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Passing the SASInstitute A00-402 exam is a valuable credential for professionals who work with SAS Viya and want to demonstrate their expertise in building and deploying supervised machine learning models. SAS Viya 3.5 Supervised Machine Learning Pipelines certification is recognized globally and can help individuals advance their careers in data science, machine learning, and artificial intelligence.
Steps to Become SAS Certified Specialist (A00-402) Exam
Candidates working across multiple industries can take up this certification to validate their skills. There are several ways available to become SAS Certified Specialist certified candidate. These include taking paid training courses from a few commercial vendors or enrolling in university programs that are dedicated to preparing students for this certification.
Candidates can also seek out relevant work experience and directly apply for the certification without any formal training programs. To ensure that candidates fulfill all requirements for getting certified, they are subject to rigorous testing under the supervision of certified trainers who are employed by approved training organizations or
In order to be eligible for the SAS Certified Specialist exam, candidates should have at least 18 months of work experience in SAS programming. The exam is widely accepted in industries such as healthcare and pharmaceuticals, manufacturing, finance and insurance, retail/wholesale distribution, telecommunications, and information technology. Initially designed to cater to the needs of business managers who rely on SAS software for data analysis, the SAS Certified Specialist exam has become popular in other industries as well due to the rapid growth of the IT industry. SAS Institute A00-402 Dumps helps you in becoming a certified.
The SAS Certified Specialist certification is a credential that has been designed to assess an individual's ability in using SAS software tools, applications, and procedures. It is a professional-level certification that validates the candidates' expertise in SAS programming.
SASInstitute A00-402 Exam Overview:
| Certification Vendor: | SAS Institute |
|---|---|
| Exam Name: | SAS Viya 3.5 Supervised Machine Learning Pipelines |
| Exam Number: | A00-402 |
| Available Languages: | English |
| Exam Duration: | 100 minutes |
| Exam Format: | Multiple-choice, Short-answer |
| Certificate Validity Period: | No expiration |
| Real Exam Qty: | 50-55 |
| Passing Score: | 65% |
| Exam Price: | $180 USD |
| Recommended Training: | Machine Learning Using SAS Viya SAS Viya 3.5 Machine Learning Specialist Exam Content Guide |
| Exam Registration: | Pearson VUE Registration SAS Certification Portal |
| Exam Way: | In-person at Pearson VUE test centers or online proctored |
| Pre Condition: | No formal prerequisites; recommended experience with SAS Viya, data mining, and machine learning concepts |
| Official Syllabus URL: | https://www.sas.com/en_us/certification/credentials/advanced-analytics/machine-learning-specialist.html |
To prepare for the SASInstitute A00-402 certification exam, candidates should have a strong background in statistics, programming, and data analysis. They should also have experience with SAS Viya 3.5 software and be familiar with supervised learning algorithms such as linear regression, logistic regression, and decision trees. SASInstitute provides training courses, practice exams, and study materials to help candidates prepare for the exam and ensure they have the knowledge and skills required to pass.
SASInstitute A00-402 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Preparation and Exploration | 20-25% | - Variable selection and reduction - Partitioning data into training, validation, and test sets - Loading and accessing data sources - Handling missing values and outliers - Feature engineering and transformation - Data profiling and exploration |
| Topic 2: Model Assessment and Comparison | 15-20% | - Comparing models and selecting best performer - Profit/loss analysis and cutoff adjustment - Classification metrics: accuracy, precision, recall, F1, AUC - Interpreting model results and diagnostics - Regression metrics: RMSE, R-squared, MAE |
| Topic 3: Overview of Supervised Machine Learning | 10-15% | - Basic concepts and terminology - Prediction types and modeling goals - Model overfitting, underfitting, and generalization |
| Topic 4: Building Predictive Models | 25-30% | - Regularization and optimization methods - Regression models (linear, logistic) - Neural networks and deep learning basics - Using appropriate modeling nodes - Decision trees and tree ensembles |
| Topic 5: Model Deployment | 5-10% | - Exporting score code - Model scoring and operationalization - Registering and publishing models |
| Topic 6: Creating and Managing Pipelines | 15-20% | - Managing pipeline flow and execution - Using pipeline templates and automation - Building pipelines in Model Studio - Configuring and connecting nodes |
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