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SASInstitute A00-406 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Sources | 30-36% | - Dimensionality reduction and feature engineering - Importing and preparing data - Exploring and modifying data |
| Topic 2: Building Models | 40-46% | - Model comparison and selection - Supervised model creation (decision trees, ensembles, SVM, neural networks) |
| Topic 3: Model Assessment and Deployment | 24-30% | - Assessing model performance (metrics, ROC curves, confusion matrices) - Deploying models into production |
SASInstitute SAS® Viya® Supervised Machine Learning Pipelines Sample Questions:
What is metadata in the context of data sources?
- A. Data that is encrypted for security
- B. Data that is in a non-standard, proprietary format
- C. Data that is stored in a physical format
- D. Data about data, providing information such as data source, structure, and context
Which technique is commonly used for feature scaling or normalization in machine learning pipelines?
- A. Standardization
- B. One-Hot Encoding
- C. Principal Component Analysis (PCA)
- D. Decision Trees
Which hyperparameter in a decision tree model controls the depth of the tree and helps prevent overfitting?
- A. Max features
- B. Min samples split
- C. Learning rate
- D. Max depth
What is the purpose of regularization techniques in model building, such as L1 and L2 regularization?
- A. To prevent overfitting and reduce model complexity
- B. To increase model complexity
- C. To add more features to the model
- D. To speed up model training
Which of the following is NOT a common data format for exchanging data between systems?
- A. CSV (Comma-Separated Values)
- B. SQL (Structured Query Language)
- C. JSON (JavaScript Object Notation)
- D. HTML (Hypertext Markup Language)
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Adonis 
Matt 
Ternence 
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