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 training a binary classifier on a dataset with 1 million rows and 500 features. The model uses XGBoost and achieves an AUC of 0.95 on the training set but only 0.72 on the test set. The scientist suspects overfitting. Which combination of hyperparameter adjustments is most likely to improve generalization?
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.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Decrease 'max_depth' and increase 'min_child_weight'
Option C is correct because decreasing 'max_depth' reduces the complexity of individual trees, preventing the model from learning overly specific patterns in the training data. Increasing 'min_child_child_weight' forces the algorithm to require a higher sum of instance weights (hessian) before further partitioning, which acts as a regularization mechanism that discourages splits on noisy or sparse data. Together, these adjustments directly combat overfitting in XGBoost by limiting tree depth and requiring more evidence for splits, which improves generalization from the training AUC of 0.95 to a higher test AUC.
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.
Increase 'subsample' and decrease 'colsample_bytree'
Why it's wrong here
Increasing subsample uses more data, which might actually reduce overfitting, but the combination is not as direct as A.
✓
Decrease 'max_depth' and increase 'min_child_weight'
Why this is correct
Decreasing max_depth reduces tree complexity; increasing min_child_weight prevents overfitting by requiring more samples per leaf.
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.
✗
Decrease 'gamma' and increase 'learning_rate'
Why it's wrong here
Decreasing gamma allows more splits, increasing overfitting; increasing learning rate may cause unstable convergence.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the misconception that increasing regularization parameters like 'max_depth' or decreasing 'learning_rate' alone will fix overfitting, when in fact the correct approach is to reduce model complexity (decrease 'max_depth') and increase split regularization (increase 'min_child_weight').
Detailed technical explanation
How to think about this question
In XGBoost, 'max_depth' controls the maximum depth of a tree; deeper trees can model interactions but also memorize noise. 'min_child_weight' is the sum of instance weights (hessian) required in a child node; for binary classification with logistic loss, the hessian for each instance is p*(1-p), so a higher threshold prevents splits on small, low-confidence subsets. A real-world scenario is a medical dataset with rare events: without increasing 'min_child_weight', the model might split on a handful of patients with extreme feature values, causing overfitting that drops test AUC dramatically.
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 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
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.
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: Decrease 'max_depth' and increase 'min_child_weight' — Option C is correct because decreasing 'max_depth' reduces the complexity of individual trees, preventing the model from learning overly specific patterns in the training data. Increasing 'min_child_child_weight' forces the algorithm to require a higher sum of instance weights (hessian) before further partitioning, which acts as a regularization mechanism that discourages splits on noisy or sparse data. Together, these adjustments directly combat overfitting in XGBoost by limiting tree depth and requiring more evidence for splits, which improves generalization from the training AUC of 0.95 to a higher test AUC.
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.
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Question Discussion
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