This MLA-C01 practice question tests your understanding of deployment and orchestration of ml workflows. 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 wants to use SageMaker to deploy a model that requires GPU acceleration for inference but also needs to keep costs low when traffic is low. Which SageMaker feature should they use?
SageMaker Debugger
Why wrong: Debugger is for debugging training jobs.
SageMaker Managed Spot Training
Why wrong: Spot Training is for training jobs, not inference.
SageMaker Elastic Inference
Elastic Inference attaches GPU acceleration to any SageMaker instance, reducing cost.
SageMaker Model Monitor
Why wrong: Model Monitor is for monitoring inference quality.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
SageMaker Elastic Inference
SageMaker Elastic Inference (EI) allows you to attach a fraction of a GPU to a SageMaker endpoint for inference, providing GPU acceleration at a lower cost than using a full GPU instance. This is ideal for scenarios with variable traffic because you can scale the EI accelerator independently of the instance, and pay only for the accelerator when it's used, keeping costs low during low-traffic periods.
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.
SageMaker Debugger
Why it's wrong here
Debugger is for debugging training jobs.
SageMaker Managed Spot Training
Why it's wrong here
Spot Training is for training jobs, not inference.
SageMaker Elastic Inference
Why this is correct
Elastic Inference attaches GPU acceleration to any SageMaker instance, reducing cost.
Related concept
Read the scenario before looking for a memorised answer.
SageMaker Model Monitor
Why it's wrong here
Model Monitor is for monitoring inference quality.
Common exam traps
The trap here is that candidates often confuse SageMaker Managed Spot Training (cost savings for training) with inference cost optimization, or assume that GPU acceleration for inference requires a full GPU instance like ml.p3.2xlarge, overlooking Elastic Inference as a fractional GPU solution.
Detailed technical explanation
SageMaker Elastic Inference works by attaching a dedicated EI accelerator (e.g., ml.eia2.medium) to a SageMaker endpoint instance; the accelerator handles the matrix multiplication and convolution operations for deep learning models, while the CPU handles the rest of the inference pipeline. This allows you to use a cheaper CPU instance (e.g., ml.m5.large) for the endpoint and only pay for the EI accelerator when inference requests are processed, making it cost-effective for workloads with sporadic traffic. In a real-world scenario, a company serving a recommendation model that sees high traffic during business hours but low traffic at night could use EI to maintain low latency without provisioning a full GPU instance 24/7.
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
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FAQ
Deployment and Orchestration of ML Workflows — This question tests Deployment and Orchestration of ML Workflows — Read the scenario before looking for a memorised answer..
The correct answer is: SageMaker Elastic Inference — SageMaker Elastic Inference (EI) allows you to attach a fraction of a GPU to a SageMaker endpoint for inference, providing GPU acceleration at a lower cost than using a full GPU instance. This is ideal for scenarios with variable traffic because you can scale the EI accelerator independently of the instance, and pay only for the accelerator when it's used, keeping costs low during low-traffic periods.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 24, 2026
This MLA-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 MLA-C01 exam.
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