MLS-C01 Modeling Practice Question
A company uses Amazon SageMaker to train an XGBoost model on a large dataset. Training takes a long time. Which action can reduce training time without significantly affecting model accuracy?
⚠ Common exam trap
AWS often tests the misconception that increasing learning rate or using more powerful hardware always speeds up training without side effects, but the correct answer focuses on algorithmic efficiency rather than resource scaling.
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
✓
Enable early stopping
Early stopping halts training when the model's performance on a validation set stops improving for a specified number of rounds. This prevents overfitting and reduces training time by eliminating unnecessary iterations, while typically preserving accuracy because the optimal model is already found.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use a deep neural network instead
Why it's wrong here
DNNs often take longer.
- ✗
Increase the learning rate
Why it's wrong here
Higher learning rate may cause instability.
- ✗
Use a larger instance type
Why it's wrong here
Larger instances speed up but cost more.
- ✓
Enable early stopping
Why this is correct
Early stopping stops when no improvement.
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JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.