MLA-C01 Deployment and Orchestration of ML Workflows Practice Question
A machine learning team needs to deploy a PyTorch model that has been compiled with SageMaker Neo to improve inference performance on edge devices. Which TWO statements about SageMaker Neo are correct? (Select TWO.)
⚠ Common exam trap
MLA-C01 often tests the misconception that SageMaker Neo is tied to SageMaker training or built-in algorithms, when in fact it is a standalone compilation service that works with models from any source.
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
✓
Neo reduces model inference latency through optimization techniques
Option A is correct because SageMaker Neo applies compiler-level optimizations such as operator fusion, constant folding, and quantization-aware graph rewrites that reduce inference latency and model size on the target hardware. Option C is correct because Neo is a compilation service that takes a trained model plus a target hardware specification (for example, an Intel x86 CPU with a specific instruction set, an ARM Cortex-A SoC, or an NVIDIA Jetson GPU) and produces a hardware-optimized executable, so the compiled artifact is tied to that target. Option B is incorrect because Neo can compile models trained anywhere, including on-premises or in other clouds, as long as the model is provided in a supported framework format such as PyTorch, TensorFlow, MXNet, or ONNX. Option D is incorrect because Neo supports custom models and frameworks, not just SageMaker built-in algorithms. Option E is incorrect because endpoint auto scaling is handled by Application Auto Scaling and SageMaker endpoint scaling policies, not by Neo, which only handles model compilation.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Neo reduces model inference latency through optimization techniques
Why this is correct
SageMaker Neo applies operator fusion, constant folding and quantisation-aware compilation to shrink the model graph, directly satisfying the stem's requirement to improve inference performance on constrained edge hardware. This optimisation lowers per-inference latency rather than merely packaging the PyTorch artefacts for deployment.
- ✗
Neo requires the model to be trained on SageMaker
Why it's wrong here
Neo compiles models from frameworks including PyTorch, TensorFlow and XGBoost regardless of training location; it consumes model artefacts from Amazon S3. Tempting because SageMaker-native workflows are common, and Neo would be correct where the model was trained in SageMaker, but that is not a prerequisite.
- ✓
Neo compiles models for a specific hardware target, such as Intel or ARM
Why this is correct
Neo compiles the trained model into an optimised binary for a declared target architecture, such as Intel x86 or ARM, rather than running the framework graph directly. This satisfies the edge deployment requirement because the compiled artefact matches the device's processor.
- ✗
Neo can only compile models trained with SageMaker built-in algorithms
Why it's wrong here
Neo accepts models from any framework it supports, including PyTorch, TensorFlow, MXNet and XGBoost, trained anywhere. Tempting because built-in algorithms integrate smoothly with Neo, and it would be correct only if the question asked about models produced by SageMaker built-in algorithms, not the general constraint.
- ✗
Neo automatically scales SageMaker endpoints based on demand
Why it's wrong here
Neo is a compilation tool that converts trained models into optimised executables for target hardware; endpoint autoscaling is handled by Application Auto Scaling on SageMaker endpoints. Tempting because both concern deployment performance, and autoscaling would be correct for a scenario needing dynamic capacity adjustment under variable inference load.
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Last reviewed September 2026 · checked against the official Amazon Web Services exam blueprint
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