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SASInstitute A00-406 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Data Sources | 30-36% | - Exploring and modifying data - Importing and preparing data - Dimensionality reduction and feature engineering |
| 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 |
SASInstitute SAS® Viya® Supervised Machine Learning Pipelines Sample Questions:
What is "model deployment" in the context of data science and machine learning?
- A. The process of data cleaning
- B. The process of selecting features
- C. The process of building a model
- D. Making the model available for use in real-world applications
Correct Answer: D 🗳️
What is the main advantage of ensemble learning methods, such as Random Forest, in a machine learning pipeline?
- A. They are not suitable for large datasets.
- B. They combine multiple models to improve predictive performance.
- C. They are simple and easy to interpret.
- D. They require minimal data preprocessing.
Correct Answer: B 🗳️
Which evaluation metric is commonly used for assessing the performance of a binary classification model?
- A. Accuracy
- B. R-squared
- C. Mean Absolute Error (MAE)
- D. Root Mean Squared Error (RMSE)
Correct Answer: A 🗳️
Which of the following is a common source for external data in the context of business analytics?
- A. Intranet databases
- B. CRM data
- C. Company financial reports
- D. Employee records
Correct Answer: C 🗳️
Which hyperparameter in a decision tree model controls the depth of the tree and helps prevent overfitting?
- A. Max depth
- B. Learning rate
- C. Min samples split
- D. Max features
Correct Answer: A 🗳️
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