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SASInstitute A00-406 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Sources | 30-36% | - Dimensionality reduction and feature engineering - Importing and preparing data - Exploring and modifying data |
| Topic 2: Building Models | 40-46% | - Model comparison and selection - Supervised model creation (decision trees, ensembles, SVM, neural networks) |
| Topic 3: Model Assessment and Deployment | 24-30% | - Deploying models into production - Assessing model performance (metrics, ROC curves, confusion matrices) |
SASInstitute SAS® Viya® Supervised Machine Learning Pipelines Sample Questions:
1. Which algorithm is commonly used for decision-making tasks in classification models?
A) Principal Component Analysis (PCA)
B) Decision Trees
C) Linear Regression
D) K-Means
2. What is the primary objective of model evaluation in the context of building predictive models?
A) Assessing the model's performance and accuracy
B) Cleaning the data
C) Discovering patterns in data
D) Visualizing data
3. What is the purpose of a "canary release" in the context of model deployment?
A) To assess data quality
B) To evaluate model accuracy
C) To create synthetic data
D) To deploy a new model version to a small subset of users or systems for testing
4. What is the primary difference between supervised and unsupervised learning in model building?
A) The use of feature engineering
B) The amount of labeled data required
C) The presence or absence of a target variable
D) The type of data used
5. In the context of model building, what is the purpose of hyperparameter tuning?
A) Training the model
B) Selecting the most important features
C) Visualizing data
D) Optimizing the model's hyperparameters for better performance
Solutions:
| Question # 1 Answer: B | Question # 2 Answer: A | Question # 3 Answer: D | Question # 4 Answer: C | Question # 5 Answer: D |
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