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SASInstitute A00-406 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Data Sources | 30-36% | - Perform variable selection - Explore and understand data - Modify and prepare data - Reduce dimensionality - Create a project in Model Studio |
| Building Models | 40-46% | - Build regression models - Understand supervised machine learning concepts - Integrate custom code - Build decision trees and ensemble models - Build neural networks and SVM models - Use model interpretability tools |
| Model Assessment and Deployment | 24-30% | - Evaluate and compare model performance - Select champion models - Apply model assessment principles - Deploy models into production |
SASInstitute SAS® Viya® Supervised Machine Learning Pipelines Sample Questions:
1. A project has been created and a pipeline has been run in Model Studio.
Which project setting can you edit?
A) Event-based Sampling proportions
B) Partition Data percentages
C) Rules for model comparison statistic
D) Advisor Options for missing values
2. When deploying a machine learning model, what is meant by "model latency"?
A) The time it takes to build a model
B) The time it takes to train a model
C) The time it takes to create synthetic data
D) The time it takes for the model to make predictions once deployed
3. What is the main advantage of ensemble learning methods, such as Random Forest, in a machine learning pipeline?
A) They are not suitable for large datasets.
B) They are simple and easy to interpret.
C) They require minimal data preprocessing.
D) They combine multiple models to improve predictive performance.
4. What is the primary role of a data warehouse in an organization?
A) Data transformation and cleaning
B) Data exploration and visualization
C) Long-term data storage and analysis
D) Real-time data analysis
5. What is "model deployment" in the context of data science and machine learning?
A) The process of building a model
B) Making the model available for use in real-world applications
C) The process of selecting features
D) The process of data cleaning
Solutions:
| Question # 1 Answer: C | Question # 2 Answer: D | Question # 3 Answer: D | Question # 4 Answer: C | Question # 5 Answer: B |
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