MLS-C01 Data Engineering Practice Question
A company uses Amazon SageMaker to train and deploy machine learning models. The training data is stored in Amazon S3 (Parquet format, 10 TB). The data scientists have been running training jobs using the File mode input, but the jobs are taking too long due to data download time. They want to reduce the training start-up time and overall training time. Which solution is MOST cost-effective and efficient?
⚠ Common exam trap
Watch out — candidates often confuse Pipe mode with File mode, assuming both require downloading data, or they over-engineer the solution by choosing expensive network accelerators or larger instances when the simplest streaming approach is both faster and cheaper.
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
✓
Configure the SageMaker training job to use Pipe mode, which streams data directly from S3 without downloading to the instance's local storage.
Pipe mode in SageMaker streams training data directly from Amazon S3 to the training algorithm without first downloading it to the instance's local storage. This eliminates the data download step, significantly reducing startup time and overall training time for large datasets like 10 TB. It is the most cost-effective because it avoids the need for larger instances or additional data transfer acceleration services.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Configure the SageMaker training job to use Pipe mode, which streams data directly from S3 without downloading to the instance's local storage.
Why this is correct
Pipe mode reduces start-up time by streaming data, and it is cost-effective as it avoids EBS volume costs associated with File mode.
- ✗
Use S3 Transfer Acceleration to speed up the data transfer from S3 to the training instance.
Why it's wrong here
S3 Transfer Acceleration optimizes uploads over long distances, but training jobs download data; it does not apply to SageMaker training jobs.
- ✗
Use larger EC2 instances with more vCPUs and memory to speed up the training process.
Why it's wrong here
Larger instances may speed up computation but do not reduce the data download time; they increase cost unnecessarily.
- ✗
Enable Elastic Fabric Adapter (EFA) on the training instances to improve network throughput.
Why it's wrong here
EFA is designed for inter-node communication, not for S3 data loading; it does not address the download bottleneck.
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
This MLS-C01 question is part of Courseiva's 1,672-question bank — original exam-style content with full explanations and wrong-answer analysis, never real exam questions or exam 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.