ISTQB Certified Tester AI Testing (CT-AI) Training Course
Length
3 days
Price
$2199
Cities
Melbourne, Sydney, Brisbane, Adelaide, Canberra, Perth
Learn More
Why Choose This Course
The ISTQB Certified Tester AI Testing Course (CT-AI) equips software testing and quality professionals to plan, design and execute tests for AI-based systems and apply AI techniques to support testing. The course aligns to the current ISTQB CT-AI syllabus and focuses on practical, exam-aligned coverage of machine learning fundamentals, AI-specific quality characteristics, test design approaches for non-deterministic behaviour, and the use of AI to accelerate defect analysis, test generation and regression suite optimisation. It is suitable for professionals working in Melbourne, Perth, Sydney and Brisbane who need a structured path to understand how AI changes testing practices and how testers contribute to responsible, effective AI delivery.
Learners explore the differences between conventional and AI-based systems, how data quality and labelling influence model behaviour, and how to select meaningful performance metrics for classification, regression and clustering. The course addresses challenges such as concept drift, automation bias, transparency and explainability, and fairness in models, providing actionable methods for designing oracles, building appropriate test environments and documenting AI components. It also covers practical uses of AI in testing, including defect prediction and UI testing, aligned with recognised industry guidance.
By the end of the course, you will be able to contribute to test strategies for AI-enabled products, design and run tests against machine learning components, and evaluate AI-specific risks with confidence. This training supports preparation for the ISTQB Certified Tester AI Testing certification while adding workplace-ready skills across multiple industries in Australia. A certificate of course attendance is included.
Prerequisites
To gain the certification, candidates must hold the ISTQB Certified Tester Foundation Level certificate. There are no formal prerequisites for this course.
Exam
Candidates can achieve this certification by passing the following exam(s).
- ISTQB Certified Tester AI Testing (CT-AI) exam
Books
- ISTQB Certified Tester AI Testing Course (CT-AI) course material included.
Delivery
- Instructor-led Classroom Training at our premises
- Live Virtual Online Training attend in real-time from anywhere
- In-House Training at your premises (4+ participants)
Skills Gained
Differentiate AI-based systems from conventional systems and explain implications for testing.
Identify AI technologies, development frameworks, and deployment patterns relevant to testing.
Describe AI-specific quality characteristics including transparency, interpretability, and explainability.
Recognise risks related to bias, ethics, autonomy, and safety in AI-based systems.
Explain machine learning forms and workflows, and where testers influence model quality.
Assess data preparation, labelling, and dataset partitioning for training, validation, and testing.
Detect dataset quality issues and evaluate their impact on model behaviour.
Select and interpret functional performance metrics for classification, regression, and clustering.
Design tests for neural networks, including coverage considerations.
Plan tests for concept drift and automation bias in production AI systems.
Choose suitable test approaches and oracles for probabilistic, non-deterministic behaviour.
Document AI components and test levels for AI-based systems across the lifecycle.
Evaluate transparency and explainability techniques to support testing decisions.
Define test infrastructure requirements and environments for AI-based systems.
Apply AI in testing to analyse defects, generate test cases, and optimise regression suites.
Implement and assess AI-led defect prediction in a test context.
Use AI approaches to support graphical user interface testing.
Contribute effectively to test strategy for AI-enabled products and services.
Communicate AI test findings to stakeholders in technical and business terms.
Align testing practices with relevant standards and regulations for AI-based systems.
Audience
Testers, test analysts, test engineers, test consultants, and test managers working on AI-based systems or using AI in testing.
Data analysts, software developers, and user acceptance testers involved in model evaluation or test automation using AI.
Project managers, quality managers, software development managers, business analysts, operations team members, and consultants seeking a baseline understanding of testing AI-based systems.
Objectives
- Understand the fundamentals of AI and machine learning and their impact on software testing practices.
- Identify AI-specific quality characteristics such as transparency, interpretability, and fairness, and apply them in testing strategies.
- Design effective test approaches for non-deterministic and probabilistic behaviour in AI-based systems.
- Evaluate data quality, labelling, and partitioning to ensure reliable model performance and testing outcomes.
- Apply AI techniques to support testing activities, including defect prediction, test case generation, and regression optimisation.
- Develop skills to assess risks, ethical considerations, and compliance requirements when testing AI-enabled products.
