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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: Building Models | 40-46% | - Use model interpretability tools - Build neural networks and SVM models - Build regression models - Build decision trees and ensemble models - Integrate custom code - Understand supervised machine learning concepts |
| Topic 3: Data Sources | 30-36% | - Modify and prepare data - Perform variable selection - Reduce dimensionality - Explore and understand data - Create a project in Model Studio |
SASInstitute SAS® Viya® Supervised Machine Learning Pipelines Sample Questions:
Given the following properties for a neural network model, which statement is true regrading hidden units in the model? The following SAS program is submitted:
- A. The number of hidden units is 50.
- B. The number of hidden units is 1.
- C. There are no hidden units in the model.
- D. The number of hidden units is 26.
Correct Answer: D 🗳️
When building a recommendation system, which type of filtering is based on the user's behavior and preferences?
- A. Matrix factorization
- B. Content-based filtering
- C. Singular Value Decomposition (SVD)
- D. Collaborative filtering
Correct Answer: D 🗳️
When deploying a model, what is "model explainability"?
- A. The capability to interpret and understand the model's decisions and predictions
- B. The process of data preprocessing
- C. The simplicity of the model
- D. The time it takes to make predictions
Correct Answer: A 🗳️
What is a common example of an external data source for an organization?
- A. Customer surveys
- B. Internal emails
- C. Employee databases
- D. Intranet portals
Correct Answer: A 🗳️
What is the primary purpose of model deployment in the context of data science and machine learning?
- A. Making the model available for use in real-world applications
- B. Model building
- C. Model evaluation
- D. Data preprocessing
Correct Answer: A 🗳️
