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SASInstitute A00-406 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Model Assessment and Deployment | 24-30% | - Select champion models - Evaluate and compare model performance - Apply model assessment principles - Deploy models into production |
| Data Sources | 30-36% | - Modify and prepare data - Explore and understand data - Perform variable selection - Reduce dimensionality - Create a project in Model Studio |
| Building Models | 40-46% | - Build decision trees and ensemble models - Use model interpretability tools - Integrate custom code - Understand supervised machine learning concepts - Build neural networks and SVM models - Build regression models |
SASInstitute SAS® Viya® Supervised Machine Learning Pipelines Sample Questions:
What is the primary objective of model evaluation in the context of building predictive models?
- A. Cleaning the data
- B. Visualizing data
- C. Assessing the model's performance and accuracy
- D. Discovering patterns in data
Correct Answer: C 🗳️
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. Rules for model comparison statistic
- C. Event-based Sampling proportions
- D. Advisor Options for missing values
Correct Answer: B 🗳️
What is the primary goal of A/B testing in the context of model deployment?
- A. To assess data quality
- B. To create synthetic data
- C. To evaluate the model's accuracy
- D. To compare two different versions of a model or strategy to determine which performs better
Correct Answer: D 🗳️
What is the purpose of a confusion matrix in the context of classification models?
- A. To visualize the data
- B. To summarize the distribution of target variables
- C. To evaluate model performance, especially for binary classification
- D. To compute the mean squared error
Correct Answer: C 🗳️
What is "model reevaluation" in the model deployment phase?
- A. The process of selecting features
- B. The evaluation of data distribution
- C. The process of data preprocessing
- D. The periodic assessment of a deployed model's performance and potential retraining
Correct Answer: D 🗳️
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