data scanned per query. For summary statistics like mean, min, max, count, you can use aggregate functions like AVG, MIN, MAX, COUNT in SQL. Option A (SageMaker Data Wrangler) requires importing data into SageMaker, incurring transfer costs and time. Option B (Amazon EMR) requires provisioning a cluster, which adds overhead and cost for a simple summary task. Option C (S3 Select) works on a single object and cannot compute statistics across entire dataset easily; it is more suited for filtering. Option E (AWS Glue DataBrew) is a data preparation tool that may be more expensive and overkill 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.
- ✗
Import the data into Amazon SageMaker Data Wrangler
Why it's wrong here
Importing 10 TB into SageMaker is time-consuming and costly.
- ✗
Launch an Amazon EMR cluster with Spark
Why it's wrong here
EMR requires cluster management and is overkill for simple summary statistics.
- ✗
Use S3 Select to compute statistics
Why it's wrong here
S3 Select retrieves subsets of data, not aggregate statistics across multiple objects.
- ✓
Use Amazon Athena with SQL queries
Why this is correct
Athena queries data in place with no data movement and pay-per-query pricing.
- ✗
Use AWS Glue DataBrew to profile the data
Why it's wrong here
DataBrew is effective but may be more expensive than Athena for large datasets.
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.