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SASInstitute A00-406 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Data Sources | 30-36% | - Explore and understand data - Modify and prepare data - Create a project in Model Studio - Perform variable selection - Reduce dimensionality |
| Building Models | 40-46% | - Build regression models - Integrate custom code - Build neural networks and SVM models - Build decision trees and ensemble models - Use model interpretability tools - Understand supervised machine learning concepts |
| Model Assessment and Deployment | 24-30% | - Apply model assessment principles - Select champion models - Deploy models into production - Evaluate and compare model performance |
SASInstitute SAS® Viya® Supervised Machine Learning Pipelines Sample Questions:
1. Which algorithm is commonly used for decision-making tasks in classification models?
A) Decision Trees
B) K-Means
C) Principal Component Analysis (PCA)
D) Linear Regression
2. When building a recommendation system, what does "collaborative filtering" rely on?
A) The popularity of items
B) The past behavior or preferences of users
C) The characteristics of the items being recommended
D) Item-based clustering
3. When deploying a model, what is "model explainability"?
A) The process of data preprocessing
B) The capability to interpret and understand the model's decisions and predictions
C) The simplicity of the model
D) The time it takes to make predictions
4. Which type of data source typically stores structured data in a tabular format?
A) Relational databases
B) NoSQL databases
C) APIs
D) Text documents
5. In reinforcement learning, what is the agent's objective?
A) To maximize a cumulative reward over time
B) To make predictions
C) To learn from labeled data
D) To generate synthetic data
Solutions:
| Question # 1 Answer: A | Question # 2 Answer: B | Question # 3 Answer: B | Question # 4 Answer: A | Question # 5 Answer: A |



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