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SASInstitute A00-406 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Building Models | 40-46% | - Supervised model creation (decision trees, ensembles, SVM, neural networks) - Model comparison and selection |
| Topic 2: Model Assessment and Deployment | 24-30% | - Deploying models into production - Assessing model performance (metrics, ROC curves, confusion matrices) |
| Topic 3: Data Sources | 30-36% | - Importing and preparing data - Dimensionality reduction and feature engineering - Exploring and modifying data |
SASInstitute SAS® Viya® Supervised Machine Learning Pipelines Sample Questions:
1. What is the purpose of regularization techniques in model building, such as L1 and L2 regularization?
A) To speed up model training
B) To prevent overfitting and reduce model complexity
C) To add more features to the model
D) To increase model complexity
2. What is a data lake?
A) A data storage solution designed for high-speed data retrieval
B) A centralized repository for storing all structured and unstructured data at any scale
C) A specialized database for time-series data
D) A backup system for relational databases
3. Which of the following best describes unstructured data?
A) Data with a clear schema
B) Data stored in a relational database
C) Data that is organized in rows and columns
D) Data that is difficult to process and lacks a predefined structure
4. What is the primary purpose of a supervised machine learning pipeline in SAS Viya?
A) Data visualization
B) Data storage and retrieval
C) Model training and evaluation
D) Data preprocessing and cleaning
5. In reinforcement learning, what is the "reward signal"?
A) A numerical value that indicates the performance of an action taken by the agent
B) The accuracy of the model's predictions
C) The final prediction made by the model
D) A regularization parameter
Solutions:
| Question # 1 Answer: B | Question # 2 Answer: B | Question # 3 Answer: D | Question # 4 Answer: C | Question # 5 Answer: A |
