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Question 527 of 1,755

Modeling →hardMultiple ChoiceObjective-mapped

Reducing SageMaker Inference Latency with Feature Importance

This MLS-C01 practice question tests your understanding of modeling. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. 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 company is deploying a real-time fraud detection system using a gradient boosting model on AWS SageMaker. The model uses 200 features and is trained on 50 GB of data. The inference latency requirement is under 10 ms per request. During load testing, the endpoint shows average latency of 15 ms. Which change is MOST likely to reduce latency below 10 ms?

Clue words in this question

Noticing these words before you look at the options changes how you read each choice.

Clue: "most likely"
Why it matters: Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.

A
Switch to a GPU-based instance type
Why wrong: Gradient boosting models typically run faster on CPUs; GPUs are beneficial for neural networks.
B
Reduce the number of features to the top 50 based on feature importance
Fewer features reduce inference computation time, directly lowering latency.
C
Increase the number of trees in the model
Why wrong: More trees increase computation and latency.
D
Use a larger batch size for inference
Why wrong: Batch size affects throughput, not per-request latency for real-time endpoints.

Full breakdown with real-world context →

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 to the top 50 based on feature importance

Reducing the number of features from 200 to the top 50 directly decreases the amount of data each inference request must process, which lowers both feature engineering overhead and model evaluation time. For gradient boosting models on SageMaker, fewer features mean fewer decision tree splits to traverse per prediction, which can significantly reduce latency without requiring hardware changes. This is the most direct and cost-effective way to meet the 10 ms requirement.

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.

✗
Switch to a GPU-based instance type
Why it's wrong here
Gradient boosting models typically run faster on CPUs; GPUs are beneficial for neural networks.
✓
Reduce the number of features to the top 50 based on feature importance
Why this is correct
Fewer features reduce inference computation time, directly lowering latency.
Clue confirmation
The clue word "most likely" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
✗
Increase the number of trees in the model
Why it's wrong here
More trees increase computation and latency.
✗
Use a larger batch size for inference
Why it's wrong here
Batch size affects throughput, not per-request latency for real-time endpoints.

Common exam traps

Common exam trap: answer the scenario, not the keyword

The trap here is that candidates often assume GPU instances universally speed up inference, but for tree-based models like gradient boosting, the bottleneck is sequential tree traversal, not parallel computation, so feature reduction is the correct optimization.

Detailed technical explanation

How to think about this question

Gradient boosting models like XGBoost or LightGBM evaluate each tree sequentially during inference; the total latency is proportional to the number of trees multiplied by the average depth of each tree. Reducing features reduces the depth and complexity of each tree, as fewer splits are needed. In real-world scenarios, feature importance pruning can often maintain 95%+ of model accuracy while cutting latency by over 50%, making it a standard optimization technique for real-time systems.

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

A startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.

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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FAQ

Questions learners often ask

What does this MLS-C01 question test?

Modeling — This question tests Modeling — Read the scenario before looking for a memorised answer..

What is the correct answer to this question?

The correct answer is: Reduce the number of features to the top 50 based on feature importance — Reducing the number of features from 200 to the top 50 directly decreases the amount of data each inference request must process, which lowers both feature engineering overhead and model evaluation time. For gradient boosting models on SageMaker, fewer features mean fewer decision tree splits to traverse per prediction, which can significantly reduce latency without requiring hardware changes. This is the most direct and cost-effective way to meet the 10 ms requirement.

What should I do if I get this MLS-C01 question wrong?

Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.

Are there clue words in this question I should notice?

Yes — watch for: "most likely". Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.

What is the key concept behind this question?

Read the scenario before looking for a memorised answer.

About these practice questions

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Same concept, more angles

3 more ways this is tested on MLS-C01

These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.

Variation 1. A company has deployed a real-time inference endpoint using SageMaker for a fraud detection model. The model uses a Random Forest classifier. The endpoint receives predictions but the latency is too high. The metric shows p99 latency of 500ms, but the requirement is under 200ms. The team has already optimized the instance type to the maximum allowed by their budget. The data scientist suggests: A) Reducing the number of trees in the Random Forest model. B) Switching to a linear model like Logistic Regression. C) Enabling SageMaker's batch transform instead of real-time endpoint. D) Adding more instances to the endpoint behind a load balancer. Which option will MOST effectively reduce latency while maintaining acceptable accuracy?

easy

A.Switch to a linear model like Logistic Regression
✓ B.Reduce the number of trees in the Random Forest model
C.Enable SageMaker's batch transform
D.Add more instances to the endpoint

Why B: Option B (Reducing the number of trees) is the most effective method to reduce latency while maintaining acceptable accuracy. Fewer trees directly decrease inference time of the Random Forest model, although it may slightly impact accuracy. Switching to a linear model (Option A) would reduce latency but likely result in significant accuracy loss. Batch transform (Option C) is not suitable for real-time inference. Adding more instances (Option D) improves throughput but not per-request latency.

Variation 2. A company is deploying a real-time fraud detection model using Amazon SageMaker. The model must make predictions in under 100 milliseconds. The data scientist uses a pre-trained XGBoost model and deploys it to a SageMaker endpoint with an ml.c5.xlarge instance. After load testing, the average latency is 150 ms. Which action should the data scientist take to reduce latency?

medium

A.Reduce the number of trees in the XGBoost model
B.Deploy multiple instances behind a load balancer
✓ C.Enable SageMaker Neo to compile the model for the target instance
D.Use a larger instance type to increase compute capacity

Why C: Option C is correct because SageMaker Neo optimizes trained models for the target hardware platform by compiling them into an efficient runtime. This reduces inference latency without changing the model architecture, making it ideal for meeting the sub-100ms requirement when the current latency is 150ms on an ml.c5.xlarge instance.

Variation 3. A company is deploying a machine learning model for real-time fraud detection. The model must have low latency (under 100 ms) and high throughput. The data scientist trains a gradient boosting model and deploys it to a SageMaker endpoint with a single ml.c5.xlarge instance. During load testing, the endpoint exceeds the latency threshold. Which change is MOST likely to reduce latency?

hard

✓ A.Replace the model with a simpler model, such as logistic regression
B.Use a larger instance type, such as ml.c5.4xlarge
C.Switch to batch transform for inference
D.Enable automatic scaling on the endpoint

Why A: Option A is correct because replacing the gradient boosting model with a simpler model like logistic regression reduces the computational complexity per inference. Gradient boosting involves traversing many decision trees, each requiring multiple conditional checks and arithmetic operations, while logistic regression is a single linear transformation. This directly lowers CPU utilization per request, reducing latency under the same instance resources.

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This MLS-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 MLS-C01 exam.