Quick Answer
The answer is to use SageMaker’s Elastic Inference to attach an EI accelerator to the existing ml.p3.2xlarge instance. This is correct because Elastic Inference provides dedicated GPU acceleration for deep neural network inference without the cost of a full GPU instance, directly addressing the need to reduce latency from 200ms back under the 100ms SLA while minimizing cost. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this scenario tests your understanding of cost-optimized inference architectures—a common trap is choosing a larger GPU instance like ml.p3.8xlarge, which reduces latency but dramatically increases cost, or adding more instances behind a load balancer, which adds complexity and expense. Elastic Inference is purpose-built for this exact trade-off: it decouples GPU acceleration from compute, so you pay only for the acceleration you need. Memory tip: think “EI = Extra Inference, not Extra Instance” to remember it’s a cost-effective add-on, not a full upgrade.
This MLS-C01 practice question tests your understanding of modeling. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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 runs a real-time recommendation system on SageMaker with a model that uses a deep neural network. The endpoint uses a single ml.p3.2xlarge instance. Recently, the number of users has grown, and the endpoint's latency has increased from 50ms to 200ms, exceeding the SLA of 100ms. The model inference code is optimized and cannot be improved further. The company wants to reduce latency while minimizing cost. The data scientist has the following options: A. Switch to a larger instance type with more GPU memory, such as ml.p3.8xlarge. B. Use SageMaker's Elastic Inference to attach an EI accelerator to the existing instance. C. Deploy the model on multiple smaller instances (e.g., ml.p3.2xlarge) behind a load balancer and distribute traffic. D. Convert the model to use TensorFlow Lite and deploy on a CPU-based instance. Which option is the MOST cost-effective and meets the latency requirement?
Convert to TensorFlow Lite on CPU
Why wrong: May lose accuracy and not meet latency on CPU.
Use SageMaker's Elastic Inference
Elastic Inference provides cost-effective GPU acceleration.
Switch to a larger instance type, e.g., ml.p3.8xlarge
Why wrong: Larger instance is more expensive and may not be cost-effective.
Deploy on multiple smaller instances behind a load balancer
Why wrong: Multiple instances increase cost and complexity.
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's Elastic Inference
Option B is the most cost-effective because Elastic Inference provides dedicated GPU acceleration at a fraction of the cost of a full GPU instance. Option A increases cost significantly. Option C may reduce latency but increases cost and complexity. Option D may not maintain accuracy and may not meet latency requirements on CPU.
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.
Convert to TensorFlow Lite on CPU
Why it's wrong here
May lose accuracy and not meet latency on CPU.
Use SageMaker's Elastic Inference
Why this is correct
Elastic Inference provides cost-effective GPU acceleration.
Related concept
Read the scenario before looking for a memorised answer.
Switch to a larger instance type, e.g., ml.p3.8xlarge
Why it's wrong here
Larger instance is more expensive and may not be cost-effective.
Deploy on multiple smaller instances behind a load balancer
Why it's wrong here
Multiple instances increase cost and complexity.
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's Elastic Inference — Option B is the most cost-effective because Elastic Inference provides dedicated GPU acceleration at a fraction of the cost of a full GPU instance. Option A increases cost significantly. Option C may reduce latency but increases cost and complexity. Option D may not maintain accuracy and may not meet latency requirements on CPU.
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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