- A
Amazon Elastic Inference attaches a dedicated GPU accelerator to a CPU instance, reducing cost compared to a full GPU instance
Elastic Inference provides GPU acceleration at lower cost.
- B
SageMaker Neo and Amazon Elastic Inference cannot be used together
Why wrong: They can be used together; Neo compiles the model, and Elastic Inference provides the GPU.
- C
SageMaker Neo provides a GPU acceleration service similar to Elastic Inference
Why wrong: Neo compiles models but does not provide acceleration hardware.
- D
SageMaker Neo optimizes the model by compiling it for the target hardware, reducing inference latency
Neo compiles models to improve performance on specific hardware.
- E
Amazon Elastic Inference compiles the model to run on GPU hardware
Why wrong: Elastic Inference does not compile; it provides hardware acceleration.
MLA-C01 Deployment and Orchestration of ML Workflows Practice Question
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.
An ML engineer needs to deploy a model that requires GPU acceleration but wants to reduce inference cost by optimizing the model. They are considering SageMaker Neo compilation and Amazon Elastic Inference. Which TWO statements are correct about these services? (Choose two.)
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Amazon Elastic Inference attaches a dedicated GPU accelerator to a CPU instance, reducing cost compared to a full GPU instance
Option A is correct because Amazon Elastic Inference allows you to attach a fraction of a GPU accelerator to an Amazon EC2 CPU instance, providing GPU acceleration at a lower cost than using a full GPU instance. This reduces inference cost by only paying for the GPU compute you need, without the overhead of a dedicated GPU instance.
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.
- ✓
Amazon Elastic Inference attaches a dedicated GPU accelerator to a CPU instance, reducing cost compared to a full GPU instance
Why this is correct
Elastic Inference provides GPU acceleration at lower cost.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
SageMaker Neo and Amazon Elastic Inference cannot be used together
Why it's wrong here
They can be used together; Neo compiles the model, and Elastic Inference provides the GPU.
- ✗
SageMaker Neo provides a GPU acceleration service similar to Elastic Inference
Why it's wrong here
Neo compiles models but does not provide acceleration hardware.
- ✓
SageMaker Neo optimizes the model by compiling it for the target hardware, reducing inference latency
Why this is correct
Neo compiles models to improve performance on specific hardware.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Amazon Elastic Inference compiles the model to run on GPU hardware
Why it's wrong here
Elastic Inference does not compile; it provides hardware acceleration.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is confusing model compilation (SageMaker Neo) with hardware acceleration (Elastic Inference), leading candidates to think they are mutually exclusive or that Elastic Inference performs compilation.
Detailed technical explanation
How to think about this question
SageMaker Neo uses Apache TVM to compile trained models into an optimized binary for specific hardware targets (e.g., Intel, NVIDIA, ARM), reducing inference latency and memory footprint. Amazon Elastic Inference uses a separate GPU accelerator (up to 32 GB of GPU memory) that attaches via the AWS Nitro system, allowing CPU instances to offload tensor operations without needing a full GPU instance. In practice, you can use Neo to compile a TensorFlow model for an inf1 instance, then attach an Elastic Inference accelerator to a c5 instance for cost-effective inference.
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.
TExam Day Tips
- 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 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
Questions learners often ask
What does this MLA-C01 question test?
Deployment and Orchestration of ML Workflows — This question tests Deployment and Orchestration of ML Workflows — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Amazon Elastic Inference attaches a dedicated GPU accelerator to a CPU instance, reducing cost compared to a full GPU instance — Option A is correct because Amazon Elastic Inference allows you to attach a fraction of a GPU accelerator to an Amazon EC2 CPU instance, providing GPU acceleration at a lower cost than using a full GPU instance. This reduces inference cost by only paying for the GPU compute you need, without the overhead of a dedicated GPU instance.
What should I do if I get this MLA-C01 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
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Last reviewed: Jul 4, 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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