SAS Certified Specialist: Forecasting and Optimization Using SAS Viya 3.4 (A00-407) Practice Exam

SAS Certified Specialist: Forecasting and Optimization Using SAS Viya 3.4 (A00-407) Practice Exam

The SAS Certified Specialist: Forecasting and Optimization Using SAS Viya 3.4 exam validates your ability to use SAS Viya for data visualization, pipeline modeling, hierarchical forecasting, post-forecasting tasks, and optimization. It assesses your skills across various functionalities of SAS Viya, making you a valuable asset in data-driven decision-making processes.

Who Should Take This Exam?

This certification is ideal for professionals working in various fields who want to demonstrate their expertise in forecasting and optimization using SAS Viya. The target audience includes:

• Business Analysts
• Data Analysts
• Statisticians
• SAS Programmers
• Anyone Aspiring to Be a Forecasting and Optimization Expert

Exam Details 

• Exam Code: A00-407
• Exam Name: SAS Certified Specialist: Forecasting and Optimization Using SAS Viya 3.4
• Exam Languages: English
• Exam Questions: 50 Questions
• Time: 90 minutes
• Passing Score: 68%

Course Outline 

The Exam covers the given topics  - 

Domain 1: Learn 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

Domain 2: Understand 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

Domain 3: Understand 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

Domain 4: Learn 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

Domain 5: 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