MLS-C01 Modeling Practice Question
A bank is building a credit risk model using a large dataset with 500 features and 2 million samples. The dataset contains many categorical features with high cardinality (e.g., zip code, occupation). The model must be deployed on SageMaker and provide real-time predictions with low latency. They also need to explain individual predictions for regulatory compliance. Which approach is most appropriate?
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 a gradient boosting model with ordinal encoding for categorical features and use SageMaker's built-in XGBoost with SHAP
XGBoost with ordinal encoding and SHAP balances performance, latency, and explainability.
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 a linear model with target encoding for categorical features and deploy with SageMaker's built-in linear learner algorithm
Why it's wrong here
Linear models may not capture complex interactions.
- ✗
Use a deep neural network with embedding layers for categorical features and use SageMaker's built-in Debugger for explanations
Why it's wrong here
Deep networks may be slower and Debugger is for training, not inference explanations.
- ✗
Use XGBoost with one-hot encoding for categorical features and deploy with SageMaker's built-in SHAP explainer
Why it's wrong here
One-hot encoding on high cardinality features creates too many features, increasing latency.
- ✓
Use a gradient boosting model with ordinal encoding for categorical features and use SageMaker's built-in XGBoost with SHAP
Why this is correct
Ordinal encoding handles high cardinality without explosion; XGBoost captures interactions; SHAP provides explanations.
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.