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Instant Download SASInstitute : A00-406 Questions & Answers as PDF & Test Engine
- Exam Code: A00-406
- Exam Name: SAS® Viya® Supervised Machine Learning Pipelines
- Updated: Sep 04, 2026
- No. of Questions: 98 Questions and Answers
- Download Limit: Unlimited
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SASInstitute A00-406 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Building Models | 40-46% | - Model comparison and selection - Supervised model creation (decision trees, ensembles, SVM, neural networks) |
| Model Assessment and Deployment | 24-30% | - Assessing model performance (metrics, ROC curves, confusion matrices) - Deploying models into production |
| Data Sources | 30-36% | - Exploring and modifying data - Importing and preparing data - Dimensionality reduction and feature engineering |
SASInstitute SAS® Viya® Supervised Machine Learning Pipelines Sample Questions:
Question 1
What is the main advantage of ensemble methods in model building?
A. They require minimal data preprocessing
B. They combine multiple models to improve predictive performance
C. They produce simple and interpretable models
D. They work well with high-dimensional data
Question 2
Given the following properties for a neural network model, which statement is true regrading hidden units in the model? The following SAS program is submitted:
A. The number of hidden units is 26.
B. The number of hidden units is 50.
C. The number of hidden units is 1.
D. There are no hidden units in the model.
Question 3
What is the significance of the "bias-variance trade-off" in machine learning?
A. It indicates the trade-off between accuracy and precision.
B. It refers to the trade-off between the number of features and the model's complexity.
C. It represents the trade-off between underfitting and overfitting.
D. It is not relevant in machine learning.
Question 4
When deploying a machine learning model, what is "model drift"?
A. The process of feature extraction
B. A change in the distribution of the input data or target variable over time
C. A measure of feature importance
D. A sudden increase in the model's accuracy
Question 5
What is the main goal of data preprocessing in a machine learning pipeline?
A. To prepare the data for analysis and modeling
B. To visualize the data
C. To remove irrelevant features
D. To train the model
Solutions:
| Question 1 Answer: B | Question 2 Answer: A | Question 3 Answer: C | Question 4 Answer: B | Question 5 Answer: A |
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