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MLS-C01 Exploratory Data Analysis Practice Question
A data engineer is performing exploratory data analysis on a large dataset stored in Amazon S3 (10 TB in CSV format). The dataset has 2000 columns and 50 million rows. The engineer needs to compute summary statistics (mean, median, standard deviation) for each numeric column and identify missing values. Which approach is MOST cost-effective and time-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
✓
Convert the data to Apache Parquet format, then use Amazon Athena to run SQL queries for statistics.
Using Amazon Athena with columnar formats like Parquet after converting from CSV reduces query costs and improves performance. Option A (Redshift Spectrum) requires setting up a Redshift cluster, which is overkill. Option B (SageMaker Data Wrangler) may struggle with 2000 columns. Option D (AWS Glue ETL) is more expensive and slower for simple 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.
- ✗
Use Amazon Redshift Spectrum to query the data directly from S3.
Why it's wrong here
Redshift Spectrum requires a Redshift cluster, adding cost and complexity.
- ✗
Load the data into Amazon SageMaker Data Wrangler and compute statistics interactively.
Why it's wrong here
Data Wrangler has limitations with very wide datasets and interactive use may be slow.
- ✓
Convert the data to Apache Parquet format, then use Amazon Athena to run SQL queries for statistics.
Why this is correct
Parquet reduces data scanned, and Athena is cost-effective for ad-hoc queries.
- ✗
Use AWS Glue ETL to compute statistics and write results to S3.
Why it's wrong here
Glue ETL is more expensive and complex than query-based approaches.
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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Last reviewed: Jun 20, 2026
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.
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