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SASInstitute A00-406 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Model Assessment and Deployment | 24-30% | - Deploying models into production - Assessing model performance (metrics, ROC curves, confusion matrices) |
| Building Models | 40-46% | - Model comparison and selection - Supervised model creation (decision trees, ensembles, SVM, neural networks) |
| Data Sources | 30-36% | - Dimensionality reduction and feature engineering - Importing and preparing data - Exploring and modifying data |
SASInstitute SAS® Viya® Supervised Machine Learning Pipelines Sample Questions:
1. What is the main advantage of ensemble learning methods, such as Random Forest, in a machine learning pipeline?
A) They are not suitable for large datasets.
B) They combine multiple models to improve predictive performance.
C) They are simple and easy to interpret.
D) They require minimal data preprocessing.
2. Which SAS Viya component is typically used for deploying and monitoring machine learning models in production?
A) SAS Model Manager
B) SAS Data Loader
C) SAS Enterprise Miner
D) SAS Visual Analytics
3. Which metric is commonly used to evaluate the performance of a regression model?
A) Mean Absolute Error (MAE)
B) Confusion Matrix
C) Precision
D) F1 Score
4. Which hyperparameter in a decision tree model controls the depth of the tree and helps prevent overfitting?
A) Learning rate
B) Max features
C) Min samples split
D) Max depth
5. In the context of model building, what is the purpose of hyperparameter tuning?
A) Visualizing data
B) Optimizing the model's hyperparameters for better performance
C) Training the model
D) Selecting the most important features
Solutions:
| Question # 1 Answer: B | Question # 2 Answer: A | Question # 3 Answer: A | Question # 4 Answer: D | Question # 5 Answer: B |
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