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SASInstitute A00-406 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Model Assessment and Deployment | 24-30% | - Apply model assessment principles - Evaluate and compare model performance - Deploy models into production - Select champion models |
| Topic 2: Data Sources | 30-36% | - Reduce dimensionality - Modify and prepare data - Perform variable selection - Explore and understand data - Create a project in Model Studio |
| Topic 3: Building Models | 40-46% | - Integrate custom code - Use model interpretability tools - Build decision trees and ensemble models - Build regression models - Build neural networks and SVM models - Understand supervised machine learning concepts |
SASInstitute SAS® Viya® Supervised Machine Learning Pipelines Sample Questions:
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 in a non-standard, proprietary format
- D. Data that is stored in a physical format
Correct Answer: A 🗳️
Which of the following best describes unstructured data?
- A. Data that is difficult to process and lacks a predefined structure
- B. Data with a clear schema
- C. Data that is organized in rows and columns
- D. Data stored in a relational database
Correct Answer: A 🗳️
When assessing a classification model, what is the confusion matrix used to measure?
- A. Precision and recall
- B. Model accuracy
- C. Model complexity
- D. Data distribution
Correct Answer: A 🗳️
Which algorithm is commonly used for decision-making tasks in classification models?
- A. K-Means
- B. Principal Component Analysis (PCA)
- C. Decision Trees
- D. Linear Regression
Correct Answer: C 🗳️
In natural language processing (NLP), what is a common preprocessing step for text data before building models?
- A. Standardization
- B. Principal Component Analysis (PCA)
- C. One-Hot Encoding
- D. Tokenization
Correct Answer: D 🗳️
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