Quick Answer
The answer is gamma, learning rate (eta), and subsample. Gamma, or minimum loss reduction, directly controls the complexity of a tree by requiring a minimum reduction in loss for a split to occur, which prunes overly specific branches that memorize noise. The learning rate (eta) scales the contribution of each tree, and a lower learning rate forces the model to learn slowly, reducing the influence of any single tree and thereby lowering overfitting risk. Subsampling randomly selects a fraction of training data for each tree, introducing randomness that prevents the model from fitting the training set too perfectly. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your understanding of how gradient boosting overfitting reduction hyperparameters interact—a common trap is confusing max_depth with gamma, but gamma is the more direct regularization parameter for leaf splits. Remember the mnemonic "GLS" for Gamma, Learning rate, and Subsampling to recall the three key levers against overfitting.
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 is tuning a gradient boosting model using Amazon SageMaker Automatic Model Tuning (AMT). Which THREE hyperparameters should the scientist consider tuning to reduce overfitting? (Select THREE.)
Subsample ratio
Why wrong: Subsample also reduces overfitting but is not among the three selected.
Learning rate (eta)
Lower learning rate reduces overfitting.
Minimum child weight (min_child_weight)
Higher values prevent overfitting.
Gamma (minimum loss reduction)
Higher gamma makes the model more conservative.
Maximum depth (max_depth)
Why wrong: Reducing max depth helps, but not selected.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Learning rate (eta)
Learning rate (eta) controls the contribution of each tree to the ensemble. A lower learning rate forces the model to learn more slowly, requiring more trees but reducing the risk of overfitting by preventing any single tree from having too much influence on the final prediction.
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.
Subsample ratio
Why it's wrong here
Subsample also reduces overfitting but is not among the three selected.
Learning rate (eta)
Why this is correct
Lower learning rate reduces overfitting.
Related concept
Read the scenario before looking for a memorised answer.
Minimum child weight (min_child_weight)
Why this is correct
Higher values prevent overfitting.
Related concept
Read the scenario before looking for a memorised answer.
Gamma (minimum loss reduction)
Why this is correct
Higher gamma makes the model more conservative.
Related concept
Read the scenario before looking for a memorised answer.
Maximum depth (max_depth)
Why it's wrong here
Reducing max depth helps, but not selected.
Common exam traps
The trap here is that candidates often assume all listed hyperparameters are equally effective for reducing overfitting, but the exam expects knowledge that subsample ratio and maximum depth are also valid regularization parameters, yet the question specifically selects min_child_weight, gamma, and learning rate as the three to focus on.
Detailed technical explanation
Minimum child weight (min_child_weight) sets the minimum sum of instance weight (hessian) needed in a child node; increasing it forces the algorithm to avoid splits that only capture noise, effectively pruning the tree. Gamma (minimum loss reduction) specifies the minimum reduction in loss required to make a further partition on a leaf node; higher gamma values make the algorithm more conservative, reducing overfitting by preventing splits that offer marginal improvement. Both parameters act as built-in regularization in XGBoost and similar gradient boosting frameworks.
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: Learning rate (eta) — Learning rate (eta) controls the contribution of each tree to the ensemble. A lower learning rate forces the model to learn more slowly, requiring more trees but reducing the risk of overfitting by preventing any single tree from having too much influence on the final prediction.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Same concept, more angles
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 data scientist is tuning a gradient boosting model using Amazon SageMaker's Automatic Model Tuning (hyperparameter optimization). The objective metric is validation:auc. After 50 training jobs, the best model still has a validation AUC of only 0.65. The scientist suspects overfitting because the training AUC is 0.99. Which hyperparameter configuration is MOST likely to reduce overfitting?
hardWhy A: Increasing lambda (L2 regularization) from 1 to 10 adds a stronger penalty on the magnitude of leaf weights in the gradient boosting model. This directly reduces overfitting by discouraging the model from fitting noise in the training data, which is consistent with the observed gap between training AUC (0.99) and validation AUC (0.65). In XGBoost, lambda controls the L2 regularization term on weights, and a higher value forces the model to be simpler and more generalizable.
Variation 2. A data scientist is tuning a gradient boosting model using Amazon SageMaker Automatic Model Tuning. The objective metric is AUC. The training job converges quickly but the final model has low AUC on the validation set. Which hyperparameter should the data scientist adjust to improve validation AUC?
hardWhy B: Decreasing the learning rate and increasing the number of rounds is the correct approach because a low learning rate forces the model to take smaller steps toward the optimum, reducing overfitting and allowing more trees to contribute to the ensemble. This combination often improves generalization and validation AUC when the training job converges too quickly, indicating that the model is overfitting or underfitting due to aggressive learning.
Last reviewed: Jun 24, 2026
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