MLS-C01 Practice Question: Machine Learning Implementation and Operations
A data scientist is using Amazon SageMaker to train a large neural network on a GPU instance. The training is taking longer than expected. The scientist wants to reduce training time without changing the model architecture. Which TWO approaches should the scientist consider?
⚠ Common exam trap
A common mix-up: candidates confuse cost-saving techniques (Spot Training) or monitoring tools (Debugger) with performance optimization, or mistakenly think hyperparameter tuning reduces training time when it actually increases total compute effort.
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 SageMaker's distributed training with multiple GPU instances.
SageMaker's distributed training can split the large neural network across multiple GPU instances, reducing wall-clock training time through data parallelism or model parallelism. Option D is correct because switching to a larger GPU instance type with more CUDA cores increases the computational throughput per step, directly accelerating training without altering the model architecture.
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 Automatic Model Tuning to find optimal hyperparameters.
Why it's wrong here
Tuning finds better hyperparameters but may increase total time.
- ✗
Use SageMaker Managed Spot Training to reduce cost.
Why it's wrong here
Spot training saves money but does not inherently reduce training time.
- ✓
Use SageMaker's distributed training with multiple GPU instances.
Why this is correct
Distributed training parallelizes computation, reducing wall-clock time.
- ✓
Switch to a larger GPU instance type with more CUDA cores.
Why this is correct
A larger instance provides more compute power, reducing training time.
- ✗
Enable SageMaker Debugger to capture training metrics.
Why it's wrong here
Debugger monitors but does not accelerate training.
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Same concept, more angles
1 more way this is tested on MLS-C01
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A data scientist is using Amazon SageMaker to train a model. Training is taking longer than expected. The scientist notices that the training job is using a single instance type with limited GPU memory. Which action will MOST likely reduce training time?
easy- ✓ A.Configure the training job to use distributed data parallelism across multiple instances.
- B.Use SageMaker Managed Spot Training to lower cost.
- C.Use batch normalization layers.
- D.Enable SageMaker Debugger for real-time monitoring.
Why A: The training job is bottlenecked by limited GPU memory on a single instance. Distributed data parallelism splits the dataset across multiple instances, each processing a subset of the data in parallel, which directly reduces wall-clock training time by leveraging aggregate GPU memory and compute. This is the most effective action to address the stated problem of slow training due to limited GPU memory.
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.