- A
Use a GPU instance such as ml.g4dn.xlarge
GPUs accelerate transformer inference significantly.
- B
Reduce the maximum sequence length to 128
Why wrong: May truncate important text and reduce accuracy.
- C
Increase the batch size for inference requests
Why wrong: Larger batch improves throughput but not per-request latency.
- D
Use SageMaker Neo to compile the model for the target instance
Why wrong: Neo may help but likely insufficient to reach 100 ms on CPU.
Question 41 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 machine learning engineer is deploying a sentiment analysis model using Amazon SageMaker. The model is a BERT-based transformer that takes up to 512 tokens. The engineer notices that inference latency is high (over 500 ms per request) on a single ml.c5.xlarge instance. The application requires latency under 100 ms. The model has already been optimized using half-precision (FP16). Which action should the engineer take to reduce latency?
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 a GPU instance such as ml.g4dn.xlarge
Option A (Use a GPU instance such as ml.g4dn.xlarge) provides the parallel processing power needed to accelerate transformer inference, significantly reducing latency for BERT-based models even after FP16 optimization. Option B (Reduce max sequence length to 128) would lower computational cost and latency but at the risk of truncating input and degrading accuracy, so it is not the primary recommended action. Option C (Increase batch size) improves throughput for multiple requests but does not reduce the latency of a single request. Option D (SageMaker Neo) optimizes the model for the target instance, but with an already optimized model (FP16) and strict latency requirement under 100 ms, switching to a GPU instance is more effective.
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 a GPU instance such as ml.g4dn.xlarge
Why this is correct
GPUs accelerate transformer inference significantly.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Reduce the maximum sequence length to 128
Why it's wrong here
May truncate important text and reduce accuracy.
- ✗
Increase the batch size for inference requests
Why it's wrong here
Larger batch improves throughput but not per-request latency.
- ✗
Use SageMaker Neo to compile the model for the target instance
Why it's wrong here
Neo may help but likely insufficient to reach 100 ms on CPU.
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
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.
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.
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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 a GPU instance such as ml.g4dn.xlarge — Option A (Use a GPU instance such as ml.g4dn.xlarge) provides the parallel processing power needed to accelerate transformer inference, significantly reducing latency for BERT-based models even after FP16 optimization. Option B (Reduce max sequence length to 128) would lower computational cost and latency but at the risk of truncating input and degrading accuracy, so it is not the primary recommended action. Option C (Increase batch size) improves throughput for multiple requests but does not reduce the latency of a single request. Option D (SageMaker Neo) optimizes the model for the target instance, but with an already optimized model (FP16) and strict latency requirement under 100 ms, switching to a GPU instance is more effective.
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.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
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Last reviewed: Jun 20, 2026
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