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SASInstitute A00-406 Exam Syllabus Topics:

SectionObjectives
Topic 1: Machine Learning Pipelines in SAS Viya- Pipeline construction
  • 1. Node-based pipeline design
    • 2. Model Studio workflows
      - Model tuning and optimization
      • 1. Hyperparameter tuning
        • 2. Automated machine learning (AutoML)
          Topic 2: 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
                  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: 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

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

                                  Which of the following is an example of a NoSQL database that is commonly used to store unstructured data?

                                  • A. Microsoft SQL Server
                                  • B. MySQL
                                  • C. MongoDB
                                  • D. Oracle Database
                                  Reveal Solution  Discussion  0

                                  Correct Answer: C  🗳️

                                  In model assessment, what is the purpose of feature importance analysis?

                                  • A. To create synthetic features
                                  • B. To assess data quality
                                  • C. To visualize data distribution
                                  • D. To evaluate the significance of input features in making predictions
                                  Reveal Solution  Discussion  0

                                  Correct Answer: D  🗳️

                                  What does "data lineage" refer to in the context of data source management?

                                  • A. The structure of a relational database
                                  • B. The history of data transformation processes
                                  • C. The security protocols for data access
                                  • D. The physical location of data storage
                                  Reveal Solution  Discussion  0

                                  Correct Answer: B  🗳️

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

                                  • A. Data exploration and visualization
                                  • B. Long-term data storage and analysis
                                  • C. Real-time data analysis
                                  • D. Data transformation and cleaning
                                  Reveal Solution  Discussion  0

                                  Correct Answer: B  🗳️

                                  Which feature extraction method can take both interval variables and class variables as inputs?

                                  • A. Singular value decomposition
                                  • B. Autoencoder
                                  • C. Principal component analysis
                                  • D. Robust PCA
                                  Reveal Solution  Discussion  0

                                  Correct Answer: B  🗳️

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