MLS-C01 Modeling Practice Question
A machine learning engineer is training a deep learning model using the SageMaker built-in XGBoost algorithm. The training job is taking longer than expected. The engineer notices that the training data is stored in S3 in CSV format and is 500 GB in size. The instance type is ml.c4.8xlarge with 10 instances. Which change would most likely reduce training time?
⚠ Common exam trap
The MLS-C01 exam often tests the distinction between data format optimization (Parquet) and data ingestion mode (Pipe vs. File), where candidates mistakenly choose a format change without recognizing that the primary bottleneck is the data transfer mechanism, not the storage format.
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 Pipe input mode instead of File input mode.
Pipe input mode streams data directly from S3 to the training instances without first downloading it to the local EBS volume, eliminating the I/O bottleneck of reading a 500 GB CSV file. This reduces the time spent on data loading and allows the XGBoost algorithm to begin training sooner, which is especially beneficial for large datasets.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Convert the data to Parquet format.
Why it's wrong here
Parquet may help but XGBoost built-in algorithm does not support Parquet directly.
- ✗
Increase the number of instances to 20.
Why it's wrong here
Increasing instances may not help if the bottleneck is data loading.
- ✓
Use Pipe input mode instead of File input mode.
Why this is correct
Pipe mode streams data directly, reducing I/O bottleneck.
- ✗
Increase the size of the EBS volume attached to each instance.
Why it's wrong here
EBS volume size does not affect data loading speed.
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.