PDF Version DemoSASInstitute SAS Certified Specialist A00-406 SAS® Viya® Supervised Machine Learning Pipelines Exams
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SASInstitute A00-406 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Model Assessment and Deployment | 24-30% | - Select champion models - Deploy models into production - Evaluate and compare model performance - Apply model assessment principles |
| Topic 2: Data Sources | 30-36% | - Modify and prepare data - Explore and understand data - Reduce dimensionality - Create a project in Model Studio - Perform variable selection |
| Topic 3: Building Models | 40-46% | - Build neural networks and SVM models - Build decision trees and ensemble models - Use model interpretability tools - Build regression models - Understand supervised machine learning concepts - Integrate custom code |
SASInstitute SAS® Viya® Supervised Machine Learning Pipelines Sample Questions:
Question #1
When building a deep learning neural network, what is the purpose of the activation function in each neuron?
A. To control the number of hidden layers
B. To initialize the model
C. To define the learning rate
D. To introduce non-linearity
Question #2
What is the primary objective of model validation during the model assessment phase?
A. To ensure the model generalizes well to new, unseen data
B. To assess the accuracy of the model
C. To build a model from scratch
D. To create synthetic data
Question #3
Which technique is used for feature selection in a machine learning pipeline when dealing with a large number of features?
A. Regularization
B. One-Hot Encoding
C. Principal Component Analysis (PCA)
D. Naive Bayes
Question #4
What is the purpose of an ROC curve (Receiver Operating Characteristic) in model assessment?
A. To visualize data distribution
B. To measure feature importance
C. To evaluate regression models
D. To compare a model's true positive rate with the false positive rate
Question #5
Which type of model is commonly used for anomaly detection in datasets?
A. Clustering Models
B. Linear Regression
C. Decision Trees
D. Principal Component Analysis (PCA)
Solutions:
| Question #1 Correct Answer: D | Question #2 Correct Answer: A | Question #3 Correct Answer: A | Question #4 Correct Answer: D | Question #5 Correct Answer: A |
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