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SASInstitute A00-406 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Sources | 30-36% | - Reduce dimensionality - Explore and understand data - Create a project in Model Studio - Modify and prepare data - Perform variable selection |
| Topic 2: Building Models | 40-46% | - Understand supervised machine learning concepts - Use model interpretability tools - Build neural networks and SVM models - Build decision trees and ensemble models - Integrate custom code - Build regression models |
| Topic 3: Model Assessment and Deployment | 24-30% | - Evaluate and compare model performance - Deploy models into production - Apply model assessment principles - Select champion models |
SASInstitute SAS® Viya® Supervised Machine Learning Pipelines Sample Questions:
In the context of model deployment, what is "model compliance"?
- A. The model's efficiency
- B. The process of feature selection
- C. The model's simplicity
- D. The degree to which the model adheres to regulatory or ethical guidelines
Correct Answer: D 🗳️
What does the term "bagging" refer to in ensemble learning?
- A. The process of combining multiple identical models to reduce variance
- B. A form of dimensionality reduction
- C. A type of feature extraction
- D. A technique that reduces model complexity
Correct Answer: A 🗳️
In a machine learning pipeline, what is the purpose of cross-validation?
- A. To split the dataset into training and testing sets
- B. To evaluate the model's performance on new data
- C. To train multiple models on different subsets of the data to assess generalization
- D. To visualize the data distribution
Correct Answer: C 🗳️
What is the difference between a classification problem and a regression problem in machine learning?
- A. Classification predicts categorical outcomes, while regression predicts numeric outcomes.
- B. There is no difference; the terms are used interchangeably.
- C. Classification is a type of regression problem.
- D. Regression predicts categorical outcomes, while classification predicts numeric outcomes.
Correct Answer: A 🗳️
What is the significance of the "bias-variance trade-off" in machine learning?
- A. It indicates the trade-off between accuracy and precision.
- B. It represents the trade-off between underfitting and overfitting.
- C. It is not relevant in machine learning.
- D. It refers to the trade-off between the number of features and the model's complexity.
Correct Answer: B 🗳️
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