Quick Answer
The answer is to use SageMaker Neo to compile the model for the target instance. SageMaker Neo optimizes trained models by applying hardware-specific compiler optimizations, which reduce inference latency and memory footprint without requiring larger or additional instances—directly addressing the goal of reducing latency without increasing cost. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your understanding of Neo’s role in model optimization versus scaling strategies; a common trap is choosing larger instances or adding more endpoints, which increase cost, or confusing batch transform (offline) with real-time inference. Remember the key trade-off: Neo compiles for efficiency, not capacity—think “Neo compiles, not multiplies” to avoid costly scaling mistakes.
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 uses SageMaker to deploy a model for real-time inference. The model is a large ensemble that requires 8 GB of memory and has high latency. The team wants to reduce latency without increasing cost. Which strategy is most effective?
Use a larger instance type with more memory.
Why wrong: Larger instances increase cost.
Deploy the model on multiple instances behind a load balancer.
Why wrong: Multiple instances increase cost.
Use SageMaker Neo to compile the model for the target instance.
Neo optimizes model for faster inference without additional cost.
Switch from real-time inference to batch transform.
Why wrong: Batch transform is not suitable for real-time 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
Use SageMaker Neo to compile the model for the target instance.
Option D is correct because SageMaker Neo optimizes trained models for target hardware, reducing latency and memory footprint. Option A is wrong because using a larger instance increases cost. Option B is wrong because batch transform is for offline, not real-time. Option C is wrong because multiple instances increase cost.
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.
Use a larger instance type with more memory.
Why it's wrong here
Larger instances increase cost.
Deploy the model on multiple instances behind a load balancer.
Why it's wrong here
Multiple instances increase cost.
Use SageMaker Neo to compile the model for the target instance.
Why this is correct
Neo optimizes model for faster inference without additional cost.
Related concept
Read the scenario before looking for a memorised answer.
Switch from real-time inference to batch transform.
Why it's wrong here
Batch transform is not suitable for real-time inference.
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 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
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
Modeling — This question tests Modeling — Read the scenario before looking for a memorised answer..
The correct answer is: Use SageMaker Neo to compile the model for the target instance. — Option D is correct because SageMaker Neo optimizes trained models for target hardware, reducing latency and memory footprint. Option A is wrong because using a larger instance increases cost. Option B is wrong because batch transform is for offline, not real-time. Option C is wrong because multiple instances increase cost.
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.
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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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