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SASInstitute A00-402 (SAS Viya 3.5 Supervised Machine Learning Pipelines) certification exam is designed to evaluate the individual's proficiency in designing, building, and deploying machine learning pipelines using SAS Viya. SAS Viya 3.5 Supervised Machine Learning Pipelines certification exam covers various topics such as data preparation, model development, model evaluation, and model deployment. A00-402 exam is ideal for professionals who work in the domain of predictive modeling, data mining, and machine learning. The SAS Viya platform offers a range of supervised machine learning algorithms, and the exam evaluates the candidate's ability to use these algorithms to solve real-world problems.
Reference: https://www.sas.com/en_us/certification/credentials/advanced-analytics/machine-learning-specialist.html
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Basics of SAS Institute A00-402 Exam
SAS Institute A00-402 Exam is a computer based test which is conducted by PEARSON VUE. It is a test on the fundamentals of programming in SAS Language. In this exam, you will be tested on your knowledge of the SAS language and its use. This includes your ability to code, design and create reports and charts, along with handling and modifying data in a database. It is required that the candidate should have an experience of 3 months or more as a SAS programmer before sitting for this exam. Since he/she should have worked with SAS on regular basis, he/she should be able to grasp the concepts quickly and effectively. The candidate can enroll themselves for the same once they have cleared the SAS Language first level test. SAS Institute A00-402 Dumps will help you prepare for the same. The SAS Language first level exam covers an overview of SAS Basics such as creating formulas, writing procedures, building classes etc. Once you are clear about the basics then it is time for you to enroll for this exam and attempt it without much preparation.
SASInstitute A00-402 Exam Syllabus Topics:
| Section | Weight | Objectives |
| Topic 1: Model Assessment and Deployment | 30% | - Evaluate and deploy models
- 1. Select champion models
- 2. ROC and lift curve analyses
- 3. Assess model performance metrics
- 4. Deploy models to production
|
| Topic 2: Data Sources and Data Exploration | 30% | - Access and manage data in Model Studio
- 1. Create projects and pipelines
- 2. Import and load data into SAS Viya
- 3. Explore data distributions and quality
- 4. Partition data into training, validation, and test sets
|
| Topic 3: Building Supervised Models | 40% | - Develop and tune machine learning models
- 1. Model comparison and tuning
- 2. Support vector machines
- 3. Gradient boosting models
- 4. Decision trees and forest models
|
| Topic 4: Data Preparation | | - Prepare and modify data for modeling
- 1. Transform, standardize and derive variables
- 2. Feature extraction and selection
- 3. Handle missing values
|