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


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  • Exam Code: A00-406
  • Exam Name: SAS® Viya® Supervised Machine Learning Pipelines
  • Total Questions: 98

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SASInstitute A00-406 Exam Syllabus Topics:

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

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

                                  1. What is the main purpose of feature engineering in model building?

                                  A) Model evaluation
                                  B) Creating new features or transforming existing ones to improve model performance
                                  C) Data visualization
                                  D) Data preprocessing


                                  2. What is a common example of an external data source for an organization?

                                  A) Internal emails
                                  B) Customer surveys
                                  C) Employee databases
                                  D) Intranet portals


                                  3. What is the primary goal of A/B testing in the context of model deployment?

                                  A) To evaluate the model's accuracy
                                  B) To compare two different versions of a model or strategy to determine which performs better
                                  C) To create synthetic data
                                  D) To assess data quality


                                  4. What is the main goal of data preprocessing in a machine learning pipeline?

                                  A) To visualize the data
                                  B) To prepare the data for analysis and modeling
                                  C) To remove irrelevant features
                                  D) To train the model


                                  5. What is the primary role of a data warehouse in an organization?

                                  A) Data exploration and visualization
                                  B) Real-time data analysis
                                  C) Long-term data storage and analysis
                                  D) Data transformation and cleaning


                                  Solutions:

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

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