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SASInstitute A00-406 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Building Models | 40-46% | - Model comparison and selection - Supervised model creation (decision trees, ensembles, SVM, neural networks) |
| Model Assessment and Deployment | 24-30% | - Assessing model performance (metrics, ROC curves, confusion matrices) - Deploying models into production |
| Data Sources | 30-36% | - Exploring and modifying data - Importing and preparing data - Dimensionality reduction and feature engineering |
SASInstitute SAS® Viya® Supervised Machine Learning Pipelines Sample Questions:
1. In the context of data integration, what does "data transformation" refer to?
A) Backing up data for disaster recovery
B) Storing data in a centralized repository
C) Converting and reshaping data to match the target schema
D) Extracting data from source systems
2. What is the main advantage of using a RESTful API (Representational State Transfer) as a data source?
A) Support for complex data structures
B) High security features
C) Real-time data processing
D) Simple and standardized communication
3. What is "model deployment" in the context of data science and machine learning?
A) The process of selecting features
B) Making the model available for use in real-world applications
C) The process of building a model
D) The process of data cleaning
4. Which of the following is a common source for external data in the context of business analytics?
A) CRM data
B) Intranet databases
C) Company financial reports
D) Employee records
5. What is the main purpose of feature engineering in model building?
A) Data preprocessing
B) Creating new features or transforming existing ones to improve model performance
C) Data visualization
D) Model evaluation
Solutions:
| Question # 1 Answer: C | Question # 2 Answer: D | Question # 3 Answer: B | Question # 4 Answer: C | Question # 5 Answer: B |






