MLA-C01 ML Model Development Practice Question
A financial institution is training a fraud detection model using SageMaker. The dataset is highly imbalanced, with only 0.1% fraudulent transactions. The team wants to use SageMaker Automatic Model Tuning to find the best hyperparameters. They notice that the tuning job spends most of its time on configurations that predict all transactions as non-fraudulent. Which hyperparameter should they tune to directly address this issue?
⚠ Common exam trap
The trap here is focusing on general regularization or optimization hyperparameters, when the core issue is class imbalance that requires a weighting adjustment.
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
✓
scale_pos_weight
The scale_pos_weight hyperparameter in SageMaker's XGBoost algorithm adjusts the weight of the positive class, which is crucial for imbalanced datasets. By increasing this value, the model's loss function penalizes false negatives more, encouraging better detection of fraudulent transactions. This directly tackles the problem of the model predicting all instances as the majority class.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
max_depth
Why it's wrong here
max_depth determines the maximum depth of each tree. Increasing it can capture more complex patterns, but it does not correct for class imbalance. With extreme imbalance, deeper trees may still predict the majority class due to the loss function's dominance. The problem requires reweighting the classes, not increasing model capacity.
- ✗
learning_rate
Why it's wrong here
The learning rate controls the step size during gradient descent. While it affects convergence speed and final model performance, it does not directly address class imbalance. A lower learning rate might help the model learn more slowly, but without adjusting for class weights, the model may still ignore the minority class. The issue is not optimization speed but the objective's bias toward the majority class.
- ✓
scale_pos_weight
Why this is correct
In SageMaker's built-in XGBoost algorithm, scale_pos_weight controls the balance of positive and negative weights. Setting it to a higher value increases the weight of the positive class (fraudulent transactions), making the model pay more attention to them. This directly addresses the issue of the model predicting all transactions as non-fraudulent, as it penalizes misclassification of the minority class more heavily.
- ✗
subsample
Why it's wrong here
subsample controls the fraction of samples used per tree. It is a regularization technique to prevent overfitting. It does not change the class distribution or the loss function's sensitivity to the minority class. Tuning subsample alone will not resolve the issue of the model ignoring fraudulent transactions.
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Last reviewed September 2026 · checked against the official Amazon Web Services exam blueprint
This MLA-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 MLA-C01 exam.