- A
Use SageMaker's hyperparameter tuning to find faster convergence
Why wrong: Tuning adds overhead and does not guarantee 3x speedup.
- B
Use a smaller batch size on each instance
Why wrong: Smaller batch size increases training time.
- C
Use SageMaker's managed spot training with checkpointing
Why wrong: Spot training reduces cost, not necessarily time.
- D
Use SageMaker's distributed training with data parallelism using Horovod
Data parallelism across 4 instances can reduce training time nearly linearly.
Question 546 of 1,755
MLS-C01 Modeling Practice Question
This MLS-C01 practice question tests your understanding of modeling. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
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?
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
Option D is correct because 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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates 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.
Detailed technical explanation
How to think about this question
Horovod's allreduce implementation uses NVIDIA NCCL for GPU-to-GPU communication, achieving near-linear scaling when the batch size per GPU is kept constant and the learning rate is adjusted via the 'linear scaling rule' (e.g., double the batch size, double the LR). In SageMaker's built-in image classification, distributed training is configured by setting the 'sagemaker_distributed' hyperparameter to 'True' and specifying the number of instances; the algorithm automatically handles gradient synchronization and model averaging across workers.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.
What to study next
Got this wrong? Here's your next step.
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FAQ
Questions learners often ask
What does this MLS-C01 question test?
Modeling — This question tests Modeling — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Use SageMaker's distributed training with data parallelism using Horovod — Option D is correct because 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.
What should I do if I get this MLS-C01 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jul 4, 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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