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ISTQB Certified Tester – Testing with Generative AI (CT-Gen AI)

Generative AI brings new opportunities and risks to software testing, requiring testers to understand how AI systems behave, fail, and can be validated. This certification provides a structured introduction into testing applications that incorporate generative models, focusing on data quality, prompt reliability, model evaluation, and risk mitigation. It helps professionals understand the practical and ethical considerations of testing AI-driven systems.

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Course Includes

  • 16 Hrs of Sessions
  • 24/7 Lifetime Support
  • One-on-one doubt clearing
  • Hands-On Driven
  • Certification Based Curriculum
  • Career path guidance
  • Real Time Users
  • Flexible Schedules

Why Choose TM SQUARE?

TM SQAURE is world’s best organization which has delivered result orientated trainings on ISTQB Certified Tester – Testing with Generative AI (CT-Gen AI) Training to global participants and corporates.

We have delivered the The ISTQB Certified Tester – Testing with Generative AI (CT-Gen AI) program to over 15000+ professionals, focusing on practical, application-oriented learning. Our sessions are designed to be engaging, activity-based, and tailored to real-world challenges. With an average participant rating of 4.9/5, the program is consistently recognized for its clarity, relevance, and impact. Join this course to experience the expertise of our professional instructors and best experience of learning concepts.

TM SQUARE is a affiliated member of ISTQB and aligned with ISTQB standards and syllabus.

Course Overview

Generative AI brings new opportunities and risks to software testing, requiring testers to understand how AI systems behave, fail, and can be validated. This certification provides a structured introduction into testing applications that incorporate generative models, focusing on data quality, prompt reliability, model evaluation, and risk mitigation. It helps professionals understand the practical and ethical considerations of testing AI-driven systems.

 

Course Outline

What is your return policy?

We offer a 30-day return policy from the date of purchase.

Do I need a receipt to return an item?

Yes, a valid receipt or proof of purchase is required.

Can I return online purchases in store?

Yes, online purchases can be returned in any of our physical stores.

How long does shipping take?

Standard shipping typically takes 3–5 business days.

Do you ship internationally?

Yes, we ship to most countries worldwide. Additional charges may apply.

Which countries are excluded?

We currently do not ship to embargoed countries or regions with postal restrictions.

Introduction to Generative AI for Software Testing

Generative AI Foundations and Key Concepts
AI Spectrum: Symbolic AI, Classical Machine Learning, Deep Learning, and Generative AI
Basics of Generative AI and LLMs
Foundation, Instruction-Tuned and Reasoning LLMs
Multimodal LLMs and Vision-Language Models
Leveraging Generative AI in Software Testing: Core Principles
Key LLM Capabilities for Test Tasks
AI Chatbots and LLM-Powered Testing Applications for Software Testing
Prompt Engineering for Effective Software Testing

Effective Prompt Development
Structure of Prompts for Generative AI in Software Testing
Core Prompting Techniques for Software Testing
System Prompt and User Prompt
Applying Prompt Engineering Techniques to Software Test Tasks
Test Analysis with Generative AI
Test Design and Test Implementation with Generative AI
Automated Regression Testing with Generative AI
Test Monitoring and Test Control with Generative AI
Choosing Prompting Techniques for Software Testing
Evaluate Generative AI Results and Refine Prompts for Software Test tasks
Metrics for Evaluating the Results of Generative AI on Test tasks
Techniques for Evaluating and Iteratively Refining Prompts
Managing Risks of Generative AI in Software Testing

Hallucinations, Reasoning Errors and Biases
Hallucinations, Reasoning Errors and Biases in Generative AI
Identify Hallucinations, Reasoning Errors and Biases in LLM Output
Mitigation techniques of GenAI hallucinations, reasoning errors and biases in software test tasks
Mitigation of Non-Deterministic Behavior of LLMs
Data Privacy and Security Risks of Generative AI in Software Testing
Data Privacy and Security Risks Associated with Using Generative AI
Data Privacy and Vulnerabilities in Generative AI for Test processes and Tools
Mitigation Strategies to Protect Data Privacy and Enhance Security in Testing with Generative AI
Energy Consumption and Environmental Impact of Generative AI in Software Testing
The Impact of Using GenAI on Energy Consumption and CO2 Emissions
AI Regulations, Standards, and Best Practice Frameworks
AI Regulations, Standards and Frameworks Relevant to GenAI in Software Testing
LLM-Powered Test Infrastructure for Software Testing

Architectural Approaches for LLM-Powered Test Infrastructure
Key Architectural Components and Concepts of LLM-Powered Test Infrastructure
Retrieval-Augmented Generation
The Role of LLM-Powered Agents in Automating Test processes
Fine-Tuning and LLMOps: Operationalizing Generative AI for Software Testing
Fine-Tuning LLMs for Test tasks
LLMOps when Deploying and Managing LLMs for Software Testing
Deploying and Integrating Generative AI in Test organizations

Roadmap for the Adoption of Generative AI in Software Testing
Risks of Shadow AI
Key Aspects of a Generative AI Strategy in Software Testing
Selecting LLMs/SLMs for Software Test Tasks
Phases when Adopting Generative AI in Software Testing
Manage Change when Adopting Generative AI for Software Testing
Essential Skills and Knowledge for Testing with Generative AI
Building Generative AI Capabilities in Test Teams
Evolving Test Processes in AI-Enabled Test organizations
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