ISTQBCertified Tester Testing with Generative AI
The ISTQB Certified Tester – Testing with Generative AI (CT-GenAI) certification equips testing and quality-engineering professionals with the knowledge to apply Large Language Models (LLMs) and generative AI across the entire testing lifecycle. From prompt engineering and risk management to integrating AI-powered test infrastructure, this specialist credential prepares you to drive responsible, AI-assisted quality assurance in your organization.
858 practice questions · Updated 2026-07-30
5Domains
31Objectives
233Concepts
858Questions
CT-GENAI Curriculum
Every domain, objective, and concept the CT-GENAI exam measures.
- Definition of Generative AI
- Core principles of Generative AI
- Definition of Large Language Models (LLMs)
- How LLMs work
- Capabilities of LLMs
- Limitations of LLMs
- Applications of Generative AI and LLMs in software testing
- Foundation LLMs
- Instruction-Tuned LLMs
- Reasoning LLMs
- Differences Among LLM Types
- Definition of Multimodal LLMs
- Definition of Vision-Language Models
- Capabilities of Multimodal LLMs
- Applications in Software Testing
- Limitations and Challenges
- Text Generation
- Text Summarization
- Text Classification
- Text Extraction
- Question Answering
- Language Translation
- Code Generation and Understanding
- Conversational Interaction
- AI Chatbots in Software Testing
- LLM-Powered Testing Applications
- Use Cases of AI Chatbots in Testing
- Limitations and Risks of AI Chatbots in Testing
- Prompt Structure Components
- Role Assignment in Prompts
- Context Provision
- Task Specification
- Constraints and Boundaries
- Output Format Definition
- Iterative Refinement of Prompts
- Common Prompt Patterns for Testing
- Avoiding Ambiguity and Bias
- Evaluating Prompt Effectiveness
- Core Prompting Techniques
- Zero-shot Prompting
- Few-shot Prompting
- Chain-of-Thought Prompting
- Role Prompting
- Contextual Prompting
- Iterative Refinement
- Definition of System Prompt
- Definition of User Prompt
- Differences Between System and User Prompts
- Crafting Effective System Prompts
- Crafting Effective User Prompts
- Context and Instruction Separation
- Prompt Hierarchy and Precedence
- Iterative Refinement of Prompts
- Test Analysis Fundamentals with Generative AI
- Generative AI for Test Condition Identification
- Generative AI for Test Case Design
- Generative AI for Test Data Generation
- Generative AI for Test Oracles
- Generative AI for Traceability and Coverage Analysis
- Evaluating and Refining Generative AI Outputs
- Ethical and Practical Considerations in AI-Assisted Test Analysis
- Test Design with Generative AI
- Test Implementation with Generative AI
- AI-Generated Test Case Creation
- AI-Generated Test Data Generation
- AI-Assisted Test Script Generation
- Validation of AI-Generated Tests
- Integration of AI in Test Design Workflow
- Limitations and Risks of AI in Test Design
- Regression Testing Fundamentals
- Generative AI in Regression Testing
- Test Case Generation for Regression
- Test Data Synthesis for Regression
- Test Oracle Automation
- Regression Test Selection and Prioritization
- Automated Test Maintenance
- Integration with CI/CD Pipelines
- Evaluating AI-Generated Regression Tests
- Challenges and Limitations
- Test Monitoring with GenAI
- Test Control with GenAI
- GenAI for Anomaly Detection in Test Results
- Predictive Test Monitoring
- Automated Test Reporting with GenAI
- Human Oversight in GenAI-Driven Monitoring
- Define evaluation metrics for GenAI test tasks
- Classify metrics by test task type
- Apply accuracy and correctness metrics
- Apply coverage and completeness metrics
- Apply relevance and usefulness metrics
- Apply consistency and reliability metrics
- Apply efficiency and performance metrics
- Apply human-centric evaluation metrics
- Interpret metric results for decision-making
- Evaluation metrics for prompt effectiveness
- Iterative refinement strategies
- Error analysis and root cause identification
- Techniques for prompt variation
- Handling edge cases and adversarial inputs
- Incorporating feedback loops
- Balancing specificity and generality
- Documenting prompt evolution
- Definition of Hallucinations
- Types of Hallucinations
- Causes of Hallucinations
- Detection of Hallucinations
- Definition of Reasoning Errors
