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SASInstitute A00-406 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Model Assessment and Deployment | 24-30% | - Apply model assessment principles - Evaluate and compare model performance - Deploy models into production - Select champion models |
| Topic 2: Data Sources | 30-36% | - Explore and understand data - Reduce dimensionality - Perform variable selection - Create a project in Model Studio - Modify and prepare data |
| Topic 3: Building Models | 40-46% | - Build regression models - Build neural networks and SVM models - Build decision trees and ensemble models - Understand supervised machine learning concepts - Integrate custom code - Use model interpretability tools |
SASInstitute SAS® Viya® Supervised Machine Learning Pipelines Sample Questions:
What is the main advantage of ensemble learning methods, such as Random Forest, in a machine learning pipeline?
- A. They require minimal data preprocessing.
- B. They combine multiple models to improve predictive performance.
- C. They are not suitable for large datasets.
- D. They are simple and easy to interpret.
Correct Answer: B 🗳️
What is the purpose of cross-entropy loss in machine learning, especially in the context of classification?
- A. To calculate the mean squared error of a regression model
- B. To measure the dissimilarity between predicted and actual class probabilities
- C. To quantify the variance of a model
- D. To evaluate feature importance
Correct Answer: B 🗳️
Which evaluation metric is commonly used for assessing the performance of a regression model?
- A. Mean Absolute Error (MAE)
- B. Confusion Matrix
- C. F1 Score
- D. Precision
Correct Answer: A 🗳️
Which hyperparameter in a decision tree model controls the depth of the tree and helps prevent overfitting?
- A. Min samples split
- B. Max depth
- C. Max features
- D. Learning rate
Correct Answer: B 🗳️
In reinforcement learning, what is the agent's objective?
- A. To make predictions
- B. To maximize a cumulative reward over time
- C. To generate synthetic data
- D. To learn from labeled data
Correct Answer: B 🗳️
