MLS-C01 Modeling Practice Question
A data scientist is using Amazon SageMaker built-in XGBoost algorithm to train a regression model. The training job completes successfully but the model performance on the test set is poor, with high bias. Which hyperparameter adjustment is most likely to help reduce bias?
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
✓
Increase the max_depth parameter.
High bias (underfitting) can be reduced by increasing the model complexity. Increasing max_depth allows more complex trees. Decreasing max_depth would increase bias. Increasing gamma increases regularization and bias. Reducing num_round (number of trees) reduces complexity.
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 max_depth parameter.
Why this is correct
Increasing max_depth allows trees to learn more complex patterns, reducing bias.
- ✗
Reduce the num_round parameter.
Why it's wrong here
Reducing num_round reduces the number of trees, which can increase bias.
- ✗
Increase the gamma parameter.
Why it's wrong here
Gamma controls regularization; increasing it increases bias.
- ✗
Decrease the max_depth parameter.
Why it's wrong here
Decreasing max_depth would increase bias, not reduce it.
Go deeper
Related to this question
About these practice questions
This MLS-C01 question is part of Courseiva's 1,672-question bank — original exam-style content with full explanations and wrong-answer analysis, never real exam questions or exam dumps. Learn why practice questions differ from exam dumps →
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.