- A
SageMaker Model Monitor
Why wrong: Model Monitor detects data drift, not inference optimization.
- B
SageMaker Neo
Neo compiles models for target hardware, optimizing for edge deployment.
- C
SageMaker Debugger
Why wrong: Debugger monitors training, not deployment optimization.
- D
SageMaker Elastic Inference
Why wrong: Elastic Inference is a GPU acceleration add-on for inference, not compilation for edge.
Question 101 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 machine learning engineer needs to optimize a trained TensorFlow model for deployment on edge devices with limited compute. Which SageMaker feature should they use to compile the model for target 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
✓
SageMaker Neo
SageMaker Neo is the correct choice because it is specifically designed to compile trained machine learning models into an optimized format for target hardware architectures, such as ARM, Intel, or NVIDIA, enabling efficient inference on edge devices with limited compute resources. It uses a compiler to apply hardware-specific optimizations like operator fusion and memory layout tuning, reducing latency and memory footprint without requiring manual code changes.
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 Model Monitor
Why it's wrong here
Model Monitor detects data drift, not inference optimization.
- ✓
SageMaker Neo
Why this is correct
Neo compiles models for target hardware, optimizing for edge deployment.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
SageMaker Debugger
Why it's wrong here
Debugger monitors training, not deployment optimization.
- ✗
SageMaker Elastic Inference
Why it's wrong here
Elastic Inference is a GPU acceleration add-on for inference, not compilation for edge.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse SageMaker Neo with SageMaker Elastic Inference, mistakenly thinking Elastic Inference compiles models for edge devices, when in fact Elastic Inference only accelerates cloud inference by attaching a fractional GPU and does not perform compilation or target edge hardware.
Detailed technical explanation
How to think about this question
SageMaker Neo leverages Apache TVM (Tensor Virtual Machine) under the hood to perform graph-level and operator-level optimizations, including automatic kernel tuning for the specified target hardware (e.g., Intel, ARM, NVIDIA, or Xilinx). In a real-world scenario, deploying a TensorFlow object detection model to a Raspberry Pi would require Neo to convert the model to a format like TensorFlow Lite or ONNX with hardware-specific quantization, reducing inference time from seconds to milliseconds while maintaining accuracy.
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
An e-commerce site experiences heavy traffic on Black Friday and near-zero traffic during off-peak weeks. Rather than provisioning permanent large VMs, the team uses auto-scaling groups that add capacity automatically under load and reduce it overnight. Questions like this test whether you understand elasticity, availability zones, and cloud compute scaling patterns.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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Deployment and Orchestration of ML Workflows — study guide chapter
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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 machine learning models into an optimized format for target hardware architectures, such as ARM, Intel, or NVIDIA, enabling efficient inference on edge devices with limited compute resources. It uses a compiler to apply hardware-specific optimizations like operator fusion and memory layout tuning, reducing latency and memory footprint without requiring manual code changes.
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.
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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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