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SASInstitute A00-406 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Sources | 30-36% | - Importing and preparing data - Exploring and modifying data - Dimensionality reduction and feature engineering |
| 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% | - Assessing model performance (metrics, ROC curves, confusion matrices) - Deploying models into production |
SASInstitute SAS® Viya® Supervised Machine Learning Pipelines Sample Questions:
1. Which type of data source typically stores structured data in a tabular format?
A) Relational databases
B) NoSQL databases
C) Text documents
D) APIs
2. Which of the following is a common source for external data in the context of business analytics?
A) Intranet databases
B) Employee records
C) CRM data
D) Company financial reports
3. In a machine learning pipeline, what is the purpose of cross-validation?
A) To train multiple models on different subsets of the data to assess generalization
B) To split the dataset into training and testing sets
C) To visualize the data distribution
D) To evaluate the model's performance on new data
4. In a supervised machine learning pipeline, what is the purpose of the test data set?
A) To preprocess the data
B) To train the machine learning model
C) To evaluate the model's predictions
D) To validate the model's performance
5. What does API stand for in the context of data sources?
A) Advanced Programming Integration
B) Application Programming Interface
C) Automated Program Integration
D) Application Program Interface
Solutions:
| Question # 1 Answer: A | Question # 2 Answer: D | Question # 3 Answer: A | Question # 4 Answer: D | Question # 5 Answer: B |
