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SASInstitute A00-406 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Building Models | 40-46% | - Build decision trees and ensemble models - Integrate custom code - Use model interpretability tools - Understand supervised machine learning concepts - Build neural networks and SVM models - Build regression models |
| Topic 2: Model Assessment and Deployment | 24-30% | - Apply model assessment principles - Deploy models into production - Select champion models - Evaluate and compare model performance |
| Topic 3: Data Sources | 30-36% | - Create a project in Model Studio - Modify and prepare data - Perform variable selection - Reduce dimensionality - Explore and understand data |
SASInstitute SAS® Viya® Supervised Machine Learning Pipelines Sample Questions:
What is metadata in the context of data sources?
- A. Data that is in a non-standard, proprietary format
- B. Data that is stored in a physical format
- C. Data that is encrypted for security
- D. Data about data, providing information such as data source, structure, and context
Correct Answer: D 🗳️
In machine learning, what does "overfitting" refer to?
- A. A model that has too much complexity and fits the training data too closely
- B. A model that is undertrained and has high bias
- C. A model that performs well on new, unseen data
- D. A model that is unable to make predictions
Correct Answer: A 🗳️
What does the term "bias" in machine learning refer to?
- A. The simplicity of a model
- B. Systematic errors that cause a model to consistently underpredict or overpredict
- C. The overall accuracy of a model
- D. A model's inability to generalize to new data
Correct Answer: B 🗳️
What does API stand for in the context of data sources?
- A. Application Program Interface
- B. Application Programming Interface
- C. Advanced Programming Integration
- D. Automated Program Integration
Correct Answer: B 🗳️
What is the primary purpose of model assessment in the context of data science and machine learning?
- A. Data visualization
- B. Data preprocessing
- C. Evaluating and selecting the best-performing model
- D. Model building
Correct Answer: C 🗳️
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