For those in the software testing field, the rise of Artificial Intelligence (AI) presents a unique opportunity to upskill and specialize. The International Software Testing Qualifications Board (ISTQB) offers the Certified Tester AI Testing (CT-AI) certification to meet this growing need. The certification will enhance your knowledge of artificial intelligence and deep learning, focusing on testing AI-driven systems and leveraging AI for testing purposes. This blog post will explain everything you will need to know about this certification.
Who is This Certification For?
The ISTQB AI Tester certification caters to a broad audience involved in testing AI-based systems, including:
- Testers
- Test analysts
- Data analysts
- Test engineers
- Test consultants
- Test managers
- User acceptance testers
- Software developers
Additionally, it benefits individuals seeking a foundational understanding of AI-based systems testing, such as:
- Project managers
- Quality managers
- Software development managers
- Business analysts
- Operations team members
- IT directors
- Management consultants
Certification Overview
Prerequisites:
The ISTQB Foundation Level certificate: Certified Tester Foundation Level (CTFL) v4.0 is a prerequisite for taking the CT-AI exam.
What Will You Need to Study?
The syllabus extensively covers the testing of AI-based systems and the use of AI in testing. It is organized into eleven chapters, covering topics such as:
- Introduction to AI
- Quality Characteristics for AI-Based Systems
- Machine Learning (ML) – Overview, Data, and Functional Performance Metrics
- ML – Neural Networks and Testing
- Testing AI-Based Systems Overview and AI-Specific Quality Characteristics
- Methods and Techniques for the Testing of AI-Based Systems
- Test Environments for AI-Based Systems
- Using AI for Testing
You can download the syllabus from:
ISTQB Certified Tester – Foundation Level Syllabus v4.0
Learning Objectives:
The four learning levels for the ISTQB AI Testing certification are K1 (remembering keywords), K2 (understanding concepts and relationships), K3 (applying knowledge to solve problems), and K4 (analyzing information and concluding).
Hands-On Competencies:
The certification includes hands-on objectives to develop practical skills, such as building and evaluating ML models, using explainability tools, and applying various testing techniques.
