The correct combination is XGBoost with SMOTE, feature selection via SHAP, and deployment as a SageMaker endpoint. SMOTE (Synthetic Minority Oversampling Technique) directly addresses the severe class imbalance by generating synthetic samples for the minority class, which improves recall by giving the model more positive examples to learn from, while SHAP (SHapley Additive exPlanations) provides interpretability by ranking feature importance, allowing you to select the most influential features and reduce noise from the 200 EHR variables. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this scenario tests your ability to balance performance and explainability in a regulated healthcare context—a common trap is choosing only a resampling method without considering interpretability, or using PCA for feature selection when SHAP is needed for model-agnostic explanations. Memory tip: think “SMOTE for recall, SHAP for explainability, XGBoost for structured data” to quickly recall the winning trio.
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 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 with SMOTE and SHAP balances interpretability and performance.
Key principle: NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated.
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
Static NAT maps one inside address to one outside address.
✗
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: NAT rules depend on direction and matching traffic
NAT is not only about the public address. The inside/outside interface roles and the ACL or rule that matches traffic are just as important.
Detailed technical explanation
How to think about this question
NAT questions usually test address translation, overload/PAT behaviour, static mappings and whether the right traffic is being translated. Read the interface direction and address terms carefully.
KKey Concepts to Remember
Static NAT maps one inside address to one outside address.
PAT allows many inside hosts to share one public address using ports.
Inside local and inside global describe the private and translated addresses.
NAT ACLs identify traffic for translation, not always security filtering.
TExam Day Tips
→Identify inside and outside interfaces first.
→Check whether the scenario needs static NAT, dynamic NAT or PAT.
→Do not confuse NAT matching ACLs with normal packet-filtering intent.
Key takeaway
NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated.
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.
Review the four NAT address types (inside local, inside global, outside local, outside global), PAT port overload, and static vs dynamic NAT use cases. Then practise related MLS-C01 NAT questions on configuration and troubleshooting.
Modeling — This question tests Modeling — Static NAT maps one inside address to one outside address..
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 — XGBoost with SMOTE and SHAP balances interpretability and performance.
What should I do if I get this MLS-C01 question wrong?
Review the four NAT address types (inside local, inside global, outside local, outside global), PAT port overload, and static vs dynamic NAT use cases. Then practise related MLS-C01 NAT questions on configuration and troubleshooting.
What is the key concept behind this question?
Static NAT maps one inside address to one outside address.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A healthcare company is building a model to predict patient readmission within 30 days of discharge. The dataset includes 10,000 patient records with 200 features, including lab results, demographics, and historical admissions. The target variable is highly imbalanced: only 8% of patients are readmitted. The data scientist splits the data into 80% training and 20% test sets, ensuring the same proportion of readmissions in each. The scientist trains a logistic regression model and a random forest model. The logistic regression achieves 92% accuracy but recall of 10% for the readmitted class. The random forest achieves 90% accuracy but recall of 25%. The business requirement is to achieve at least 60% recall for readmissions while maintaining reasonable precision. The scientist also has access to a large collection of unlabeled patient records from other hospitals. Which strategy should the data scientist use to meet the business requirement?
medium
A.Collect more labeled data from other hospitals.
✓ B.Use SMOTE to oversample the minority class in the training set.
C.Use random undersampling of the majority class in the training set.
D.Switch to a deep neural network with more layers.
Why B: Option B is correct because using SMOTE (Synthetic Minority Over-sampling Technique) generates synthetic samples for the minority class, which can improve recall. Option A is wrong because collecting more data may not be feasible and may not help if imbalance persists. Option C is wrong because undersampling reduces data and may lose information. Option D is wrong because changing to a deep learning model may not help with limited data.
Variation 2. A data scientist is building a fraud detection model using a highly imbalanced dataset. The model uses a random forest classifier. The recall for the minority class is 0.6, and precision is 0.9. The business requires recall above 0.8. Which action should the data scientist take to improve recall?
medium
A.Perform feature selection to remove noisy features.
B.Increase the maximum depth of the trees.
C.Increase the class weight for the minority class in the algorithm.
✓ D.Decrease the probability threshold for classifying a transaction as fraudulent.
E.Increase the number of trees in the random forest.
Why D: Option D is correct because decreasing the classification threshold for the positive class increases recall (more positives predicted) at the cost of precision. Option A (more trees) reduces variance, may not improve recall. Option B (class weights) can help but is already used. Option C (feature selection) may reduce recall. Option E (increase max depth) could lead to overfitting and not necessarily improve recall.
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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.
Question Discussion
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