MLS-C01 Modeling Practice Question
A company is using SageMaker's built-in image classification algorithm to classify product images into 100 categories. The training takes 3 hours on a single p3.2xlarge instance. They need to reduce training time to under 1 hour. They have access to a cluster of 4 p3.2xlarge instances. Which approach should they take?
⚠ Common exam trap
It's easy for candidates to confuse cost-saving techniques (spot training) or accuracy-tuning methods (hyperparameter tuning) with performance scaling, failing to recognize that distributed data parallelism is the only option that directly reduces training time by leveraging multiple GPUs in parallel.
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 data parallelism using Horovod
SageMaker's built-in image classification algorithm supports distributed training with data parallelism using Horovod, which splits the mini-batch across multiple GPUs and synchronizes gradients via allreduce. With 4 p3.2xlarge instances (each with 1 GPU), this reduces per-iteration time proportionally, enabling the 3-hour job to complete in under 1 hour when scaling batch size and learning rate appropriately.
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's hyperparameter tuning to find faster convergence
Why it's wrong here
Tuning adds overhead and does not guarantee 3x speedup.
- ✗
Use a smaller batch size on each instance
Why it's wrong here
Smaller batch size increases training time.
- ✗
Use SageMaker's managed spot training with checkpointing
Why it's wrong here
Spot training reduces cost, not necessarily time.
- ✓
Use SageMaker's distributed training with data parallelism using Horovod
Why this is correct
Data parallelism across 4 instances can reduce training time nearly linearly.
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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.