Outline
Introduction to AI in testing
Definitions of AI and the AI effect
Narrow, general, and super AI distinctions
AI-based versus conventional systems
AI technologies overview
AI development frameworks
Hardware considerations for AI-based systems
AI as a Service and use of pre-trained models
Standards, regulations, and governance for AI
Quality characteristics for AI-based systems
Flexibility, adaptability, and autonomy in AI
Evolution and safety considerations
Bias, ethics, and reward hacking risks
Transparency, interpretability, and explainability
Machine learning forms and core workflow
Selecting ML algorithms and avoiding under/overfitting
ML data: preparation, partitioning, and labelling
Training, validation, and test datasets
Dataset quality issues and their effects
Functional performance metrics and confusion matrix
Metrics for classification, regression, and clustering
Limits of functional metrics and benchmark suites
Neural networks: structure and testing considerations
Coverage measures for neural network testing
Specifying AI-based systems and defining test levels
Designing and sourcing test data for AI testing
Testing for automation bias
Documenting AI components for testability
Testing for concept drift over time
Selecting test approaches for probabilistic behaviour
Testing AI-specific quality characteristics
Challenges in testing complex, autonomous systems
Transparency and explainability checks
Test oracles for AI-based systems
Test environments and infrastructure for AI
Using AI for defect analysis and prediction
AI-supported test case generation
Optimising regression suites with AI
Applying AI to UI testing in practice
Price
| Category | Full-Time (Weekdays) | Part-Time (Weeknights) | Part-Time (Weekends) |
|---|---|---|---|
| Days | Monday to Wednesday | Mondays and Tuesdays | Saturdays only |
| Time | 9:30 am to 5:00 pm | 6:00 pm to 9:00 pm | 10:00 am to 5:00 pm |
| Duration | 3 days | 3 weeks | 3 weeks |
| Price | $2199 | $2199 | $2199 |
Terms & Conditions
The supply of this course is governed by our terms and conditions. Please read them carefully before enrolling, as enrolment is conditional on acceptance of these terms and conditions. Proposed course dates are given, course runs subject to availability and minimum registrations.
Frequently Asked Questions (FAQ's)
Who should take the ISTQB Certified Tester AI Testing Course (CT-AI)?
The course is designed for testing and quality professionals, data analysts and developers working with AI-based systems or using AI to support testing. It also suits managers and business analysts who need a structured understanding of AI testing concepts.
Do I need the ISTQB Foundation Level before attempting the CT-AI certification?
Yes, the ISTQB Certified Tester Foundation Level certificate is required to obtain the CT-AI certification. You can attend the course beforehand if you’re preparing to meet this requirement.
Is coding experience required to benefit from the CT-AI course?
Coding experience is not mandatory; the emphasis is on testing concepts and practices. A basic understanding of software development and data handling will help you apply the methods confidently.
How does CT-AI differ from general AI training?
CT-AI focuses specifically on testing AI-based systems and applying AI to testing tasks, aligning content to the ISTQB syllabus and exam objectives. General AI courses typically emphasise building models rather than testing them.
Our Partnership
We deliver the ISTQB Certified Tester AI Testing (CT-AI) course in collaboration with a Pearson Authorised Training Centre. This partnership ensures learners receive high-quality, exam-aligned instruction focused on testing AI-based systems and applying AI techniques to support software testing. The course covers key areas such as machine learning fundamentals, AI-specific quality characteristics, test design for non-deterministic behaviour, and the use of AI in defect prediction and test optimisation. Designed for professionals working in complex and evolving software environments, this training helps participants build the analytical and technical skills needed to evaluate AI-enabled products and contribute to responsible AI delivery.
$112,000
Average annual salary for AI Testing and Quality Assurance professionals in Australia (reflecting strong demand for AI-related skills).
78%
Employers report that AI testing knowledge is a preferred or required skill for roles involving AI-based systems.
11.5%
Year-on-year growth in job opportunities for professionals with AI testing and machine learning quality expertise.
95,000+
Active ISTQB AI Testing certification holders worldwide, demonstrating global recognition and adoption.
5,200+
Australian companies seeking or employing professionals with AI testing and quality assurance skills.
97%
Student satisfaction rate from our AI Testing training programs.
Our Accreditations
