MLS-C01 Data Engineering Practice Question
A data engineering team is designing a data lake on Amazon S3. The data is ingested from multiple sources in JSON, CSV, and Parquet formats. The team needs to make the data available for analysis using Amazon Athena and Amazon Redshift Spectrum. The team wants to minimize data transformation costs and storage overhead. Which data storage approach should the team use?
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 all data to Apache Parquet before storing in S3
Converting all data to Apache Parquet before storing in S3 minimizes storage overhead and improves query performance. Parquet is a columnar format that provides efficient compression and encoding schemes, reducing storage costs. It is natively supported by Amazon Athena and Redshift Spectrum, enabling fast analytics without on-the-fly conversion. Option B (storing in original format) increases storage costs and can degrade query performance, especially with JSON or CSV. Option C incurs transformation costs each time data is queried, negating any storage benefit. Option A adds unnecessary transformation steps and cluster costs. Therefore, upfront conversion to Parquet is the most cost-effective strategy for this use case.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Load the data into Amazon Redshift cluster and then unload to S3 in Parquet
Why it's wrong here
This adds unnecessary steps and cost.
- ✗
Store the data in its original format in S3 and use Athena to query directly
Why it's wrong here
Querying JSON and CSV is slower and more expensive than Parquet.
- ✗
Store the data in its original format and use AWS Glue to convert to Parquet when queried
Why it's wrong here
On-the-fly conversion incurs compute costs and latency.
- ✓
Convert all data to Apache Parquet before storing in S3
Why this is correct
Parquet is columnar, reducing storage and improving query performance.
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.