MLS-C01 Modeling Practice Question
A healthcare company is building a model to predict patient readmission within 30 days. They have structured electronic health records (EHR) data with 200 features. The data includes missing values, categorical variables with high cardinality (e.g., diagnosis codes), and a severe class imbalance (5% readmission). They need to deploy a model on SageMaker that is interpretable and achieves high recall for the positive class. Which combination of techniques should they use?
⚠ Common exam trap
A common mix-up: candidates choose logistic regression (Option B) for interpretability without considering the practical issues of high-cardinality categorical variables and class imbalance, or they select deep learning (Option D) for its flexibility but overlook the strict interpretability requirement in healthcare.
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 XGBoost with SMOTE, feature selection via SHAP, and deploy as a SageMaker endpoint
XGBoost natively handles missing values, making it well-suited for EHR data with missing entries. SMOTE addresses the severe class imbalance by generating synthetic samples of the minority class, which improves recall. SHAP feature selection provides interpretability by identifying the most influential features, and deploying as a SageMaker endpoint enables real-time predictions. This combination directly meets the requirements of high recall and interpretability.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Use XGBoost with SMOTE, feature selection via SHAP, and deploy as a SageMaker endpoint
Why this is correct
XGBoost handles missing values, SMOTE addresses imbalance, SHAP provides interpretability.
- ✗
Use logistic regression with one-hot encoding and random undersampling
Why it's wrong here
One-hot encoding on high cardinality features creates huge feature space; logistic regression may not capture interactions.
- ✗
Use PCA for dimensionality reduction, then train a linear SVM with class weights
Why it's wrong here
PCA loses interpretability and SVM may not handle missing values well.
- ✗
Use a deep neural network with embeddings for categorical variables and oversample the minority class
Why it's wrong here
Deep networks are less interpretable and may not be needed.
Go deeper
Related to this question
About these practice questions
This MLS-C01 question is part of Courseiva's 1,672-question bank — original exam-style content with full explanations and wrong-answer analysis, never real exam questions or exam 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.