MLS-C01 Data Engineering Practice Question
A data scientist needs to train a machine learning model using a large dataset (500 GB) stored in an S3 bucket. The training will be performed on a SageMaker notebook instance. The data scientist wants to minimize data transfer costs and reduce training time. Which data ingestion approach should the data engineer recommend?
⚠ Common exam trap
Many exam-takers assume they must copy data locally for faster access (Option B), not realizing that SageMaker's native S3 integration with Pipe mode is designed specifically to avoid that overhead and is the most cost-effective and performant approach for large datasets.
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 the SageMaker SDK to directly read the data from S3 during training without copying it to the notebook.
The SageMaker SDK allows training jobs to read data directly from S3 using the Pipe or File mode, which avoids copying the 500 GB dataset to the notebook instance's EBS volume. This minimizes data transfer costs (no egress from S3 to the notebook) and reduces training time by streaming data directly to the training container without intermediate storage.
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 the SageMaker SDK to directly read the data from S3 during training without copying it to the notebook.
Why this is correct
SageMaker can read data directly from S3, minimizing transfer and storage costs.
- ✗
Copy the dataset to the notebook instance's attached EBS volume before training.
Why it's wrong here
Copying data incurs transfer costs and uses local storage, increasing time and cost.
- ✗
Load the dataset into an Amazon RDS database and query it from the notebook.
Why it's wrong here
RDS is for transactional data, not for large ML datasets; incurs additional costs.
- ✗
Mount the S3 bucket to the notebook instance using Amazon Elastic File System (EFS).
Why it's wrong here
EFS is a network file system, not optimized for S3 access; it adds cost and complexity.
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 MLS-C01 question from scratch — 1,672 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 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.