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SASInstitute SAS® Viya® Supervised Machine Learning Pipelines : A00-406
- Exam Code: A00-406
- Exam Name: SAS® Viya® Supervised Machine Learning Pipelines
- Updated: Oct 01, 2026
- Q & A: 98 Questions and Answers
About SASInstitute A00-406 Exam
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SASInstitute A00-406 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Building Models | 40-46% | - Supervised model creation (decision trees, ensembles, SVM, neural networks) - Model comparison and selection |
| Topic 2: Model Assessment and Deployment | 24-30% | - Assessing model performance (metrics, ROC curves, confusion matrices) - Deploying models into production |
| Topic 3: Data Sources | 30-36% | - Exploring and modifying data - Dimensionality reduction and feature engineering - Importing and preparing data |
SASInstitute SAS® Viya® Supervised Machine Learning Pipelines Sample Questions:
Question #1
What is the primary objective of model evaluation in the context of building predictive models?
- A. Discovering patterns in data
- B. Visualizing data
- C. Cleaning the data
- D. Assessing the model's performance and accuracy
Correct Answer: D 🗳️
Question #2
What is the primary purpose of model deployment in the context of data science and machine learning?
- A. Making the model available for use in real-world applications
- B. Model building
- C. Model evaluation
- D. Data preprocessing
Correct Answer: A 🗳️
Question #3
When deploying a machine learning model, what is "model drift"?
- A. A measure of feature importance
- B. A sudden increase in the model's accuracy
- C. The process of feature extraction
- D. A change in the distribution of the input data or target variable over time
Correct Answer: D 🗳️
Question #4
Which evaluation metric is commonly used for assessing the performance of a regression model?
- A. Confusion Matrix
- B. F1 Score
- C. Precision
- D. Mean Absolute Error (MAE)
Correct Answer: D 🗳️
Question #5
In model evaluation, what is the purpose of a ROC curve (Receiver Operating Characteristic)?
- A. To compare models' performance in terms of sensitivity and specificity
- B. To visualize data distribution
- C. To measure feature importance
- D. To evaluate the mean squared error of a model
Correct Answer: A 🗳️
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