SASInstitute A00-406 : SAS® Viya® Supervised Machine Learning Pipelines

A00-406 real exams

Exam Code: A00-406

Exam Name: SAS® Viya® Supervised Machine Learning Pipelines

Updated: Aug 02, 2026

Q & A: 98 Questions and Answers

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SASInstitute A00-406 Exam Syllabus Topics:

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

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

                                  1. What does "feature selection" refer to in the context of model building?

                                  A) The process of choosing the most relevant variables (features) for the model
                                  B) The creation of synthetic features from existing data
                                  C) The evaluation of model accuracy
                                  D) The visualization of data distribution


                                  2. What is "model retraining" in the context of model deployment?

                                  A) Periodically updating and improving a deployed model with new data
                                  B) The process of feature selection
                                  C) The process of data preprocessing
                                  D) The process of building the initial model


                                  3. What is the primary purpose of model deployment in the context of data science and machine learning?

                                  A) Data preprocessing
                                  B) Making the model available for use in real-world applications
                                  C) Model evaluation
                                  D) Model building


                                  4. What is feature engineering in the context of machine learning pipelines?

                                  A) Testing the model's performance
                                  B) Building a machine learning model from scratch
                                  C) Creating new features from existing data
                                  D) Applying the model to new data


                                  5. In the context of data integration, what does "data transformation" refer to?

                                  A) Extracting data from source systems
                                  B) Storing data in a centralized repository
                                  C) Backing up data for disaster recovery
                                  D) Converting and reshaping data to match the target schema


                                  Solutions:

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

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