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SASInstitute A00-406 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Model Assessment and Deployment | 24-30% | - Deploying models into production - Assessing model performance (metrics, ROC curves, confusion matrices) |
| Building Models | 40-46% | - Model comparison and selection - Supervised model creation (decision trees, ensembles, SVM, neural networks) |
| Data Sources | 30-36% | - Dimensionality reduction and feature engineering - Exploring and modifying data - Importing and preparing data |
SASInstitute SAS® Viya® Supervised Machine Learning Pipelines Sample Questions:
1. Which data source typically provides access to real-time financial market data?
A) Weather stations
B) Stock market APIs
C) Social media platforms
D) Online news websites
2. In reinforcement learning, what is the agent's objective?
A) To make predictions
B) To maximize a cumulative reward over time
C) To generate synthetic data
D) To learn from labeled data
3. What is the primary purpose of model deployment in the context of data science and machine learning?
A) Data preprocessing
B) Making the model available for use in real-world applications
C) Model building
D) Model evaluation
4. In the context of data integration, what does "data transformation" refer to?
A) Extracting data from source systems
B) Converting and reshaping data to match the target schema
C) Storing data in a centralized repository
D) Backing up data for disaster recovery
5. Which data source allows for real-time data streaming and processing?
A) IoT devices
B) Data warehouses
C) Static data files
D) Cloud storage
Solutions:
| Question # 1 Answer: B | Question # 2 Answer: B | Question # 3 Answer: B | Question # 4 Answer: B | Question # 5 Answer: A |




