MLA-C01 Deployment and Orchestration of ML Workflows Practice Question
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?
⚠ Common exam trap
AWS 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.
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.
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
SageMaker Neo compiles the model for the specific target instance's instruction set, producing an optimised artefact that runs faster on ml.p3.2xlarge. This satisfies the reduce-cost-without-losing-throughput constraint by improving hardware utilisation rather than shrinking the instance.
- ✗
Triton Inference Server on SageMaker
Why it's wrong here
Triton serves models but does not compile them for target hardware; it handles multi-framework inference and dynamic batching. The stem requires hardware-specific compilation to cut cost while preserving throughput, which SageMaker Neo performs by producing an optimised model for the ml.p3.2xlarge's GPU.
- ✗
Amazon Elastic Inference
Why it's wrong here
Elastic Inference attaches fractional GPU acceleration to CPU instances; it cannot compile a model for the ml.p3.2xlarge's GPU and is being deprecated. It suits reducing cost on CPU-based endpoints, not hardware-specific compilation, which SageMaker Neo performs for the target instance.
- ✗
SageMaker Inference Recommender
Why it's wrong here
Inference Recommender benchmarks instance types and configurations to suggest cost-effective deployments; it does not compile or optimise model artefacts for target hardware. It suits right-sizing instance selection, whereas the stated goal of compiling for the ml.p3.2xlarge requires SageMaker Neo.
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Same concept, more angles
1 more way 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.
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.