Welcome to your one-stop solution for all the information you need to excel in the SAS Viya Forecasting and Optimization (A00-407) Certification exam. This page provides an in-depth overview of the SAS A00-407 Exam Summary, Syllabus Topics, and Sample Questions, designed to lay the foundation for your exam preparation. We aim to help you achieve your SAS Certified Specialist - Forecasting and Optimization Using SAS Viya certification goals seamlessly. Our detailed syllabus outlines each topic covered in the exam, ensuring you focus on the areas that matter most. With our sample questions and practice exams, you can gauge your readiness and boost your confidence to take on the SAS Viya Forecasting and Optimization exam.
Why SAS Viya Forecasting and Optimization Certification Matters
The SAS A00-407 exam is globally recognized for validating your knowledge and skills. With the SAS Certified Specialist - Forecasting and Optimization Using SAS Viya credential, you stand out in a competitive job market and demonstrate your expertise to make significant contributions within your organization. The SAS Viya Forecasting and Optimization Certification exam will test your proficiency in the various syllabus topics.
SAS A00-407 Exam Summary:
| Exam Name | SAS Viya Forecasting and Optimization |
| Exam Code | A00-407 |
| Exam Duration | 90 minutes |
| Exam Questions | 50 |
| Passing Score | 68% |
| Exam Price | $180 (USD) |
| Books / Training |
Forecasting Using Model Studio in SAS Viya Optimization Concepts for Data Science and Artificial Intelligence Applied Data Mining for Forecasting Demand-Driven Forecasting |
| Exam Registration | Pearson VUE |
| Sample Questions | SAS Viya Forecasting and Optimization Certification Sample Question |
| Practice Exam | SAS Viya Forecasting and Optimization Certification Practice Exam |
SAS A00-407 Exam Syllabus Topics:
| Objective | Details |
|---|---|
Data Visualization (15%-20%) |
|
| Create project and load data |
- Create a Forecasting project (define variable roles) - Load data from various sources - Use Data tab functionality |
| Visualize data using attribute variables |
- Load Attributes table - Identify scenarios in which attribute variable are useful in visualizing data - Create a Visualization using Attribute Variables |
Pipeline Modeling (25%-30%) |
|
| Model using a pipeline |
- Auto-forecast using a pipeline - Build and run a custom pipeline - Given a scenario select and use appropriate pipeline template - Visualize the forecasts |
| Determine the champion models |
- Compare models within a pipeline - Recognize and interpret the model family of the champion model - Define the role of accuracy statistics in pipeline comparison - Select the champion model for the project - Explore the champion model |
| Judge model accuracy using accuracy statistics |
- Define and calculate MAPE, MAE, RMSE Adaptive learning - Given a scenario determine when is best appropriate to use MAPE, MAE or RMSE - Use a holdout sample to do honest assessment |
Hierarchical Forecasting (15%-20%) |
|
| Generate a forecast using data with a hierarchical structure |
- Generate a hierarchical forecast with default functionality - Improve the fit of a forecast by adding combined models - Share a model using The Exchange - Visualize the forecast models for a given level of the hierarchy |
| Use Time Series data creation options |
- Explain the differences between data accumulation and data aggregation - Given a scenario select the appropriate accumulation or aggregation options |
| Implement a hierarchical model or combined model |
- Given a scenario select the appropriate reconciliation method for a hierarchical model - Generate a combined model |
Post-Forecasting Functionality (10%-15%) |
|
| Implement an override on a forecast in SAS Model Studio |
- Apply an override to a forecast - Resolve an override conflict - Use attribute variable to set an override - Disseminate tables containing the results of a forecast (manually vs. automatically) |
| Export a forecast | - Prepare exported data set for use in SAS Visual Analytics |
Optimization (25%-30%) |
|
| Optimize using Linear Programming |
- Explain local properties of functions that are used to solve mathematical optimization problems - Use the OPTMODEL procedure to enter and solve simple linear programming problems - Formulate linear programming problems using index sets of arrays of decision variables, families of constraints, and values stored in parameter arrays - Modify a linear programming problem (changing bounds or coefficients, fixing variables, adding variables or constraints) within the OPTMODEL procedure |
| Optimize using Nonlinear Programming |
- Use the OPTMODEL procedure to enter and solve simple nonlinear programming problems - Describe how, conceptually and geometrically, iterative improvement algorithms solve nonlinear programming problems - Identify the optimality conditions for nonlinear programming problems - Solve nonlinear programming problems using OPTMODEL procedure - Interpret information written to the SAS log during the solution of a nonlinear programming problem - Differentiate between the NLP algorithms and how solver options influence the NLP algorithms |
| Optimize using Mixed Integer Linear Programming |
- Use the OPTMODEL procedure to enter and solve simple MILP problems - Identify the optimality conditions for MILP problems - Solve MILP programming problems using the OPTMODEL procedure |
The SAS has created this credential to assess your knowledge and understanding in the specified areas through the A00-407 certification exam. The SAS Certified Specialist - Forecasting and Optimization Using SAS Viya exam holds significant value in the market due to the brand reputation of SAS. We highly recommend thorough study and extensive practice to ensure you pass the SAS Viya Forecasting and Optimization exam with confidence.