ISTQB Certified Tester AI Testing (CT-AI) Training Course
Length
3 days
Price
$2199
Cities
Melbourne, Sydney, Brisbane, Adelaide, Canberra, Perth
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Why Choose This Course
The ISTQB AI Tester CT-AI course is instructor-led and aligned to the ISTQB Certified Tester AI Testing syllabus. It focuses on testing AI-based systems and using AI to support software testing, with coverage mapped to machine learning fundamentals, AI-specific quality characteristics, and practical test design for non-deterministic behaviour.
Learners explore how AI-based systems differ from conventional systems, how data quality and labelling influence model outcomes, and how to select meaningful performance metrics for classification, regression and clustering. The syllabus also addresses transparency and explainability, bias and ethics, and organisational adoption considerations so participants can contribute effectively to test strategies for AI-enabled products.
Training is available to professionals across Melbourne, Sydney, Brisbane, Adelaide, Canberra and Perth via virtual and classroom modalities offered by established Australian training providers. The emphasis is on exam-aligned content and hands-on practice to build workplace-ready skills without making unrealistic promises. A certificate of course attendance is included.
Prerequisites
Candidates can achieve this certification by passing the following exam(s).
ISTQB Certified Tester AI Testing (CT-AI).
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
- Explain how AI-based systems differ from conventional systems and the implications for testing.
- Identify and describe AI technologies, development frameworks and common machine learning forms.
- Analyse AI-specific quality characteristics, including transparency, interpretability, explainability, autonomy, evolution and safety.
- Prepare and manage datasets in the ML workflow; understand training, validation and test splits, labelling and dataset quality issues.
- Select and interpret functional performance metrics for classification, regression and clustering; understand metric limitations.
- Design and execute tests for AI-based components and integrated systems, addressing non-determinism and probabilistic behaviour.
- Recognise and mitigate bias (algorithmic, sample, inappropriate) and automation bias in AI-based systems.
- Apply approaches for concept drift detection and documentation of AI components.
- Use AI to support testing activities such as defect analysis, test case generation and regression suite optimisation.
- Contribute to test strategy and recognise test infrastructure needs for AI testing.
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.
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 |
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
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 AI Tester CT-AI course?
Do I need a prior certification?
Is the course available across major Australian cities?
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