- Types of Reasoning Errors
- Detection of Reasoning Errors
- Definition of Biases
- Types of Biases
- Sources of Biases
- Detection of Biases
- Mitigation Strategies
- Understanding Non-Determinism in LLMs
- Identifying Impact on Testing
- Techniques for Output Stabilization
- Using Deterministic Modes and APIs
- Implementing Post-Processing Normalization
- Designing Robust Test Assertions
- Retry and Consensus Strategies
- Version Pinning and Model Control
- Monitoring and Logging for Non-Determinism
- Evaluating Residual Risk
- Identify data privacy risks in GenAI testing
- Identify data security risks in GenAI testing
- Apply data anonymization and pseudonymization techniques
- Implement data minimization strategies
- Ensure compliance with data protection regulations
- Establish secure data handling and access controls
- Conduct privacy impact assessments for GenAI testing
- Monitor and audit GenAI outputs for data leakage
- Energy Consumption of GenAI
- CO2 Emissions of GenAI
- Impact on Software Testing
- Mitigation Strategies
- Identify AI regulations
- Identify AI standards
- Identify AI frameworks
- Map regulations to GenAI testing
- Map standards to GenAI testing
- Map frameworks to GenAI testing
- Apply regulations in testing context
- Apply standards in testing context
- Apply frameworks in testing context
- LLM-Powered Test Infrastructure Overview
- Core Components of LLM-Powered Test Infrastructure
- LLM Integration in Test Execution
- Data Flow and Pipeline Architecture
- Role of LLMs in Test Automation
- Infrastructure Considerations for LLM-Powered Testing
- RAG fundamentals
- RAG in test infrastructure
- Retrieval techniques
- Chunking and embedding
- Prompt construction with retrieved context
- Evaluation of RAG systems
- Challenges and limitations
- Definition and Purpose of LLM-Powered Agents
- Agent Architecture and Components
- Automation of Test Case Generation
- Automation of Test Execution
- Automation of Test Result Analysis
- Integration with Test Infrastructure
- Benefits and Limitations
- Human Oversight and Collaboration
- Purpose of fine-tuning LLMs for test tasks
- Fine-tuning vs. prompt engineering
- Preparing a dataset for fine-tuning
- Fine-tuning process overview
- Evaluation of fine-tuned models for testing
- Challenges and best practices in fine-tuning for testing
- LLMOps fundamentals
- LLM lifecycle management
- Model versioning and registry
- Deployment strategies for LLMs
- Scaling and resource management
- Monitoring and observability
- Evaluation and quality assurance
- Security and compliance
- Cost optimization
- Integration with CI/CD
- Governance and ethics
- Definition of Shadow AI
- Risks of Shadow AI
- Mitigation Strategies for Shadow AI
- Define Generative AI Strategy
- Identify Strategic Goals
- Assess Organizational Readiness
- Plan Resource Allocation
- Address Risks and Ethics
- Establish Governance and Compliance
- Integrate with Existing Processes
- Measure and Evaluate Strategy
- LLM vs SLM characteristics
- Test task requirements analysis
- Model selection criteria
- Trade-off evaluation
- Deployment considerations
- Model benchmarking for testing
- Cost-benefit analysis
- Integration with test frameworks
- Ethical and compliance aspects
- Continuous evaluation and adaptation
- Adoption Phases Overview
- Phase 1: Exploration and Pilot
- Phase 2: Integration and Scaling
- Phase 3: Optimization and Governance
- Challenges and Success Factors
- Fundamentals of Generative AI
- Generative AI in Software Testing
- Test Data Generation
- Test Case and Script Generation
- Defect and Anomaly Detection
- AI Model Evaluation and Validation
- Ethical and Legal Considerations
- Integration with Test Tools and Frameworks
- Human Oversight and Collaboration
- Challenges and Limitations
- Identify GenAI skill requirements
- Plan training and upskilling
- Foster a GenAI adoption culture
- Establish GenAI roles and responsibilities
- Integrate GenAI into team processes
- Evaluate GenAI effectiveness
- Identify AI impact on test processes
- Adapt test planning for AI
- Integrate AI into test design
- Evolve test execution with AI
- Update test reporting and metrics
- Manage test data for AI
- Address AI-specific risks in testing
- Align test processes with AI governance
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