Question 1,217 of 1,672
MLS-C01 Data Engineering Practice Question
A machine learning engineer needs to process a large dataset that does not fit on a single Amazon SageMaker notebook instance's EBS volume. The data is stored in S3. What is the MOST efficient way to access the data from the notebook?
⚠ Common exam trap
It's easy for candidates to confuse SageMaker's File input mode (designed for training jobs) with a general-purpose data access method for notebooks, or they assume that mounting S3 as a filesystem (s3fs) is efficient for large-scale data processing, when in reality it introduces performance penalties due to FUSE overhead and lack of native parallel I/O.
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
✓
Read the data directly from S3 using the boto3 library.
Reading data directly from S3 using the boto3 library is the most efficient approach for a dataset that exceeds the notebook instance's EBS volume capacity. Boto3 allows you to stream data in chunks or use S3 Select for server-side filtering, avoiding the need to download the entire dataset to local storage. This method leverages S3's high-throughput API and eliminates the bottleneck of writing to a local EBS volume, which is limited in size and I/O performance.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Increase the EBS volume size to 5 TB.
Why it's wrong here
Notebook instances have a maximum EBS size of 5 TB, but downloading 5 TB is inefficient.
- ✗
Mount the S3 bucket as a file system using s3fs.
Why it's wrong here
s3fs may be slow and not recommended for large datasets.
- ✓
Read the data directly from S3 using the boto3 library.
Why this is correct
Reading directly from S3 avoids storage limitations and is efficient for large datasets.
- ✗
Use SageMaker File input mode in the notebook.
Why it's wrong here
File input mode is for training jobs, not notebooks.
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 |
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
Last reviewed: Jun 11, 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.
Question Discussion
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
Sign in to join the discussion.