Quick Answer
The answer is to compile the model using SageMaker Neo and attach an Elastic Inference accelerator to the endpoint. SageMaker Neo optimizes the model for the target hardware by applying compiler-level graph optimizations and kernel fusion, which directly reduces compute overhead and speeds up inference. Elastic Inference attaches a dedicated, low-cost GPU acceleration to individual endpoints, allowing you to avoid the full overhead of a large GPU instance while still benefiting from accelerated tensor operations. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your understanding that latency reduction is about efficient resource utilization, not simply adding more compute power—a common trap is assuming a GPU instance always lowers latency, when in fact it can introduce unnecessary overhead for small models. Another trap is selecting larger batch sizes, which increase latency, or enabling Model Monitor, which adds inference overhead. Memory tip: think “Neo compiles, Elastic accelerates” to recall the two targeted approaches for reducing inference latency on a SageMaker real-time endpoint.
This MLS-C01 practice question tests your understanding of machine learning implementation and operations. 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.
Which TWO approaches can reduce inference latency on a SageMaker real-time endpoint? (Choose 2.)
Attach an Elastic Inference accelerator
Provides GPU acceleration at lower cost.
Increase the batch size
Why wrong: Larger batch size increases processing time.
Enable SageMaker Model Monitor
Why wrong: Adds overhead, not reduces latency.
Use a GPU instance type
Why wrong: GPU may not reduce latency for small requests.
Compile the model using SageMaker Neo
Optimizes model for inference.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Attach an Elastic Inference accelerator
Using a GPU instance (Option A) and enabling SageMaker Model Monitor (Option E) are not directly for latency reduction. Actually, correct: Option B (Elastic Inference) and Option D (compiled model with SageMaker Neo) reduce latency. Option A is wrong because GPU does not always reduce latency; it can add overhead. Option C is wrong because larger batch sizes increase latency. Option E is wrong because Model Monitor adds overhead.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Attach an Elastic Inference accelerator
Why this is correct
Provides GPU acceleration at lower cost.
Related concept
Read the scenario before looking for a memorised answer.
Increase the batch size
Why it's wrong here
Larger batch size increases processing time.
Enable SageMaker Model Monitor
Why it's wrong here
Adds overhead, not reduces latency.
Use a GPU instance type
Why it's wrong here
GPU may not reduce latency for small requests.
Compile the model using SageMaker Neo
Why this is correct
Optimizes model for inference.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
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
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.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
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
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
Machine Learning Implementation and Operations — This question tests Machine Learning Implementation and Operations — Read the scenario before looking for a memorised answer..
The correct answer is: Attach an Elastic Inference accelerator — Using a GPU instance (Option A) and enabling SageMaker Model Monitor (Option E) are not directly for latency reduction. Actually, correct: Option B (Elastic Inference) and Option D (compiled model with SageMaker Neo) reduce latency. Option A is wrong because GPU does not always reduce latency; it can add overhead. Option C is wrong because larger batch sizes increase latency. Option E is wrong because Model Monitor adds overhead.
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.
Read the scenario before looking for a memorised answer.
About these practice questions
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Last reviewed: Jun 20, 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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