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SASInstitute A00-406 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Data Sources | 30-36% | - Importing and preparing data - Dimensionality reduction and feature engineering - Exploring and modifying data |
| 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 |
SASInstitute SAS® Viya® Supervised Machine Learning Pipelines Sample Questions:
1. What is the primary purpose of model documentation in the model deployment phase?
A) To assess data quality
B) To create synthetic data
C) To evaluate the model's accuracy
D) To provide information on the model's development, architecture, and usage
2. Which type of model is well-suited for solving classification problems when dealing with high- dimensional data, such as text?
A) K-Means Clustering
B) Random Forest
C) Linear Regression
D) Support Vector Machine (SVM)
3. In model assessment, what is the purpose of feature importance analysis?
A) To assess data quality
B) To visualize data distribution
C) To create synthetic features
D) To evaluate the significance of input features in making predictions
4. When deploying a machine learning model, what is meant by "model latency"?
A) The time it takes to build a model
B) The time it takes for the model to make predictions once deployed
C) The time it takes to create synthetic data
D) The time it takes to train a model
5. What is the purpose of an ROC curve (Receiver Operating Characteristic) in model assessment?
A) To compare a model's true positive rate with the false positive rate
B) To visualize data distribution
C) To evaluate regression models
D) To measure feature importance
Solutions:
| Question # 1 Answer: D | Question # 2 Answer: D | Question # 3 Answer: D | Question # 4 Answer: B | Question # 5 Answer: A |
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