ISTQB® Certified Tester - AI Testing (CT-AI)

Price
Net: €
VAT: €

Price
Price on Request

Duration
3 days

For companies and job seekers:
this course is 100% fundable!

Location

Course Language
English

Training Solutions
Online Live

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Intelligent systems make decisions based on data. Quality assurance must therefore systematically test models, training processes, and dynamic behavior. Current expertise in the field of AI testing combines classic testing methodology with data science and modern software architecture.

Key topics

Special features of AI systems in a testing context.
Verification and validation of machine learning models.
Test data management and data integrity.
Explainability, fairness, and bias control.
Testing strategy for adaptive and self-learning systems.
Governance, compliance, and risk management.

Prerequisites
Experience in software testing or quality assurance; understanding of basic development processes and test concepts.

Target audience
QA professionals, test analysts, IT project managers, development teams, data analysts, and those responsible for AI-supported products.

Conclusion
: Structured testing procedures for AI create reliability, traceability, and trust in data-based applications—essential for organizations that want to shape innovation responsibly.

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Course Content

Introduction to Artificial Intelligence

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 deployment of AI models
Machine learning development frameworks
Regulations and Standards for AI

Quality Characteristics for AI-Based Systems

AI-Specific Quality Characteristics
AI and Security
Acceptance Criteria for AI-Based Systems

Introduction to Machine Learning

Various Forms of Machine Learning
The Machine Learning Process
Pre-trained models
Fine-tuning
Retrieval-Augmented Generation

Data for machine learning

Data Preparation Measures

ML Performance Metrics for Classification

Calculation of Performance Metrics for Machine Learning

Neural Networks

Structure and Functioning of a Deep Neural Network
Coverage Metrics for Neural Networks

Testing of AI-Based Systems

Closed 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

Test Generative AI
Red Teaming

Machine Learning Testing Levels and Systems

Testing Levels for Machine Learning Systems.
Risk-Based Testing of Machine Learning Systems.

Validation of Input Data for Machine Learning Systems

Risks Associated with Input Data and Measures to Mitigate Them
Bias Check
Data pipeline check
Data representativeness check
Data restriction test
Label Accuracy Test

Functional Tests for Machine Learning Systems

Risks Associated with Machine Learning Models and Measures to Mitigate Them
Documentation and Validation of Machine Learning Models
Functional performance tests for probabilistic machine learning systems
Adversarial Testing for Machine Learning Systems
Metamorphic Tests
Drift Tests
Tests for overfitting and underfitting
A/B tests
Back-to-back tests

Machine Learning Development and Testing

Risks in Machine Learning Development and How to Mitigate Them.
Testing During the Deployment of Machine Learning Systems.

Your training will be 100% funded

Our funding schemes are available exclusively to participants who live or work in Germany. Funding is not available outside Germany.

For career starters, job seekers, career changers, startups, experienced professionals, those looking to advance their careers, specialists, etc.

The QCG program for everyone in the company

Further information

The education voucher for all job seekers

Further information

Frequently asked questions

What is AI testing in the context of software testing?

AI testing refers to the systematic testing of AI-based systems and machine learning models. The focus is on data quality, model behavior, bias, traceability, risk analysis, and special testing methods for learning systems.

What content is covered in ISTQB AI Testing?

Key topics include the fundamentals of AI and machine learning, testing strategies for AI systems, data validation, bias detection, model evaluation, testing environments, and risks and quality assurance for AI-based applications.

What are the requirements for AI testing?

Basic knowledge of software testing is recommended, as well as ISTQB Foundation certification, ideally. Basic knowledge of data analysis, machine learning, or AI concepts facilitates understanding of the test methods.

For which roles is AI testing particularly relevant?

This topic is relevant for software testers, test managers, QA engineers, data scientists, AI engineers, developers, and those responsible for quality assurance in AI or data-driven projects.

Why is testing becoming increasingly important for AI systems?

AI systems make autonomous decisions and are based on training data. Testing procedures must therefore evaluate data quality, model behavior, fairness, robustness, and transparency in order to reduce risks, wrong decisions, and bias.

What are the special features of testing AI systems?

The focus is on data dependency, non-deterministic results, and learning models. Classic testing methods are expanded to include procedures for data validation, model evaluation, bias analysis, and continuous quality assurance.

What are the advantages of certification in the field of AI testing?

Certification documents expertise in testing strategies for AI-based systems. Organizations benefit from structured quality assurance, while professionals demonstrate competence in a rapidly growing technology field.

What is AI testing used for in companies?

Areas of application include machine learning, such as automated decision-making processes, image recognition, speech processing, and predictive analytics. The goal is reliable quality, secure systems, and traceable AI results.

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