MLS-C01 Practice Question: Machine Learning Implementation and Operations
A company runs a machine learning pipeline on Amazon SageMaker. The pipeline consists of three steps: data preprocessing (using a custom container), training (using a built-in algorithm), and model evaluation (using a custom container). The pipeline is orchestrated using AWS Step Functions. Recently, the pipeline has been failing intermittently at the model evaluation step with a 'TimeoutError'. The evaluation step runs a Python script that loads the trained model and a test dataset from S3, computes metrics, and writes results back to S3. The step is configured with a timeout of 600 seconds. The test dataset size has grown over time. The data science team suspects that the timeout is due to the increased data size. They want a solution that minimizes changes to the existing infrastructure and avoids increasing the timeout arbitrarily. Which approach should the team take?
⚠ Common exam trap
Test-takers frequently default to scaling up infrastructure (larger instances or higher timeouts) instead of optimizing the code, which is a classic 'throw hardware at the problem' misconception that the MLS-C01 exam tests by rewarding efficient, cost-conscious solutions.
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
✓
Modify the evaluation script to process the test dataset in parallel batches, and use multiprocessing to distribute the workload within the same container.
It addresses the root cause—the evaluation script's inability to process the growing dataset within the 600-second timeout—by parallelizing the workload within the same container. This approach minimizes infrastructure changes (no instance type or timeout increase) and leverages Python's multiprocessing to reduce wall-clock time, directly tackling the 'TimeoutError' without arbitrary timeout extensions.
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 timeout to 1200 seconds and use a larger instance type for the evaluation step.
Why it's wrong here
Increases cost and does not address root cause.
- ✗
Increase the timeout to 1800 seconds to accommodate the larger dataset.
Why it's wrong here
Does not address root cause; may lead to future timeouts.
- ✓
Modify the evaluation script to process the test dataset in parallel batches, and use multiprocessing to distribute the workload within the same container.
Why this is correct
Reduces wall-clock time without increasing timeout or instance size.
- ✗
Switch the evaluation step to use the 'ml.m5.4xlarge' instance type for more memory and compute.
Why it's wrong here
Increases cost and may not reduce time enough if script is single-threaded.
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 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.