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SASInstitute A00-406 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Model Assessment and Deployment | 24-30% | - Evaluate and compare model performance - Deploy models into production - Select champion models - Apply model assessment principles |
| Data Sources | 30-36% | - Modify and prepare data - Explore and understand data - Create a project in Model Studio - Reduce dimensionality - Perform variable selection |
| Building Models | 40-46% | - Integrate custom code - Build neural networks and SVM models - Use model interpretability tools - Build decision trees and ensemble models - Build regression models - Understand supervised machine learning concepts |
SASInstitute SAS® Viya® Supervised Machine Learning Pipelines Sample Questions:
Which type of model is typically used for time-series forecasting?
- A. Decision Trees
- B. AutoRegressive Integrated Moving Average (ARIMA)
- C. Logistic Regression
- D. K-Means Clustering
When building a recommendation system, what does "collaborative filtering" rely on?
- A. The popularity of items
- B. The characteristics of the items being recommended
- C. The past behavior or preferences of users
- D. Item-based clustering
In the context of model assessment, what does "bias" refer to?
- A. A measure of the model's precision
- B. The process of feature engineering
- C. The simplicity of the model
- D. A systematic error that causes a model to consistently overpredict
Which algorithm is commonly used for binary classification in machine learning pipelines, especially when dealing with imbalanced datasets?
- A. Support Vector Machine (SVM)
- B. Linear Regression
- C. Principal Component Analysis (PCA)
- D. K-Means Clustering
Which of the following metrics is commonly used to evaluate the performance of a binary classification model in a machine learning pipeline?
- A. Accuracy
- B. R-squared
- C. Root Mean Squared Error (RMSE)
- D. Mean Absolute Error (MAE)
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