- A
SageMaker Neo
Neo compiles models for target hardware, optimizing for performance and cost.
- B
Triton Inference Server on SageMaker
Why wrong: Triton is a serving framework that can support multiple frameworks and dynamic batching, but it does not compile models.
- C
Amazon Elastic Inference
Why wrong: Elastic Inference attaches GPU acceleration to CPU instances but does not compile models; it's a hardware add-on.
- D
SageMaker Inference Recommender
Why wrong: Inference Recommender helps choose the right instance type and configuration but does not compile models.
Question 479 of 1,000
SageMaker Neo — Compiling Models for Optimized Inference | AWS Certified Machine Learning Engineer Associate Explained
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 deploys a large NLP model on a SageMaker real-time endpoint using an ml.p3.2xlarge instance. To reduce inference cost without sacrificing throughput, they want to compile the model for their target hardware. Which service should they use?
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 service because it compiles trained machine learning models into an optimized binary for a specific target hardware (e.g., ml.p3.2xlarge with NVIDIA GPUs). This reduces inference latency and cost by applying hardware-specific optimizations such as kernel fusion and memory layout tuning, while preserving the original model's throughput. The compilation process uses Apache TVM under the hood to generate efficient code for the target instance type.
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 compiles models for target hardware, optimizing for performance and cost.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Triton Inference Server on SageMaker
Why it's wrong here
Triton is a serving framework that can support multiple frameworks and dynamic batching, but it does not compile models.
- ✗
Amazon Elastic Inference
Why it's wrong here
Elastic Inference attaches GPU acceleration to CPU instances but does not compile models; it's a hardware add-on.
- ✗
SageMaker Inference Recommender
Why it's wrong here
Inference Recommender helps choose the right instance type and configuration but does not compile models.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the distinction between model compilation (Neo) and runtime serving optimizations (Triton) or hardware acceleration (Elastic Inference), leading candidates to confuse a compile-time optimization service with a runtime serving framework or a hardware add-on.
Detailed technical explanation
How to think about this question
SageMaker Neo uses Apache TVM to perform graph-level and operator-level optimizations, such as fusing consecutive operations (e.g., convolution + ReLU) and quantizing weights to reduce precision (e.g., FP16 or INT8), which can significantly reduce memory bandwidth and compute time on GPU instances like ml.p3.2xlarge. A subtle behavior is that Neo compilation requires the model to be in a supported framework format (e.g., TensorFlow SavedModel, PyTorch TorchScript) and may fail if the model contains unsupported operators, requiring fallback to uncompiled execution. In a real-world scenario, a company deploying a large NLP model like BERT could see up to 2x throughput improvement after Neo compilation, directly reducing the number of instances needed.
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 service because it compiles trained machine learning models into an optimized binary for a specific target hardware (e.g., ml.p3.2xlarge with NVIDIA GPUs). This reduces inference latency and cost by applying hardware-specific optimizations such as kernel fusion and memory layout tuning, while preserving the original model's throughput. The compilation process uses Apache TVM under the hood to generate efficient code for the target instance type.
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 →
Same concept, more angles
1 more ways this is tested on MLA-C01
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. 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?
medium- ✓ A.SageMaker Neo
- B.AWS Step Functions
- C.Amazon Elastic Inference
- D.SageMaker Triton Inference Server
Why A: 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.
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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