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. 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 data scientist trains a gradient boosting model on a large dataset using SageMaker. The training completes successfully, but when deploying the model to a real-time endpoint, inference latency is too high. Which change is MOST likely to reduce latency without significant accuracy loss?
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.
Use a larger instance type for the endpoint
Why wrong: Larger instance may not address model complexity.
Prune the trees by removing nodes with low importance
Pruning reduces model size and inference time.
Increase the number of trees in the ensemble
Why wrong: More trees increase latency.
Use SageMaker Batch Transform instead of real-time
Why wrong: Batch Transform is for offline predictions, not reducing latency for real-time.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Prune the trees by removing nodes with low importance
Pruning trees by removing nodes with low importance reduces the model's complexity, which directly decreases inference latency because fewer decision paths need to be evaluated. In gradient boosting, this can be done with minimal accuracy loss if the removed nodes correspond to splits that contribute little to the overall prediction, as measured by feature importance or gain.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Use a larger instance type for the endpoint
Why it's wrong here
Larger instance may not address model complexity.
Prune the trees by removing nodes with low importance
Why this is correct
Pruning reduces model size and inference time.
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 ensemble
Why it's wrong here
More trees increase latency.
Use SageMaker Batch Transform instead of real-time
Why it's wrong here
Batch Transform is for offline predictions, not reducing latency for real-time.
Common exam traps
The trap here is that candidates often confuse scaling the endpoint (Option A) as the primary fix for latency, when the real issue is model complexity that can be reduced through pruning without significant accuracy loss.
Detailed technical explanation
Gradient boosting models like XGBoost or LightGBM store trees as a sequence of split conditions; pruning low-importance nodes reduces the total number of nodes traversed per inference. In practice, post-training pruning using gain-based importance (e.g., setting a minimum gain threshold) can reduce model size by 30-50% with less than 1% accuracy drop, especially when the model was trained with early stopping or excessive depth. Real-world scenarios like high-traffic web APIs benefit from this because it lowers p99 latency without requiring hardware upgrades.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
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. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. 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
Modeling — This question tests Modeling — Read the scenario before looking for a memorised answer..
The correct answer is: Prune the trees by removing nodes with low importance — Pruning trees by removing nodes with low importance reduces the model's complexity, which directly decreases inference latency because fewer decision paths need to be evaluated. In gradient boosting, this can be done with minimal accuracy loss if the removed nodes correspond to splits that contribute little to the overall prediction, as measured by feature importance or gain.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Yes — watch for: "most likely". Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 24, 2026
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