The answer is to use a GPU instance such as ml.g4dn.xlarge. BERT’s transformer architecture relies heavily on parallel matrix operations, which GPUs handle far more efficiently than CPUs, directly reducing inference latency for BERT on SageMaker below the 100 ms threshold. Even with half-precision optimization, a CPU instance like ml.c5.xlarge lacks the parallel compute cores needed for sub-500 ms token processing. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your understanding that GPU instances accelerate transformer inference, while common traps include mistaking batch-size increases (which improve throughput, not single-request latency) or sequence-length reduction (which risks accuracy loss). SageMaker Neo compilation can help, but for strict latency requirements on BERT, a GPU is the immediate fix. Memory tip: “BERT needs a GPU to be quick—CPUs choke on the transformer trick.”
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 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 B (use a GPU instance) accelerates inference for transformers. Option A (increase batch size) can help throughput but not latency for single requests. Option C (reduce max sequence length) may hurt accuracy. Option D (use SageMaker Neo) is for compilation but may not achieve sub-100ms.
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.
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 cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
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 a GPU instance such as ml.g4dn.xlarge — Option B (use a GPU instance) accelerates inference for transformers. Option A (increase batch size) can help throughput but not latency for single requests. Option C (reduce max sequence length) may hurt accuracy. Option D (use SageMaker Neo) is for compilation but may not achieve sub-100ms.
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.
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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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