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SASInstitute A00-406 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Topic 1: Machine Learning Pipelines in SAS Viya | - Pipeline construction
|
| Topic 2: Model Evaluation and Deployment | - Model assessment metrics
|
| Topic 3: Supervised Machine Learning Models | - Model selection techniques
|
| Topic 4: Data Preparation for Machine Learning | - Feature engineering
|
SASInstitute SAS® Viya® Supervised Machine Learning Pipelines Sample Questions:
1. In the context of model assessment, what does "bias" refer to?
A) A systematic error that causes a model to consistently overpredict
B) The simplicity of the model
C) The process of feature engineering
D) A measure of the model's precision
2. In machine learning, what does "overfitting" refer to?
A) A model that has too much complexity and fits the training data too closely
B) A model that is unable to make predictions
C) A model that performs well on new, unseen data
D) A model that is undertrained and has high bias
3. What is the purpose of data profiling in data source management?
A) To execute data queries
B) To assess the quality and characteristics of data
C) To optimize data storage
D) To create data visualizations
4. Which SAS Viya component is typically used for deploying and monitoring machine learning models in production?
A) SAS Enterprise Miner
B) SAS Visual Analytics
C) SAS Model Manager
D) SAS Data Loader
5. Which type of model is well-suited for solving classification problems when dealing with high- dimensional data, such as text?
A) Support Vector Machine (SVM)
B) Random Forest
C) K-Means Clustering
D) Linear Regression
Solutions:
| Question # 1 Answer: A | Question # 2 Answer: A | Question # 3 Answer: B | Question # 4 Answer: C | Question # 5 Answer: A |
