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MLA-C01 Practice Question: An ML team is developing a regression model using…
An ML team is developing a regression model using Amazon SageMaker. They have a 100 GB CSV dataset stored in Amazon S3. The data is contained in a single large file. They launch a SageMaker training job with an ml.p3.8xlarge instance using a custom Docker container. The training script loads the data using pandas' read_csv from S3 directly. The team observes that the training job takes over 24 hours, and CloudWatch metrics show: GPU utilization is consistently above 90%, but CPU utilization is below 30%. Network I/O is moderate, and disk I/O is low. The team has already tried switching to a larger instance type (ml.p3.16xlarge) with no significant improvement. They need to reduce training time. Which action is MOST likely to achieve this?
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
✓
Split the CSV file into multiple smaller files (e.g., 100 MB each) and update the training script to read from a list of files in S3.
The bottleneck is data loading. The single large CSV file prevents parallelism; SageMaker's Pipe mode streams data directly to the algorithm, but custom containers must support it. However, a simpler and effective approach is to split the data into multiple smaller files, enabling SageMaker's distributed data loading across instances and improving I/O parallelism. Increasing instance count with single file doesn't help because each instance still reads the same file. Changing instance type already tried. Spot instances don't improve speed. EBS volume doesn't matter.
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 SageMaker Pipe Mode to stream data directly from S3 to the algorithm, bypassing the local file system.
Why it's wrong here
Pipe mode is designed for streaming, but it requires the algorithm to support it. The custom container may not, and even if it does, the single file still limits parallelism.
- ✓
Split the CSV file into multiple smaller files (e.g., 100 MB each) and update the training script to read from a list of files in S3.
Why this is correct
This allows SageMaker to parallelize data loading across multiple instances or even multiple processes within one instance, improving I/O throughput.
- ✗
Use Amazon SageMaker Managed Spot Training to reduce cost, then use the savings to rent a larger instance.
Why it's wrong here
Spot instances reduce cost but not training time. The instance size has already been increased without improvement.
- ✗
Increase the number of training instances by using a distributed training configuration with Horovod.
Why it's wrong here
With a single CSV file, adding more instances won't help because each instance still reads the same file, leading to contention and no speedup.
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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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This MLA-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 MLA-C01 exam.