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SASInstitute A00-406 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Model Assessment and Deployment | 24-30% | - Evaluate and compare model performance - Apply model assessment principles - Select champion models - Deploy models into production |
| Topic 2: Data Sources | 30-36% | - Perform variable selection - Create a project in Model Studio - Explore and understand data - Reduce dimensionality - Modify and prepare data |
| Topic 3: Building Models | 40-46% | - Integrate custom code - Build decision trees and ensemble models - Build neural networks and SVM models - Use model interpretability tools - Understand supervised machine learning concepts - Build regression models |
SASInstitute SAS® Viya® Supervised Machine Learning Pipelines Sample Questions:
What does "data lineage" refer to in the context of data source management?
- A. The structure of a relational database
- B. The history of data transformation processes
- C. The physical location of data storage
- D. The security protocols for data access
Correct Answer: B 🗳️
What is the main goal of data preprocessing in a machine learning pipeline?
- A. To remove irrelevant features
- B. To visualize the data
- C. To prepare the data for analysis and modeling
- D. To train the model
Correct Answer: C 🗳️
What is metadata in the context of data sources?
- A. Data about data, providing information such as data source, structure, and context
- B. Data that is encrypted for security
- C. Data that is stored in a physical format
- D. Data that is in a non-standard, proprietary format
Correct Answer: A 🗳️
What is a common example of an external data source for an organization?
- A. Internal emails
- B. Customer surveys
- C. Intranet portals
- D. Employee databases
Correct Answer: B 🗳️
Which of the following is a common technique for handling missing data in a machine learning pipeline?
- A. Imputing missing values
- B. Replacing missing values with zeros
- C. Ignoring missing data
- D. Deleting rows with missing data
Correct Answer: A 🗳️
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