hardMultiple Choice
MLA-C01 Practice Question: A team uses SageMaker Neo to compile a model for…
A team uses SageMaker Neo to compile a model for deployment on a target device. After compilation, they deploy the compiled model to a SageMaker endpoint using the Neo-optimized container. The endpoint fails to start with error "RuntimeError: Unable to load model". What could be the issue?
⚠ Common exam trap
AWS often tests the misconception that Neo compilation is a generic optimization that works on any endpoint instance, when in fact the target architecture must exactly match the deployment instance's hardware.
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
✓
The target device architecture during compilation does not match the endpoint instance architecture.
SageMaker Neo compiles a model for a specific target architecture (e.g., ARM, x86, GPU). When deploying the compiled model to a SageMaker endpoint, the endpoint instance type must have a CPU or accelerator architecture that matches the target device specified during compilation. If they do not match, the Neo-optimized runtime cannot load the compiled binary, resulting in a 'RuntimeError: Unable to load model'.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
The compiled model was not uploaded to the correct S3 path.
Why it's wrong here
The Neo-optimised container loads the compiled artifact from the S3 location recorded in the compilation job's output, so a mismatched path surfaces as a load failure rather than a silent fallback. Uploading to S3 is genuinely required, but the path is set by the job, making this the wrong culprit here.
- ✗
The Neo compilation job failed silently.
Why it's wrong here
A silently failed compilation job leaves no valid compiled artifact, but Neo reports job status explicitly, so a failed job would not present as a running endpoint throwing a load error. Checking job status is a valid troubleshooting step, yet it does not explain this runtime failure.
- ✗
The endpoint instance type does not support Neo.
Why it's wrong here
Neo compilation targets specific instance families, but deploying to an unsupported instance type fails at endpoint configuration or provisioning, not with a model-load RuntimeError. Instance-type selection matters when choosing a compilation target, which is why it appears relevant, but it does not produce this error.
- ✓
The target device architecture during compilation does not match the endpoint instance architecture.
Why this is correct
SageMaker Neo compiles the model into device-specific machine code for the architecture specified at compilation. Deploying that artefact on an endpoint instance with a different CPU or GPU architecture means the compiled binary cannot load, producing the runtime error. Matching the target architecture to the endpoint resolves it.
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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.