Certified AI Testing Professional (CAITP)

Certified Testing AI Professional: Ensure AI Systems' Accuracy and Reliability. Master AI Quality Assurance and Enhance Performance with Industry Standards.

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What Sets Our Program Apart?

  •    E-Learning Library Access
  •    Expertly crafted BOK with ready-to-implement resources
  •    Lifetime Valid Certification with 2 Exam Attempts
  •    Capstone Projects
  •    Generative AI Interview Practice Platform
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About AI Testing Certification

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Objectives Of Certified AI Testing Professional

  • Validate AI testing knowledge and practical expertise
  • Ensure the reliability and safety of AI systems
  • Understand ethical considerations in AI system testing
  • Apply AI testing best practices and methodologies
  • Leverage AI for effective software testing processes
  • Identify AI system risks and mitigation strategies
  • Implement AI testing tools and techniques effectively
  • Continuously upskill in the evolving AI testing domain

Benefits Of AI Testing Professional Certificate

  • Unlock AI testing job opportunities
  • Accelerate career growth in AI
  • Enhance AI project delivery capabilities
  • Leverage AI testing best practices
  • Demonstrate AI testing thought leadership
  • Gain competitive edge in AI
  • Build AI testing professional network
  • Stay ahead in evolving AI
  • Increase AI testing job marketability
  • Maximize AI testing career potential

Exam Syllabus Of AI Testing Professional Certification

  • Definition of AI and AI Effect
  • Narrow, General, and Super AI
  • AI-Based and Conventional Systems
  • AI Technologies
  • AI Development Frameworks
  • Hardware for AI-Based Systems
  • AI as a Service (AIaaS)
  • Pre-Trained Models
  • Standards, Regulations, and AI

  • Flexibility and Adaptability
  • Autonomy
  • Evolution
  • Bias
  • Ethics
  • Side Effects and Reward Hacking
  • Transparency, Interpretability, and Explainability
  • Safety and AI

  • Forms of ML
  • ML Workflow
  • Selecting a Form of ML
  • Factors Involved in ML Algorithm Selection
  • Overfitting and Underfitting

  • Data Preparation as part of the ML Workflow
  • Training, Validation, and Test Datasets in the ML Workflow
  • Dataset Quality Issues
  • Data Quality and its Effect on the ML Model
  • Data Labelling for Supervised Learning

  • Confusion Matrix
  • Additional ML Functional Performance Metrics for Classification, Regression, and Clustering
  • Limitations of ML Functional Performance Metrics
  • Selecting ML Functional Performance Metrics
  • Benchmark Suites for ML

  • Neural Networks
  • Coverage Measures for Neural Networks

  • Specification of AI-Based Systems
  • Test Levels for AI-Based Systems
  • Test Data for Testing AI-Based Systems
  • Testing for Automation Bias in AI-Based Systems
  • Documenting an ML Model
  • Testing for Concept Drift
  • Selecting a Test Approach for an ML System

  • Challenges Testing Self-Learning Systems
  • Testing Autonomous AI-Based Systems
  • Testing for Algorithmic, Sample, and Inappropriate Bias
  • Challenges Testing Probabilistic and Non-Deterministic AI-Based Systems
  • Challenges Testing Complex AI-Based Systems
  • Testing the Transparency, Interpretability, and Explainability of AI-Based Systems
  • Test Oracles for AI-Based Systems
  • Test Objectives and Acceptance Criteria

  • Adversarial Attacks and Data Poisoning
  • Pairwise Testing
  • Back-to-Back Testing
  • A/B Testing
  • Metamorphic Testing
  • Experience-Based Testing of AI-Based Systems
  • Selecting Test Techniques for AI-Based Systems

  • Test Environments for AI-Based Systems
  • Virtual Test Environments for Testing AI-Based Systems

  • AI Technologies for Testing
  • Using AI to Analyze Reported Defects
  • Using AI for Test Case Generation
  • Using AI for the Optimization of Regression Test Suites
  • Using AI for Defect Prediction
  • Using AI for Testing User Interfaces

Meet our Advisor

Nadeem Augustine
Nadeem Augustine

Head of DevOps/Release Management

Daniel oh
Daniel oh

Principle technical marketing manager

Kailashpathi C Gujari
Kailashpathi C Gujari

Senior could Solution architect

Enrollment Options

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Target Audiences For AI Testing Certification

  AI Testing Engineers/Specialists
  Software Quality Assurance Professionals
  Test Automation Engineers
  System Validation Analysts
  Software Testers/Test Analysts
  Data Testing Professionals
  Testing Consultants/Managers
  Test Environment Architects
  Testing Tool Developers
  AI/ML Researchers/Educators
  DevOps Professionals

Pre-Requisites For Certified AI Testing Professional

For the GSDC Certified AI Testing Professional (CAITP) certification, having prior work experience or knowledge in AI testing or a related field is highly recommended, although not mandatory.

Exam Details Of AI Testing Professional Certification

Exam Questions

40

Exam Format

Multiple choice

Language

English

Duration

90 min

Passing Score

65%

Open Book

No

Certification Validity

Lifetime

Complimentary Retake

Yes

Sample Certificate

Certified AI Testing Professional (CAITP)

About AI Testing Certification

The GSDC AI Testing Professional certification is designed to endorse professionals in AI testing which is rapidly emerging as a significant domain. AI is predicted to displace 2 million jobs, but it also has the potential of creating double that number. AI testing is expected to generate 3MN jobs by 2024 One out of every five companies is now using AI in decision-making, and as a result, certified AI testing professionals.

This ai testing certification ensures that people are trained in a specific way on how to deal with some of the complicated cases in testing and validating AI systems to guarantee their safety, effectiveness and other virtues as well as meeting the ethical standards accorded to the AI systems.

The Certified AI Testing Professional who has completed their educational program as a professional can earn $135,000 per annum and may find new opportunities to grow in other fields. The candidates who will be passing for this certified ai testing professional certification will be showing the industry and the employers that they are in this field to stay and that they are willing to go the extra mile and learn every progressive and sophisticated tool in applying AI testing methodologies to increase the earnings and outcome of their companies.

Frequently Asked Questions

Choose your preferred package above to begin your certification journey

AI Testing Professional certifies expertise in testing AI applications. Benefits include new job opportunities, higher earnings, industry recognition, and staying up-to-date with AI testing trends.

Excellent job prospects due to increasing demand for AI testing skills across industries. Roles include AI Testing Engineer, QA Professional, Test Automation Engineer, and AI Testing Consultant.

Highly relevant as AI adoption increases. Certification proves expertise in ensuring reliability, safety, and ethics of AI systems, crucial for organizations deploying AI solutions.

Hassle-free lifetime validity allows GSDC certification holders to avoid renewal applications and payments.

Access to skilled AI testing professionals, increased confidence in AI application quality, improved risk management, competitive advantage, and enhanced credibility with customers.

Professionals involved in testing AI systems, like AI Testing Engineers, QA specialists, and Test Automation experts.

Why GSDC ?

The Global Skill Development Council (GSDC) is an independent, vendor-neutral, international credentialing and certification organization for the emerging technologies:

  • Advisory board members and SMEs are from around the world, drawn from different specializations.

  • Supported by the world's most esteemed thought leaders from Yale, MIT, Stanford, Wharton, and Harvard.

  • Hub of Trending Technologies and framework certifications.

  • Content curated by Industry's best Subject matter experts.

  • Webinars and Conferences.

  • Training Partners Across The Globe.

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