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ISTQB Certified Tester AI (CT-AI)

AI training for testers can equip them with the knowledge and skills to effectively test AI-powered systems and leverage AI to enhance the testing process. This includes understanding AI concepts, testing methodologies, and using AI-powered tools. 

This includes understanding AI concepts, testing methodologies, challenges of Testing an AI Based system and using AI-powered tools. 

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Course Includes

  • 32 Hrs of Sessions
  • 24/7 Lifetime Support
  • One-on-one doubt clearing
  • Certification Based Curriculum
  • Career path guidance
  • Real-time Use cases
  • Flexible Schedules

What is ISTQB AI Tester Training?

ISTQB AI Tester Training is a specialized software testing course focused on testing AI and machine learning-based applications. It helps professionals understand AI concepts, AI quality characteristics, machine learning workflows, and testing techniques for AI-driven systems.

 

Why Learn ISTQB AI Testing in 2026?

AI-powered applications are rapidly growing across industries, increasing the demand for skilled AI testing professionals. ISTQB AI Testing certification helps testers understand AI systems, machine learning workflows, AI risks, and modern testing strategies required in today’s software industry. It also supports career growth in AI-driven QA and automation domains.

 

Business Outcomes

Professionals who complete the ISTQB AI Tester Training program will be able to:

  • Understand AI and Generative AI testing concepts
  • Learn testing techniques for AI and machine learning systems
  • Design and execute test cases for AI-based applications
  • Contribute to effective AI testing strategies in Agile environments

 

Why Choose TM SQUARE?

TM SQUARE being the world’s leading organization to deliver the ISTQB AI Tester Training to global participants and corporates.

We have delivered the ISTQB AI Tester Training 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 recognised 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

  • ISTQB AI Testing certification is a career must-have for everyone who may test AI-based systems or is using AI for testing.You need to future-proof your career and your organization with AI Testing certification. ISTQB AI Testing certification is critical for the careers of not just testers, but also test analysts, data analysts, test engineers, test consultants, test managers, user acceptance testers, and software developers.
  • This certification is also important for anyone who wants a basic understanding of testing AI-based systems or will be using AI for testing, such as project managers, quality managers, software development managers, business analysts, operations team members, IT directors, and management consultants.

Participants must be ISTQB Foundation level certified for ISTQB AI Certification

 

 

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.

Module 1 Introduction to AI

AI-Based and Conventional Systems
Narrow AI, General AI, and Super AI
Different Types of AI Technologies
Generative AI
Hardware for Machine Learning Systems
Development and Hosting of AI Models
Machine Learning Development Frameworks
Regulations and Standards for AI
Module 2 Quality Characteristics for AI-Based Systems

AI-Specific Quality Characteristics
AI and Safety
Acceptance Criteria for AI-Based Systems
Module 3 Machine Learning

Introduction to Machine Learning
Different Forms of Machine Learning
Machine Learning Workflow
Hands-on Exercise: Create a Machine Learning Model
Pretrained Models, Fine-Tuning, and Retrieval-Augmented Generation
Data for Machine Learning
Activities in Data Preparation
Hands-on Exercise: Data Preparation in Support of the Creation of a Machine Learning Model
ML Functional Performance Metrics for Classification
Calculation of Machine Learning Functional Performance Metrics
Hands-on Exercise: Evaluate a Machine Learning Model using Selected ML Functional Performance Metrics
Hands-on Exercise: Show the Impact of Different Machine Learning Models and Dataset Combinations
Neural Networks
Structure and Working of a Deep Neural Network
Perceptron Exercise
Coverage Measures for Neural Networks
Module 4 Testing AI-Based Systems

Introduction to Testing AI-Based Systems
Locked and Adaptive AI-Based Systems
Rationale for a Statistical Approach to Testing AI-Based Systems
Test Oracles for AI-Based Systems
Testing Generative AI and Large Language Models
Testing Generative AI
Red Teaming
Hands-on Exercise: Exploratory Testing of a Large Language Mode
Test Levels and Machine Learning Systems
Risk-Based Testing of Machine Learning Systems
Module 5 Input Data Testing for Machine Learning Systems

Input Data Testing
Input Data Risks and Mitigations
Testing for Bias
Data Pipeline Testing
Testing for Data Representativeness
Dataset Constraint Testing
Label Correctness Testing
Hands-on Exercise: Input Data Testing
Module 6 Model Testing for Machine Learning Systems

Model Testing
Machine Learning Model Risks and Mitigations
Machine Learning Model Documentation and Review
ML Functional Performance Testing of Probabilistic Machine Learning Systems
Adversarial Testing of Machine Learning Systems
Metamorphic Testing
Hands-on Exercise: Apply Metamorphic Testing
Drift Testing
Testing for Overfitting and Underfitting
A/B Testing
Back-to-Back Testing
Module 7 Machine Learning Development Testing

Machine Learning Development Risks and Mitigations
Machine Learning System Deployment Testing
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