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SASInstitute A00-406 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Topic 1: Model Evaluation and Deployment | - Model deployment
|
| Topic 2: Supervised Machine Learning Models | - Regression and classification models
|
| Topic 3: Machine Learning Pipelines in SAS Viya | - Pipeline construction
|
| Topic 4: Data Preparation for Machine Learning | - Data cleaning and preprocessing
|
SASInstitute SAS® Viya® Supervised Machine Learning Pipelines Sample Questions:
1. In the context of model deployment, what is "model compliance"?
A) The model's simplicity
B) The process of feature selection
C) The model's efficiency
D) The degree to which the model adheres to regulatory or ethical guidelines
2. What is feature engineering in the context of machine learning pipelines?
A) Testing the model's performance
B) Creating new features from existing data
C) Building a machine learning model from scratch
D) Applying the model to new data
3. What does "feature selection" refer to in the context of model building?
A) The creation of synthetic features from existing data
B) The process of choosing the most relevant variables (features) for the model
C) The visualization of data distribution
D) The evaluation of model accuracy
4. Which SAS Viya component is typically used for deploying and monitoring machine learning models in production?
A) SAS Enterprise Miner
B) SAS Data Loader
C) SAS Visual Analytics
D) SAS Model Manager
5. When deploying a machine learning model, what is "model drift"?
A) A sudden increase in the model's accuracy
B) A change in the distribution of the input data or target variable over time
C) The process of feature extraction
D) A measure of feature importance
Solutions:
| Question # 1 Answer: D | Question # 2 Answer: B | Question # 3 Answer: B | Question # 4 Answer: D | Question # 5 Answer: B |
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