- A
SageMaker Neo
Neo optimizes models for target hardware to improve inference speed and reduce cost.
- B
AWS Step Functions
Why wrong: Step Functions is for orchestration, not model optimization.
- C
Amazon Elastic Inference
Why wrong: Elastic Inference provides GPU acceleration but does not compile the model.
- D
SageMaker Triton Inference Server
Why wrong: Triton is a serving container that supports multiple frameworks but does not compile models.
Question 214 of 1,000
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.
A company is deploying a large NLP model on SageMaker for real-time inference. They want to reduce inference latency and cost by optimizing the model for the target hardware. The model is trained in PyTorch. Which SageMaker feature should they use to compile the model for best performance on the chosen instance?
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
✓
SageMaker Neo
SageMaker Neo is the correct choice because it is specifically designed to compile trained models (including PyTorch models) into an optimized binary for a target hardware instance, reducing inference latency and improving throughput. Neo applies hardware-specific optimizations such as operator fusion, memory layout tuning, and quantization, which directly address the need for best performance on the chosen SageMaker 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.
- ✓
SageMaker Neo
Why this is correct
Neo optimizes models for target hardware to improve inference speed and reduce cost.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
AWS Step Functions
Why it's wrong here
Step Functions is for orchestration, not model optimization.
- ✗
Amazon Elastic Inference
Why it's wrong here
Elastic Inference provides GPU acceleration but does not compile the model.
- ✗
SageMaker Triton Inference Server
Why it's wrong here
Triton is a serving container that supports multiple frameworks but does not compile models.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse model compilation (Neo) with inference serving (Triton) or hardware acceleration (Elastic Inference), leading them to pick a service that addresses a different part of the inference pipeline.
Detailed technical explanation
How to think about this question
Under the hood, SageMaker Neo uses Apache TVM (Tensor Virtual Machine) to perform graph-level and operator-level optimizations, including automatic kernel tuning for the specific CPU or GPU instruction set (e.g., AVX-512 for Intel, Tensor Cores for NVIDIA). This compilation process can reduce model size by up to 2x and improve latency by 2–3x compared to unoptimized PyTorch models, especially for large NLP models like BERT or GPT variants. A subtle behavior is that Neo requires the model to be in a supported framework format (e.g., PyTorch traced via TorchScript) and may fail if the model contains unsupported operators, requiring fallback to the original framework.
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
Got this wrong? Here's your next step.
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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: SageMaker Neo — SageMaker Neo is the correct choice because it is specifically designed to compile trained models (including PyTorch models) into an optimized binary for a target hardware instance, reducing inference latency and improving throughput. Neo applies hardware-specific optimizations such as operator fusion, memory layout tuning, and quantization, which directly address the need for best performance on the chosen SageMaker 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
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
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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