easyMultiple Choice
MLA-C01 Practice Question: A data scientist needs to store training data in…
A data scientist needs to store training data in Amazon S3 and wants to optimize read performance for iterative training jobs. Which S3 feature should they use?
⚠ Common exam trap
AWS often tests the distinction between features that optimize data transfer (like S3 Transfer Acceleration) versus those that optimize data access patterns (like Byte-Range Fetches), leading candidates to confuse upload acceleration with read performance optimization.
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
✓
S3 Byte-Range Fetches
Byte-range fetches allow the data scientist to parallelize reads by requesting specific byte ranges of an object, which significantly improves read performance for iterative training jobs that need to access large datasets stored in S3. This feature enables multiple concurrent requests to different parts of the same object, reducing latency and increasing throughput compared to single-range reads.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
S3 Transfer Acceleration
Why it's wrong here
Transfer Acceleration speeds up uploads and downloads over long geographic distances by routing through edge locations; it does not reduce latency for repeated reads from the same Region. It is the right choice when training data must cross continents to reach the bucket.
- ✗
S3 Glacier
Why it's wrong here
Glacier storage classes archive objects with retrieval times of minutes to hours, so iterative training jobs cannot read them promptly. Glacier is correct when data is retained for compliance and accessed rarely, not for repeated training reads.
- ✓
S3 Byte-Range Fetches
Why this is correct
S3 Byte-Range Fetches let training jobs retrieve only the specific byte ranges needed from large objects, enabling parallelised, partial reads rather than downloading entire files. This satisfies the requirement to optimise read performance for iterative training jobs.
- ✗
S3 Select
Why it's wrong here
S3 Select filters rows and columns from a single object during retrieval, so it reduces bytes transferred per query rather than improving throughput across repeated reads of the same training dataset. It suits one-off SQL-style extraction from CSV, JSON or Parquet files, not iterative training workloads.
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
Related to this question
About these practice questions
Courseiva writes every MLA-C01 question from scratch — 665 in total, each with an explanation and a wrong-answer breakdown. None are copied from real exams or dumps. Learn why practice questions differ from exam dumps →
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This MLA-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 MLA-C01 exam.