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ISTQB Generative AI (CT‑GenAI) Certification Exam Prep
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Introduction to ISTQB Generative AI Certification Course
Who Can Take this Course
Career Path for Testers
Business Outcomes
Examinable Learning Objectives
1 Introduction to AI & Generative AI | Generative AI for Testers
1.1 Generative AI Foundations and Key Concepts
1.1.1 AI Spectrum Symbolic AI Classical MI Deep Learning GgenAI
1.1.2 Basics of GenAI and LLMs
1.1.3 Foundation Instruction Tuned and Reasoning LLMs
1.1.4 Moltimodal LLMs and Vision Language Models
1.2 Leveraging GenAI in Software Testing
1.2.1 Key LLM Capabilities for Test Tasks
1.2.2 AI Chatbots and LLM Powered Test Applications
2 Prompt Engineering for Effective Software Testing
2.1 Effective Prompt Development
2.1.1 Structure of Prompts for GenAI in Software Testing
2.1.2 Core Prompting Techniques for Software Testing
2.1.3 System Prompt and User Prompt
2.2 Applying Prompt Engineering Techniques Software Test Tasks
2.2.1 Test Analysis with GenAI
2.2.2 Test Design and Implementation with GenAI
2.2.3 Automated Regression Testing with GenAI
2.2.3 Automated Regression Testing with GenAI (1)
2.2.4 Test Monitoring and Test Control with GenAI
2.2.5 Choosing Prompting Techniques for Software Testing
2.3 Evaluate GenAI Results and Refine Prompts
2.3.1 Metrics for Evaluating the Results of GenAI
2.3.2 Techniques for Evaluating and Iteratively Refining Prompts
3 Managing Risks of GenAI in Software Testing
3.1 Hallucinations Reasoning Errors and Biases
3.1.1 Hallucinations Reasoning Errors and Biases in GenAI
3.1.2 Identify Hallucinations Reasoning Errors and Biases
3.1.3 Mitigation Techniques for Hallucinations Reasoning Errors and Biases
3.1.4 Mitigation of Non-Deterministic Behavior of LLMs
3.2 Data Privacy and Security Risks of GenAI in Software Testing
3.2.1 Data Privacy and Security Risks Associated with using GenAI
3.2.2 Data Privacy and Vulnerabilities in GenAI for Test Process and Tools
3.2.3 Mitigation Strategies to Protect Data Privacy and Enhance Security
3.3 Energy Consumption and ENV Impact of GenAI
3.3.1 The Impact of using GenAI on Energy Consumption
3.4 AI Regulations Standards and Best Practices
3.4.1 AI Regulations Standards and Frameworks for GenAI in Software Testing
4 LLM-Powered Test Infrastructure for Software Testing
4.1 Architectural Approaches for LLM Powered Test Infra
4.1.1 Key Arch Components and Concepts of LLM Powered Test Infra
4.1.2 Retrieval Augmented Generation
4.1.3 The Role of LLM Powered Agents in Automation Testing
4.2 Fine Tuning and LLMops
4.2.1 Fine Tuning LLMs for Test Tasks
4.2.2 LLOps When Deploying and Managing LLMs
5 Deploying and Integrating Generative AI in Test Organizations
5.1 Roadmap for Adoption of GenAI
5.1.1 Recall the Risks of Shadow AI
5.1.2 Explain the Key Aspects to Consider When Defining GenAI
5.1.3 Key Criteria for Selecting LLM SLM
5.1.4 Recall Key Phases in GenAI Adoption
5.2 Manage Change when Adopting GenAI
5.2.1 Essential Skills and Knowledge for Testing with GenAI
5.2.2 Building GenAI Capabilities in Test Teams
5.2.3 Evolving Test Processes in AI Enabled Test Organizations
Course Conclusion
Introduction
Introduction to ISTQB Generative AI Certification Course
Preview
Who Can Take this Course
Preview
Career Path for Testers
