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SASInstitute A00-406 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Building Models | 40-46% | - Supervised model creation (decision trees, ensembles, SVM, neural networks) - Model comparison and selection |
| Topic 2: Data Sources | 30-36% | - Dimensionality reduction and feature engineering - Exploring and modifying data - Importing and preparing data |
| Topic 3: 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 a supervised machine learning pipeline in SAS Viya?
A) Data visualization
B) Data storage and retrieval
C) Data preprocessing and cleaning
D) Model training and evaluation
2. What is the primary goal of A/B testing in the context of model deployment?
A) To create synthetic data
B) To evaluate the model's accuracy
C) To assess data quality
D) To compare two different versions of a model or strategy to determine which performs better
3. What is the primary difference between supervised and unsupervised learning in model building?
A) The type of data used
B) The amount of labeled data required
C) The presence or absence of a target variable
D) The use of feature engineering
4. What is the primary role of a loss function in model training?
A) To assess the accuracy of the model
B) To measure the difference between predicted and actual values
C) To visualize the data
D) To maximize the model's performance
5. What is the purpose of cross-validation in model building and evaluation?
A) Reducing the dataset size
B) Splitting the dataset into training and testing sets
C) Assessing the model's generalization performance
D) Generating synthetic data
Solutions:
| Question # 1 Answer: D | Question # 2 Answer: D | Question # 3 Answer: C | Question # 4 Answer: B | Question # 5 Answer: C |
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