Last Updated: Aug 05, 2026
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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Building Models | 40-46% | - Build regression models - Understand supervised machine learning concepts - Build neural networks and SVM models - Build decision trees and ensemble models - Integrate custom code - Use model interpretability tools |
| Topic 2: Data Sources | 30-36% | - Perform variable selection - Explore and understand data - Reduce dimensionality - Modify and prepare data - Create a project in Model Studio |
| Topic 3: Model Assessment and Deployment | 24-30% | - Apply model assessment principles - Evaluate and compare model performance - Select champion models - Deploy models into production |
1. In the context of model deployment, what is "model compliance"?
A) The model's simplicity
B) The degree to which the model adheres to regulatory or ethical guidelines
C) The model's efficiency
D) The process of feature selection
2. Which SAS Viya component is typically used for deploying and monitoring machine learning models in production?
A) SAS Enterprise Miner
B) SAS Visual Analytics
C) SAS Model Manager
D) SAS Data Loader
3. What is the primary purpose of model documentation in the model deployment phase?
A) To provide information on the model's development, architecture, and usage
B) To create synthetic data
C) To assess data quality
D) To evaluate the model's accuracy
4. What is the purpose of cross-validation in model building and evaluation?
A) Assessing the model's generalization performance
B) Generating synthetic data
C) Splitting the dataset into training and testing sets
D) Reducing the dataset size
5. A project has been created and a pipeline has been run in Model Studio.
Which project setting can you edit?
A) Advisor Options for missing values
B) Event-based Sampling proportions
C) Partition Data percentages
D) Rules for model comparison statistic
Solutions:
| Question # 1 Answer: B | Question # 2 Answer: C | Question # 3 Answer: A | Question # 4 Answer: A | Question # 5 Answer: D |
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