Preview
Business Outcomes
Preview
Examinable Learning Objectives
Preview
1 Introduction to Generative AI for Software Testing
1 Introduction to AI & Generative AI | Generative AI for Testers
Preview
1.1 Generative AI Foundations and Key Concepts
1.1.1 AI Spectrum Symbolic AI Classical MI Deep Learning GgenAI
1.1.2 Basics of GenAI and LLMs
1.1.3 Foundation Instruction Tuned and Reasoning LLMs
1.1.4 Moltimodal LLMs and Vision Language Models
1.2 Leveraging GenAI in Software Testing
1.2.1 Key LLM Capabilities for Test Tasks
1.2.2 AI Chatbots and LLM Powered Test Applications
2 Prompt Engineering for Effective Software Testing
2 Prompt Engineering for Effective Software Testing
2.1 Effective Prompt Development
2.1.1 Structure of Prompts for GenAI in Software Testing
2.1.2 Core Prompting Techniques for Software Testing
2.1.3 System Prompt and User Prompt
2.2 Applying Prompt Engineering Techniques Software Test Tasks
2.2.1 Test Analysis with GenAI
2.2.2 Test Design and Implementation with GenAI
2.2.3 Automated Regression Testing with GenAI
2.2.3 Automated Regression Testing with GenAI (1)
2.2.4 Test Monitoring and Test Control with GenAI
2.2.5 Choosing Prompting Techniques for Software Testing
2.3 Evaluate GenAI Results and Refine Prompts
2.3.1 Metrics for Evaluating the Results of GenAI
2.3.2 Techniques for Evaluating and Iteratively Refining Prompts
3 Managing Risks of GenAI in Software Testing
3 Managing Risks of GenAI in Software Testing
3.1 Hallucinations Reasoning Errors and Biases
3.1.1 Hallucinations Reasoning Errors and Biases in GenAI
3.1.2 Identify Hallucinations Reasoning Errors and Biases
3.1.3 Mitigation Techniques for Hallucinations Reasoning Errors and Biases
3.1.4 Mitigation of Non-Deterministic Behavior of LLMs
3.2 Data Privacy and Security Risks of GenAI in Software Testing
3.2.1 Data Privacy and Security Risks Associated with using GenAI
3.2.2 Data Privacy and Vulnerabilities in GenAI for Test Process and Tools
3.2.3 Mitigation Strategies to Protect Data Privacy and Enhance Security
3.3 Energy Consumption and ENV Impact of GenAI
3.3.1 The Impact of using GenAI on Energy Consumption
3.4 AI Regulations Standards and Best Practices
3.4.1 AI Regulations Standards and Frameworks for GenAI in Software Testing
4 LLM-Powered Test Infrastructure for Software Testing
4 LLM-Powered Test Infrastructure for Software Testing
4.1 Architectural Approaches for LLM Powered Test Infra
4.1.1 Key Arch Components and Concepts of LLM Powered Test Infra
4.1.2 Retrieval Augmented Generation
4.1.3 The Role of LLM Powered Agents in Automation Testing
4.2 Fine Tuning and LLMops
4.2.1 Fine Tuning LLMs for Test Tasks
Preview
4.2.2 LLOps When Deploying and Managing LLMs
5 Deploying and Integrating Generative AI in Test Organizations
5 Deploying and Integrating Generative AI in Test Organizations
5.1 Roadmap for Adoption of GenAI
5.1.1 Recall the Risks of Shadow AI
5.1.2 Explain the Key Aspects to Consider When Defining GenAI
5.1.3 Key Criteria for Selecting LLM SLM
5.1.4 Recall Key Phases in GenAI Adoption
5.2 Manage Change when Adopting GenAI
5.2.1 Essential Skills and Knowledge for Testing with GenAI
5.2.2 Building GenAI Capabilities in Test Teams
5.2.3 Evolving Test Processes in AI Enabled Test Organizations
Conclusion
Course Conclusion
Preview
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