Question 1,223 of 1,672
MLS-C01 Modeling Practice Question
A data scientist is using Amazon SageMaker to train a model using the built-in XGBoost algorithm. The training job is taking a long time. The data scientist notices that the input data is in CSV format and the training job is using File mode. The data size is 50 GB. What is the BEST way to reduce training time?
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
✓
Switch the input mode to Pipe.
Pipe mode streams data directly from S3 to the training algorithm, reducing I/O overhead and improving throughput compared to File mode, which downloads the entire dataset first. Option A (larger instance) may not help if the bottleneck is I/O rather than compute. Option B (Parquet format) can improve performance but is not as impactful as Pipe mode for streaming. Option C (reduce features) could reduce training time but at the cost of model accuracy, making it not the best approach.
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 a larger instance type with more vCPUs.
Why it's wrong here
The bottleneck is likely I/O, not compute.
- ✗
Convert the data to Parquet format.
Why it's wrong here
XGBoost expects CSV or libsvm; Parquet conversion may not be straightforward.
- ✗
Reduce the number of features in the dataset.
Why it's wrong here
Reducing features may harm model performance and is not a recommended approach to speed up training.
- ✓
Switch the input mode to Pipe.
Why this is correct
Pipe mode reduces I/O wait time by streaming data.
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 |
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Last reviewed: Jun 20, 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.
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