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SASInstitute A00-406 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Topic 1: Model Evaluation and Deployment | - Model assessment metrics
|
| Topic 2: Machine Learning Pipelines in SAS Viya | - Model tuning and optimization
|
| Topic 3: Data Preparation for Machine Learning | - Data cleaning and preprocessing
|
| Topic 4: Supervised Machine Learning Models | - Model selection techniques
|
SASInstitute SAS® Viya® Supervised Machine Learning Pipelines Sample Questions:
1. Which type of model is typically used for time-series forecasting?
A) AutoRegressive Integrated Moving Average (ARIMA)
B) Decision Trees
C) Logistic Regression
D) K-Means Clustering
2. What is the main goal of data preprocessing in a machine learning pipeline?
A) To prepare the data for analysis and modeling
B) To remove irrelevant features
C) To visualize the data
D) To train the model
3. What is the main advantage of ensemble methods in model building?
A) They produce simple and interpretable models
B) They combine multiple models to improve predictive performance
C) They require minimal data preprocessing
D) They work well with high-dimensional data
4. Which type of model is well-suited for solving classification problems when dealing with high- dimensional data, such as text?
A) Linear Regression
B) Support Vector Machine (SVM)
C) K-Means Clustering
D) Random Forest
5. Which hyperparameter in a decision tree model controls the depth of the tree and helps prevent overfitting?
A) Min samples split
B) Max features
C) Max depth
D) Learning rate
Solutions:
| Question # 1 Answer: A | Question # 2 Answer: A | Question # 3 Answer: B | Question # 4 Answer: B | Question # 5 Answer: C |
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