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SASInstitute A00-406 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Model Assessment and Deployment | 24-30% | - Select champion models - Deploy models into production - Evaluate and compare model performance - Apply model assessment principles |
| Data Sources | 30-36% | - Modify and prepare data - Perform variable selection - Reduce dimensionality - Create a project in Model Studio - Explore and understand data |
| Building Models | 40-46% | - Build neural networks and SVM models - Build regression models - Build decision trees and ensemble models - Use model interpretability tools - Integrate custom code - Understand supervised machine learning concepts |
SASInstitute SAS® Viya® Supervised Machine Learning Pipelines Sample Questions:
In reinforcement learning, what is the "reward signal"?
- A. The accuracy of the model's predictions
- B. A regularization parameter
- C. A numerical value that indicates the performance of an action taken by the agent
- D. The final prediction made by the model
Correct Answer: C 🗳️
What is the purpose of data profiling in data source management?
- A. To assess the quality and characteristics of data
- B. To optimize data storage
- C. To execute data queries
- D. To create data visualizations
Correct Answer: A 🗳️
What is a data lake?
- A. A centralized repository for storing all structured and unstructured data at any scale
- B. A specialized database for time-series data
- C. A data storage solution designed for high-speed data retrieval
- D. A backup system for relational databases
Correct Answer: A 🗳️
What is the purpose of an ROC curve (Receiver Operating Characteristic) in model assessment?
- A. To measure feature importance
- B. To evaluate regression models
- C. To compare a model's true positive rate with the false positive rate
- D. To visualize data distribution
Correct Answer: C 🗳️
What is the main advantage of ensemble methods in model building?
- A. They produce simple and interpretable models
- B. They require minimal data preprocessing
- C. They work well with high-dimensional data
- D. They combine multiple models to improve predictive performance
Correct Answer: D 🗳️
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