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3 days course

ISTQB Certified Tester - AI Testing (ISTQB CT-AI) v2.0

In this certification course, you will gain a fundamental understanding of Artificial Intelligence, how to test and quality assure AI-based solutions, and how machine learning systems can be tested throughout the entire development lifecycle.

>> Available in Danish <<

The course has been updated to ISTQB® Certified Tester AI Testing v2.0, which replaces the previous CT-AI v1.0 syllabus. The new syllabus has a clearer focus on testing AI-based systems, particularly machine learning, and now also includes key concepts and testing approaches within generative AI and large language models.

Prerequisites

To achieve the CT-AI certification, candidates must hold the ISTQB® Certified Tester Foundation Level (CTFL) certificate, but now a prerequisite to attend the course.
 
Furthermore, we recommend that you have:
  • Knowledge and understanding of programming language – Java/Python/R
  • Knowledge and understanding of statistics
  • Experience with software development and testing
 

Target audience

ISTQB Certified Tester AI Testing v2.0 is for anyone with an interest in Artificial Intelligence who wants a fundamental understanding of how AI-based systems and machine learning systems are tested and quality assured.
 
The course is particularly relevant for testers, test managers, test analysts, test engineers, software developers, data analysts, project managers, quality assurance professionals, business analysts, and others who work with or need to understand the quality of AI-based solutions.
 
Outcome
  • You will be able to contribute to the test strategy for AI-based systems and machine learning systems
  • You will be able to contribute to the design and execution of test cases for AI-based systems
  • You will gain an understanding of challenges related to testing AI-based systems, such as non-deterministic behavior, data dependency, probabilistic results, bias, transparency, robustness, and models that evolve over time
  • You will gain an understanding of generative AI and testing large language models, including exploratory testing and red teaming
  • You will gain an understanding of input data testing, including data quality, data representativeness, bias, data pipelines, and dataset validation
  • You will learn about testing machine learning models, including ML performance metrics, adversarial testing, metamorphic testing, drift testing, A/B testing, and back-to-back testing
  • You will gain an understanding of testing in relation to the development, deployment, and operation of machine learning systems
 

Content

The course is an official ISTQB-course and covers:
  • Introduction to Artificial Intelligence
    Including the difference between AI-based and conventional systems, narrow AI, general AI, super AI, generative AI, ML frameworks, hosting, hardware, and relevant standards and regulations including EU AI act.
  • Quality characteristics for AI-based systems
    Including AI-specific quality characteristics, functional correctness, adaptability, transparency, robustness, controllability, safety, ethics, and acceptance criteria.
  • Machine Learning (ML)
    Including supervised, unsupervised, and reinforcement learning, the ML workflow, data preparation, datasets, pretrained models, fine-tuning, retrieval-augmented generation, ML performance metrics, and neural networks.
  • Testing AI-based systems
    Including challenges in testing AI, locked and adaptive AI systems, statistical testing, test oracles, risk-based testing, test levels, and testing generative AI and large language models.
  • Input Data Testing for Machine Learning Systems
    Including data quality, data-related risks, bias, data representativeness, data pipeline testing, dataset validation, and label correctness.
  • Model Testing for Machine Learning Systems
    Including ML model risks, performance testing, adversarial testing, metamorphic testing, overfitting, underfitting, drift testing, A/B testing, and back-to-back testing.
  • Machine Learning Development Testing
    Including testing during development and deployment, as well as monitoring and maintaining model performance in production.

Format

This is 3 intensive course days, where you will be taught in the subjects included in the exam. The course contains both a theoretical review, practical exercises, and discussion. There will be a high degree of participant involvement.
 
Prior to course start, expect to prepare by reading approximately 10 hours from the curriculum. During the course you should expect 2 hours of homework every day after class.
 
The course materials and exam are in English.
 

Certification

The exam is an official ISTQB-exam, and it is taken online.

Questions: 40 multiple choice, and no assistance is permitted.

Duration: 1 hour

Language: English. If English is not your native language, you can receive an additional 15 minutes for the exam.

Format: You must answer at least 29 out of 44 points to pass the exam. K3 (apply) questions are worth 2 points; K1/K2 are worth 1 point.

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