This MLS-C01 practice question tests your understanding of modeling. Compare every option against the stated constraints before choosing — the best answer satisfies all requirements, not just the most obvious one. 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 classification model using Amazon SageMaker's XGBoost. The dataset is highly imbalanced (99% negative class, 1% positive class). The data scientist wants to maximize the F1-score. Which parameter adjustment is most appropriate?
Why wrong: Increasing max_depth increases model complexity, which may lead to overfitting, not address imbalance.
B
Set eta to 0.01
Why wrong: Eta (learning rate) controls the step size; lowering it can improve generalization but does not directly handle imbalance.
C
Set subsample to 0.5
Why wrong: Subsample controls the fraction of samples used per tree; it helps with overfitting but not class imbalance directly.
D
Set scale_pos_weight to 99
scale_pos_weight adjusts the balance of positive and negative weights; a value of 99 (ratio of negatives to positives) helps the model focus on the minority class.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Set scale_pos_weight to 99
Setting scale_pos_weight to 99 is the most appropriate adjustment because it directly addresses class imbalance by assigning a higher weight to the minority (positive) class during training. In XGBoost, scale_pos_weight controls the balance of positive and negative weights, typically set as sum(negative instances) / sum(positive instances), which here is 99/1 = 99. This forces the model to penalize misclassifications of the positive class more heavily, thereby improving recall and F1-score.
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.
✗
Set max_depth to 10
Why it's wrong here
Increasing max_depth increases model complexity, which may lead to overfitting, not address imbalance.
✗
Set eta to 0.01
Why it's wrong here
Eta (learning rate) controls the step size; lowering it can improve generalization but does not directly handle imbalance.
✗
Set subsample to 0.5
Why it's wrong here
Subsample controls the fraction of samples used per tree; it helps with overfitting but not class imbalance directly.
✓
Set scale_pos_weight to 99
Why this is correct
scale_pos_weight adjusts the balance of positive and negative weights; a value of 99 (ratio of negatives to positives) helps the model focus on the minority class.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may confuse hyperparameters that control model complexity (max_depth, subsample) or learning rate (eta) with those that directly handle class imbalance, missing that scale_pos_weight is the specific XGBoost parameter designed for this purpose.
Detailed technical explanation
How to think about this question
Under the hood, scale_pos_weight in XGBoost modifies the gradient of the loss function for positive class instances, effectively increasing the penalty for false negatives. This is equivalent to using a weighted cross-entropy loss where the weight for the positive class is set to the imbalance ratio. In practice, for highly imbalanced datasets like fraud detection (e.g., 0.1% fraud cases), setting scale_pos_weight to the inverse of the class ratio is a standard first step to improve recall without sacrificing too much precision.
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
An e-commerce site experiences heavy traffic on Black Friday and near-zero traffic during off-peak weeks. Rather than provisioning permanent large VMs, the team uses auto-scaling groups that add capacity automatically under load and reduce it overnight. Questions like this test whether you understand elasticity, availability zones, and cloud compute scaling patterns.
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: Set scale_pos_weight to 99 — Setting scale_pos_weight to 99 is the most appropriate adjustment because it directly addresses class imbalance by assigning a higher weight to the minority (positive) class during training. In XGBoost, scale_pos_weight controls the balance of positive and negative weights, typically set as sum(negative instances) / sum(positive instances), which here is 99/1 = 99. This forces the model to penalize misclassifications of the positive class more heavily, thereby improving recall and F1-score.
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.
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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