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SASInstitute A00-406 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Model Assessment and Deployment | 24-30% | - Select champion models - Evaluate and compare model performance - Deploy models into production - Apply model assessment principles |
| Topic 2: Data Sources | 30-36% | - Modify and prepare data - Create a project in Model Studio - Explore and understand data - Reduce dimensionality - Perform variable selection |
| Topic 3: Building Models | 40-46% | - Build regression models - Build decision trees and ensemble models - Use model interpretability tools - Build neural networks and SVM models - Integrate custom code - Understand supervised machine learning concepts |
SASInstitute SAS® Viya® Supervised Machine Learning Pipelines Sample Questions:
What is the purpose of cross-validation in model building and evaluation?
- A. Reducing the dataset size
- B. Generating synthetic data
- C. Splitting the dataset into training and testing sets
- D. Assessing the model's generalization performance
Correct Answer: D 🗳️
What is the primary difference between supervised and unsupervised learning in model building?
- A. The presence or absence of a target variable
- B. The use of feature engineering
- C. The type of data used
- D. The amount of labeled data required
Correct Answer: A 🗳️
What does the term "bias" in machine learning refer to?
- A. The simplicity of a model
- B. Systematic errors that cause a model to consistently underpredict or overpredict
- C. A model's inability to generalize to new data
- D. The overall accuracy of a model
Correct Answer: B 🗳️
What is the purpose of hyperparameter tuning in a machine learning pipeline?
- A. To evaluate the model's predictions
- B. To optimize the model's hyperparameters for better performance
- C. To train the model
- D. To select the most important features
Correct Answer: B 🗳️
A project has been created and a pipeline has been run in Model Studio.
Which project setting can you edit?
- A. Partition Data percentages
- B. Event-based Sampling proportions
- C. Rules for model comparison statistic
- D. Advisor Options for missing values
Correct Answer: C 🗳️
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