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SASInstitute A00-406 real answers - SAS® Viya® Supervised Machine Learning Pipelines

A00-406
  • Exam Code: A00-406
  • Exam Name: SAS® Viya® Supervised Machine Learning Pipelines
  • Updated: Aug 01, 2026
  • Q & A: 98 Questions and Answers
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SASInstitute A00-406 Exam Syllabus Topics:

SectionObjectives
Topic 1: Supervised Machine Learning Models- Model selection techniques
  • 1. Cross-validation
    • 2. Performance comparison
      - Regression and classification models
      • 1. Linear and logistic regression
        • 2. Tree-based models
          Topic 2: Data Preparation for Machine Learning- Feature engineering
          • 1. Variable transformation
            • 2. Encoding categorical variables
              - Data cleaning and preprocessing
              • 1. Outlier detection and treatment
                • 2. Handling missing values
                  Topic 3: Model Evaluation and Deployment- Model deployment
                  • 1. Model governance
                    • 2. Scoring models in SAS Viya
                      - Model assessment metrics
                      • 1. ROC and lift charts
                        • 2. Accuracy, precision, recall
                          Topic 4: Machine Learning Pipelines in SAS Viya- Pipeline construction
                          • 1. Node-based pipeline design
                            • 2. Model Studio workflows
                              - Model tuning and optimization
                              • 1. Automated machine learning (AutoML)
                                • 2. Hyperparameter tuning

                                  SASInstitute SAS® Viya® Supervised Machine Learning Pipelines Sample Questions:

                                  1. In reinforcement learning, what is the agent's objective?

                                  A) To maximize a cumulative reward over time
                                  B) To make predictions
                                  C) To learn from labeled data
                                  D) To generate synthetic data


                                  2. In a supervised learning pipeline, what is the role of the training data set?

                                  A) To validate the model's performance
                                  B) To evaluate the model's predictions
                                  C) To test the model's generalization capability
                                  D) To train the machine learning model


                                  3. 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) There are no hidden units in the model.
                                  B) The number of hidden units is 50.
                                  C) The number of hidden units is 1.
                                  D) The number of hidden units is 26.


                                  4. What is the significance of the "bias-variance trade-off" in machine learning?

                                  A) It is not relevant in machine learning.
                                  B) It represents the trade-off between underfitting and overfitting.
                                  C) It refers to the trade-off between the number of features and the model's complexity.
                                  D) It indicates the trade-off between accuracy and precision.


                                  5. What does the term "bagging" refer to in ensemble learning?

                                  A) A form of dimensionality reduction
                                  B) The process of combining multiple identical models to reduce variance
                                  C) A technique that reduces model complexity
                                  D) A type of feature extraction


                                  Solutions:

                                  Question # 1
                                  Answer: A
                                  Question # 2
                                  Answer: D
                                  Question # 3
                                  Answer: D
                                  Question # 4
                                  Answer: B
                                  Question # 5
                                  Answer: B

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