The answer is to use model parallelism to split the ensemble across multiple GPUs on a single instance. This approach directly addresses the core challenge of reducing inference latency for large ensemble model SageMaker deployments by distributing the 5 GB total model weight across multiple GPUs, which reduces per-device memory pressure and allows parallel computation of the ensemble’s sub-models. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this tests your understanding that model parallelism is the correct strategy when a single GPU cannot handle the combined model size within strict latency bounds, while options like increasing batch size or simply upgrading instance type often fail because they do not solve the underlying memory bottleneck. A common trap is assuming SageMaker Neo can optimize ensemble models, but Neo focuses on compiler-level optimizations for single models, not distributed execution. Remember the mnemonic: “Ensemble big? Parallel the gig.”
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 team is deploying a real-time inference endpoint using Amazon SageMaker. The model is a large ensemble of 10 deep learning models, each 500 MB. The inference latency requirement is under 200 ms. Currently, the endpoint using a single ml.p3.2xlarge instance takes 1.5 seconds per request. Which approach is MOST likely to meet the latency requirement?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "most likely"
Why it matters: Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use model parallelism to split the ensemble across multiple GPUs on a single instance.
Option C is correct because model parallelism distributes the model across multiple GPUs, reducing per-device memory and computation time. Option A is wrong because increasing batch size increases latency. Option B is wrong because changing instance type alone may not reduce latency enough. Option D is wrong because SageMaker Neo does not support model parallelism.
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.
✗
Increase the batch size to process more requests per invocation.
Why it's wrong here
Larger batch size increases latency per request.
✗
Switch to a compute-optimized instance like c5.18xlarge.
Why it's wrong here
CPU instances may not accelerate deep learning inference sufficiently.
✗
Use SageMaker Neo to compile the model for the target instance.
Why it's wrong here
Neo optimizes for single device, not model parallelism.
✓
Use model parallelism to split the ensemble across multiple GPUs on a single instance.
Why this is correct
Model parallelism reduces per-device load and can achieve latency target.
Clue confirmation
The clue word "most likely" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Detailed technical explanation
How to think about this question
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
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.
Use explanations to understand the rule behind the answer.
TExam Day Tips
→Underline the problem statement mentally.
→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
An e-commerce site experiences heavy traffic on Black Friday and near-zero traffic during off-peak weeks. Rather than provisioning permanent large VMs, the team uses auto-scaling groups that add capacity automatically under load and reduce it overnight. Questions like this test whether you understand elasticity, availability zones, and cloud compute scaling patterns.
What to study next
Got this wrong? Here's your next step.
Identify which MLS-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
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 model parallelism to split the ensemble across multiple GPUs on a single instance. — Option C is correct because model parallelism distributes the model across multiple GPUs, reducing per-device memory and computation time. Option A is wrong because increasing batch size increases latency. Option B is wrong because changing instance type alone may not reduce latency enough. Option D is wrong because SageMaker Neo does not support model parallelism.
What should I do if I get this MLS-C01 question wrong?
Identify which MLS-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
Are there clue words in this question I should notice?
Yes — watch for: "most likely". Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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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.
Question Discussion
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