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SASInstitute A00-406 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Building Models | 40-46% | - Supervised model creation (decision trees, ensembles, SVM, neural networks) - Model comparison and selection |
| Model Assessment and Deployment | 24-30% | - Assessing model performance (metrics, ROC curves, confusion matrices) - Deploying models into production |
| Data Sources | 30-36% | - Importing and preparing data - Exploring and modifying data - Dimensionality reduction and feature engineering |
SASInstitute SAS® Viya® Supervised Machine Learning Pipelines Sample Questions:
When assessing a classification model, what is the confusion matrix used to measure?
- A. Precision and recall
- B. Model complexity
- C. Data distribution
- D. Model accuracy
Correct Answer: A 🗳️
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
Correct Answer: B 🗳️
Which of the following metrics is commonly used to evaluate the performance of a binary classification model in a machine learning pipeline?
- A. R-squared
- B. Accuracy
- C. Root Mean Squared Error (RMSE)
- D. Mean Absolute Error (MAE)
Correct Answer: B 🗳️
In the context of data integration, what does "data transformation" refer to?
- A. Backing up data for disaster recovery
- B. Extracting data from source systems
- C. Storing data in a centralized repository
- D. Converting and reshaping data to match the target schema
Correct Answer: D 🗳️
What is the primary purpose of model deployment in the context of data science and machine learning?
- A. Model building
- B. Making the model available for use in real-world applications
- C. Data preprocessing
- D. Model evaluation
Correct Answer: B 🗳️




