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Certified Testing AI Professional

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.

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OBJECTIVES OF certified Ai testing professional

1.Validate AI testing knowledge and practical expertise
2.Ensure the reliability and safety of AI systems
3.Understand ethical considerations in AI system testing
4.Apply AI testing best practices and methodologies
5.Leverage AI for effective software testing processes
6.Identify AI system risks and mitigation strategies
7.Implement AI testing tools and techniques effectively
8.Continuously upskill in the evolving AI testing domain

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

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




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.

EXAMINATION of AI  testing professional Certification 

60-minutes exam.
40-multiple choice questions (MCQ).
26 out of 40-65% is needed to pass.
In case the participant does not score the passing percentage, they will be granted a 2nd attempt at no additional cost. Re-examination can be taken up to 30 days from the date of the 1st exam attempt.




  • 1.Introduction to AI:
    • 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
  • 2.Quality Characteristics for AI-Based Systems:
    • Flexibility and Adaptability
    • Autonomy
    • Evolution
    • Bias
    • Ethics
    • Side Effects and Reward Hacking
    • Transparency, Interpretability, and Explainability
    • Safety and AI
  • 3.Machine Learning (ML) Overview:
    • Forms of ML
    • ML Workflow
    • Selecting a Form of ML
    • Factors Involved in ML Algorithm Selection
    • Overfitting and Underfitting
  • 4.ML Data:
    • 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
  • 5.ML Functional Performance Metrics:
    • 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
  • 6.ML Neural Networks and Testing:
    • Neural Networks
    • Coverage Measures for Neural Networks
  • 7.Testing AI-Based Systems Overview:
    • 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
  • 8.Testing AI-Specific Quality Characteristics:
    • 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
  • 9.Methods and Techniques for the Testing of AI-Based Systems:
    • 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
  • 10.Test Environments for AI-Based Systems:
    • Test Environments for AI-Based Systems
    • Virtual Test Environments for Testing AI-Based Systems
  • 11.Using AI for Testing:
    • 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


Nathan Summers

Designation - AI Testing Lead

The GSDC Certified AI Testing Professional (CAITP) certification has been instrumental in validating my expertise and opening doors to exciting AI testing opportunities. The recognition it carries has significantly boosted my career prospects.

Mike Kim

Designation - Senior AI Test Architect

Obtaining the AI Testing Professional Certification credential was a strategic move that paid off tremendously. It showcased my commitment to staying ahead in the AI testing domain, enabling me to tackle complex projects with confidence and precision.  

Fatima Ahmed

Designation - AI Testing Consultant

As an early adopter of AI technologies, the AI Testing certification solidified my position as a trusted AI testing authority. Employers immediately recognized the value I could bring to their organizations, leading to rewarding roles and opportunities.


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