- A
Use XGBoost with SMOTE, feature selection via SHAP, and deploy as a SageMaker endpoint
XGBoost handles missing values, SMOTE addresses imbalance, SHAP provides interpretability.
- B
Use logistic regression with one-hot encoding and random undersampling
Why wrong: One-hot encoding on high cardinality features creates huge feature space; logistic regression may not capture interactions.
- C
Use PCA for dimensionality reduction, then train a linear SVM with class weights
Why wrong: PCA loses interpretability and SVM may not handle missing values well.
- D
Use a deep neural network with embeddings for categorical variables and oversample the minority class
Why wrong: Deep networks are less interpretable and may not be needed.
Question 198 of 1,755
MLS-C01 Modeling Practice Question
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 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?
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
Option A is correct because 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.
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.
- ✓
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.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
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.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often 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.
Detailed technical explanation
How to think about this question
XGBoost uses a sparsity-aware algorithm that learns the best direction to handle missing values during training, which is critical for EHR data where missingness is often informative. SMOTE (Synthetic Minority Oversampling Technique) creates synthetic examples by interpolating between minority class samples in feature space, which helps the model learn decision boundaries without simply duplicating existing points. SHAP (SHapley Additive exPlanations) values provide consistent, locally accurate feature attributions based on cooperative game theory, enabling clinicians to trust model outputs by understanding which features drive readmission risk.
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.
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FAQ
Questions learners often ask
What does this MLS-C01 question test?
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: Use XGBoost with SMOTE, feature selection via SHAP, and deploy as a SageMaker endpoint — Option A is correct because 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.
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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Last reviewed: Jul 4, 2026
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