This page is a one-stop solution for any information you may require for SAS Certified Specialist - Forecasting and Optimization Using SAS Viya (A00-407) Certification exam. The SAS A00-407 Exam Summary, Syllabus Topics and Sample Questions provide the base for the actual SAS Viya Forecasting and Optimization Specialist exam preparation, we have designed these resources to help you get ready to take your dream exam.
The SAS Certified Specialist - Forecasting and Optimization Using SAS Viya credential is globally recognized for validating SAS Viya Forecasting and Optimization knowledge. With the SAS Viya Forecasting and Optimization Specialist Certification credential, you stand out in a crowd and prove that you have the SAS Viya Forecasting and Optimization knowledge to make a difference within your organization. The SAS Certified Specialist - Forecasting and Optimization Using SAS Viya Certification (A00-407) exam will test the candidate's knowledge on following areas.
SAS A00-407 Exam Summary:
| Exam Name | SAS Certified Specialist - Forecasting and Optimization Using SAS Viya |
| Exam Code | A00-407 |
| Exam Duration | 90 minutes |
| Exam Questions | 50 |
| Passing Score | 68% |
| Exam Price | $180 (USD) |
| Books |
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 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 the knowledge and understanding of a candidate in the area as above via the certification exam. The SAS Viya Forecasting and Optimization (A00-407) Certification exam contains a high value in the market being the brand value of the SAS attached with it. It is highly recommended to a candidate to do a thorough study and also get a hand full of the practice to clear SAS Certified Specialist - Forecasting and Optimization Using SAS Viya exam without any hiccups.