- A
Increase the number of instances behind the endpoint
Why wrong: Increasing the number of instances would distribute load but does not reduce per-request latency; it may even increase it due to overhead. Incorrect.
- B
Use SageMaker's batch transform instead of real-time endpoint
Why wrong: Batch transform is for offline predictions, not real-time inference. Incorrect.
- C
Reduce the number of features by selecting the most important ones
Reducing to the most important features directly reduces model complexity and inference time without retraining. Correct.
- D
Use SageMaker's Elastic Inference to attach an acceleration to the endpoint
Why wrong: SageMaker Elastic Inference is designed to accelerate deep learning models (e.g., TensorFlow, PyTorch) by attaching a GPU accelerator. It does not speed up tree-based models like XGBoost because they do not effectively utilize GPUs. Therefore, Option D is incorrect.
MLS-C01 Feature selection for latency Practice Question
This MLS-C01 practice question tests your understanding of modeling. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. A key principle to apply: feature selection for latency. 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 company uses SageMaker to deploy a real-time inference endpoint for a fraud detection model. The model is an XGBoost model trained on 50 features. The endpoint receives 100 requests per second, but latency is higher than the required 200 ms. The team wants to reduce latency without retraining. What should they 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
Reduce the number of features by selecting the most important ones
To reduce inference latency without retraining the XGBoost model, reducing the number of features to the most important ones directly decreases the computational complexity of the model, as fewer tree splits are evaluated per request. This is a model-level optimization that does not require retraining if the feature importance is already known. SageMaker Elastic Inference, however, is designed to accelerate deep learning models by attaching a GPU accelerator; it does not speed up XGBoost or other tree-based models because they do not utilize GPUs effectively. Therefore, only option C is correct.
Key principle: Feature selection for latency
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Increase the number of instances behind the endpoint
Why it's wrong here
Increasing the number of instances would distribute load but does not reduce per-request latency; it may even increase it due to overhead. Incorrect.
- ✗
Use SageMaker's batch transform instead of real-time endpoint
Why it's wrong here
Batch transform is for offline predictions, not real-time inference. Incorrect.
- ✓
Reduce the number of features by selecting the most important ones
Why this is correct
Reducing to the most important features directly reduces model complexity and inference time without retraining. Correct.
Related concept
Feature selection for latency
- ✗
Use SageMaker's Elastic Inference to attach an acceleration to the endpoint
Why it's wrong here
SageMaker Elastic Inference is designed to accelerate deep learning models (e.g., TensorFlow, PyTorch) by attaching a GPU accelerator. It does not speed up tree-based models like XGBoost because they do not effectively utilize GPUs. Therefore, Option D is incorrect.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap is that candidates may assume Elastic Inference works for any model type, but it is specifically for deep learning. They might also overlook that retraining is not required for feature selection if importance is already established.
Detailed technical explanation
How to think about this question
XGBoost models compute predictions by traversing decision trees, where each node evaluates a feature split; reducing features eliminates entire branches from the tree structure, directly lowering the number of comparisons per inference. Feature importance can be derived from the model's built-in gain or cover metrics without retraining, allowing the team to prune low-impact features while preserving accuracy. In practice, this technique is often used when inference latency is bottlenecked by I/O or CPU-bound feature engineering, not by model size alone.
KKey Concepts to Remember
- Feature selection for latency
- SageMaker Elastic Inference
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
Feature selection for latency
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. Feature selection for latency 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
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FAQ
Questions learners often ask
What does this MLS-C01 question test?
Modeling — This question tests Modeling — Feature selection for latency.
What is the correct answer to this question?
The correct answer is: Reduce the number of features by selecting the most important ones — To reduce inference latency without retraining the XGBoost model, reducing the number of features to the most important ones directly decreases the computational complexity of the model, as fewer tree splits are evaluated per request. This is a model-level optimization that does not require retraining if the feature importance is already known. SageMaker Elastic Inference, however, is designed to accelerate deep learning models by attaching a GPU accelerator; it does not speed up XGBoost or other tree-based models because they do not utilize GPUs effectively. Therefore, only option C is correct.
What should I do if I get this MLS-C01 question wrong?
Review feature selection for latency, then practise related MLS-C01 questions on the same topic to reinforce the concept.
What is the key concept behind this question?
Feature selection for latency
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Last reviewed: Jul 4, 2026
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