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SASInstitute A00-406 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Machine Learning Pipelines in SAS Viya | - Model tuning and optimization
|
| Supervised Machine Learning Models | - Regression and classification models
|
| Model Evaluation and Deployment | - Model assessment metrics
|
| Data Preparation for Machine Learning | - Feature engineering
|
SASInstitute SAS® Viya® Supervised Machine Learning Pipelines Sample Questions:
When building a recommendation system, which type of filtering is based on the user's behavior and preferences?
- A. Singular Value Decomposition (SVD)
- B. Matrix factorization
- C. Collaborative filtering
- D. Content-based filtering
What is feature engineering in the context of machine learning pipelines?
- A. Applying the model to new data
- B. Testing the model's performance
- C. Creating new features from existing data
- D. Building a machine learning model from scratch
In model evaluation, what is the purpose of a ROC curve (Receiver Operating Characteristic)?
- A. To compare models' performance in terms of sensitivity and specificity
- B. To visualize data distribution
- C. To measure feature importance
- D. To evaluate the mean squared error of a model
What is the primary difference between supervised and unsupervised learning in model building?
- A. The amount of labeled data required
- B. The type of data used
- C. The use of feature engineering
- D. The presence or absence of a target variable
Which hyperparameter in a decision tree model controls the depth of the tree and helps prevent overfitting?
- A. Max features
- B. Max depth
- C. Min samples split
- D. Learning rate
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