- A
Configure SageMaker Debugger to optimize the inference code.
Why wrong: SageMaker Debugger monitors training and can help debug training issues, but it does not optimize inference code or reduce latency.
- B
Use SageMaker Elastic Inference to attach an accelerator.
Why wrong: SageMaker Elastic Inference attaches GPU acceleration for deep learning models, but XGBoost is a tree-based model that typically runs on CPU, so Elastic Inference is not suitable and will not reduce latency.
- C
Use SageMaker Neo to compile the model for the target instance.
SageMaker Neo compiles the trained model to optimize it for the target hardware (ml.m5.large), which can improve inference speed and reduce latency without modifying the model.
- D
Use SageMaker Batch Transform instead of a real-time endpoint.
Why wrong: SageMaker Batch Transform processes large batches of data asynchronously, not real-time. It does not affect the latency of a real-time endpoint.
MLS-C01 SageMaker Neo Practice Question
This MLS-C01 practice question tests your understanding of machine learning implementation and operations. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. A key principle to apply: sageMaker Neo. 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 is deploying a custom XGBoost model for real-time inference on Amazon SageMaker. The model was trained using the SageMaker XGBoost built-in algorithm. The endpoint is deployed with an ml.m5.large instance and is receiving around 50 requests per second. The engineer notices that the endpoint's latency is around 200 ms, but the requirement is under 100 ms. The model's serialized format is a .tar.gz file. The engineer wants to reduce inference latency without modifying the model or retraining. What should the engineer do?
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
Use SageMaker Neo to compile the model for the target instance.
Option C is correct because SageMaker Neo compiles trained models to optimize them for target hardware, reducing inference latency without modifying the model. Option A is wrong because SageMaker Debugger is used for monitoring training jobs and debugging, not for optimizing inference code. Option B is wrong because SageMaker Elastic Inference attaches GPU acceleration, which is beneficial for deep learning models but not for XGBoost (a tree-based model). Option D is wrong because SageMaker Batch Transform is designed for batch predictions on large datasets, not for real-time inference with low latency requirements.
Key principle: SageMaker Neo
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Configure SageMaker Debugger to optimize the inference code.
Why it's wrong here
SageMaker Debugger monitors training and can help debug training issues, but it does not optimize inference code or reduce latency.
- ✗
Use SageMaker Elastic Inference to attach an accelerator.
Why it's wrong here
SageMaker Elastic Inference attaches GPU acceleration for deep learning models, but XGBoost is a tree-based model that typically runs on CPU, so Elastic Inference is not suitable and will not reduce latency.
- ✓
Use SageMaker Neo to compile the model for the target instance.
Why this is correct
SageMaker Neo compiles the trained model to optimize it for the target hardware (ml.m5.large), which can improve inference speed and reduce latency without modifying the model.
Related concept
SageMaker Neo
- ✗
Use SageMaker Batch Transform instead of a real-time endpoint.
Why it's wrong here
SageMaker Batch Transform processes large batches of data asynchronously, not real-time. It does not affect the latency of a real-time endpoint.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Candidates may incorrectly choose Elastic Inference (B) thinking it speeds up all models, but it is only useful for deep learning models, not tree-based XGBoost.
Detailed technical explanation
How to think about this question
Treat this as a scenario question. Identify the problem, the constraint, and the best action. Then compare each option against those facts.
KKey Concepts to Remember
- SageMaker Neo
- Model Compilation
- Inference Latency
- XGBoost on SageMaker
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
SageMaker Neo
Real-world example
How this comes up in practice
A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. SageMaker Neo Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
What to study next
Got this wrong? Here's your next step.
Review sageMaker Neo, then practise related MLS-C01 questions on the same topic to reinforce the concept.
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FAQ
Questions learners often ask
What does this MLS-C01 question test?
Machine Learning Implementation and Operations — This question tests Machine Learning Implementation and Operations — SageMaker Neo.
What is the correct answer to this question?
The correct answer is: Use SageMaker Neo to compile the model for the target instance. — Option C is correct because SageMaker Neo compiles trained models to optimize them for target hardware, reducing inference latency without modifying the model. Option A is wrong because SageMaker Debugger is used for monitoring training jobs and debugging, not for optimizing inference code. Option B is wrong because SageMaker Elastic Inference attaches GPU acceleration, which is beneficial for deep learning models but not for XGBoost (a tree-based model). Option D is wrong because SageMaker Batch Transform is designed for batch predictions on large datasets, not for real-time inference with low latency requirements.
What should I do if I get this MLS-C01 question wrong?
Review sageMaker Neo, then practise related MLS-C01 questions on the same topic to reinforce the concept.
What is the key concept behind this question?
SageMaker Neo
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Last reviewed: Jun 20, 2026
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