MLS-C01 Modeling Practice Question
A financial services company uses Amazon SageMaker to train a model for credit risk prediction. The dataset contains 500 features and 1 million records. The target variable is binary with 20% default rate. The data scientist uses a gradient boosting algorithm (XGBoost) with default hyperparameters. After training, the model achieves 95% accuracy, but the precision for the default class is only 30%, and recall is 15%. The business requires at least 50% recall and 40% precision for the default class. The data scientist tries to adjust the decision threshold, but this does not simultaneously meet both targets. The scientist suspects that the model is not learning the default patterns well. The company also has a large dataset of unlabeled transactions that could be used. Which action should the data scientist take to improve the model?
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
✓
Use the unlabeled data for semi-supervised learning with pseudo-labeling.
Using unlabeled data for pseudo-labeling can help the model learn patterns of the minority class by generating additional training examples, which is especially beneficial when the labeled dataset is imbalanced. Option A is incorrect because PCA reduces dimensionality but does not address class imbalance and may discard features important for the default class. Option C is incorrect because increasing the learning rate can cause the model to overshoot optimal minima and may lead to overfitting, not improving recall and precision for the minority class. Option D is incorrect because feature selection reduces the number of features but does not directly address class imbalance; it could even remove features that are relevant for predicting defaults.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Apply PCA to reduce dimensionality and noise.
Why it's wrong here
PCA may discard important information for the minority class.
- ✓
Use the unlabeled data for semi-supervised learning with pseudo-labeling.
Why this is correct
Pseudo-labeling leverages unlabeled data to improve minority class detection.
- ✗
Increase the learning rate to accelerate convergence.
Why it's wrong here
Higher learning rate may not help learning minority class.
- ✗
Reduce the number of features using feature selection to simplify the model.
Why it's wrong here
Feature selection may not address the imbalance.
Go deeper
Related to this question
About these practice questions
Courseiva writes every MLS-C01 question from scratch — 1,672 in total, each with an explanation and a wrong-answer breakdown. None are copied from real exams or 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.