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SASInstitute A00-406 Exam Syllabus Topics:
| Section | Objectives |
| Model Evaluation and Deployment | - Model assessment metrics
- 1. ROC and lift charts
- 2. Accuracy, precision, recall
- Model deployment
- 1. Model governance
- 2. Scoring models in SAS Viya
|
| Supervised Machine Learning Models | - Regression and classification models
- 1. Linear and logistic regression
- 2. Tree-based models
- Model selection techniques
- 1. Cross-validation
- 2. Performance comparison
|
| Machine Learning Pipelines in SAS Viya | - Pipeline construction
- 1. Node-based pipeline design
- 2. Model Studio workflows
- Model tuning and optimization
- 1. Automated machine learning (AutoML)
- 2. Hyperparameter tuning
|
| Data Preparation for Machine Learning | - Data cleaning and preprocessing
- 1. Outlier detection and treatment
- 2. Handling missing values
- Feature engineering
- 1. Encoding categorical variables
- 2. Variable transformation
|
SASInstitute SAS® Viya® Supervised Machine Learning Pipelines Sample Questions:
1. In natural language processing, what does "stemming" involve?
A) Grouping similar words together based on their meanings
B) Converting text to numbers for model input
C) Reducing words to their base or root form
D) Creating new words to improve model performance
2. What is a common example of an external data source for an organization?
A) Internal emails
B) Customer surveys
C) Employee databases
D) Intranet portals
3. What is the purpose of data profiling in data source management?
A) To execute data queries
B) To create data visualizations
C) To optimize data storage
D) To assess the quality and characteristics of data
4. In the context of data sources, what is meant by data versioning?
A) Encrypting data to protect against unauthorized access
B) Keeping track of different versions or changes to data over time
C) Compressing data to reduce storage space
D) Storing multiple copies of the same data to increase redundancy
5. What is the primary purpose of model documentation in the model deployment phase?
A) To evaluate the model's accuracy
B) To create synthetic data
C) To assess data quality
D) To provide information on the model's development, architecture, and usage
Solutions:
Question # 1 Answer: C | Question # 2 Answer: B | Question # 3 Answer: D | Question # 4 Answer: B | Question # 5 Answer: D |