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Last Updated: Aug 28, 2026
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| Section | Weight | Objectives |
|---|---|---|
| Building Models | 40-46% | - Supervised model creation (decision trees, ensembles, SVM, neural networks) - Model comparison and selection |
| Model Assessment and Deployment | 24-30% | - Assessing model performance (metrics, ROC curves, confusion matrices) - Deploying models into production |
| Data Sources | 30-36% | - Dimensionality reduction and feature engineering - Exploring and modifying data - Importing and preparing data |
Question 1
Which metric is commonly used to evaluate the performance of a regression model?
A. Confusion Matrix
B. F1 Score
C. Precision
D. Mean Absolute Error (MAE)
Question 2
What is the primary purpose of a supervised machine learning pipeline in SAS Viya?
A. Model training and evaluation
B. Data visualization
C. Data preprocessing and cleaning
D. Data storage and retrieval
Question 3
In the context of model assessment, what does "bias" refer to?
A. A systematic error that causes a model to consistently overpredict
B. The simplicity of the model
C. A measure of the model's precision
D. The process of feature engineering
Question 4
What is the primary objective of model validation during the model assessment phase?
A. To assess the accuracy of the model
B. To create synthetic data
C. To ensure the model generalizes well to new, unseen data
D. To build a model from scratch
Question 5
What is the primary purpose of model documentation in the model deployment phase?
A. To evaluate the model's accuracy
B. To assess data quality
C. To create synthetic data
D. To provide information on the model's development, architecture, and usage
Solutions:
| Question 1 Answer: D | Question 2 Answer: A | Question 3 Answer: A | Question 4 Answer: C | Question 5 Answer: D |
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