{"product_id":"the-istqb®-certified-tester-ai-testing-ct-ai","title":"The ISTQB® Certified Tester AI Testing (CT-AI)","description":"\u003cdiv\u003e\u003cp\u003eThe ISTQB® Certified Tester AI Testing (CT-AI) certification is a globally recognized credential designed for professionals involved in testing artificial intelligence (AI) systems. This certification provides foundational knowledge of AI concepts, technologies, and the specific challenges associated with testing AI-driven applications. This course covers fundamental AI concepts, testing methodologies for AI systems, unique challenges such as Perspective Awareness and explainability, key quality characteristics like performance and security, and introduces tools and techniques for effective AI system assessment.\u003c\/p\u003e\u003c\/div\u003e\u003cdiv\u003e\n\u003ch3\u003eThe ISTQB® Certified Tester AI Testing (CT-AI) Benefits\u003c\/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eIn this course, you will:\u003c\/strong\u003e\u003c\/p\u003e\n\u003cul type=\"disc\"\u003e\n\u003cli\u003eUnderstand the current state and expected trends of AI\u003c\/li\u003e\n\u003cli\u003eExperience the implementation and testing of a ML model and recognize where testers can best influence its quality\u003c\/li\u003e\n\u003cli\u003eUnderstand the challenges associated with testing AI-Based systems, such as their self-learning capabilities, Perspective Awareness, ethics, complexity, non-determinism, transparency and explainability\u003c\/li\u003e\n\u003cli\u003eContribute to the test strategy for an AI-Based system\u003c\/li\u003e\n\u003cli\u003eDesign and execute test cases for AI-based systems\u003c\/li\u003e\n\u003cli\u003eRecognize the special requirements for the test infrastructure to support the testing of AI-based systems\u003c\/li\u003e\n\u003cli\u003eUnderstand how AI can be used to support software testing\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003ePrerequisites\u003c\/strong\u003e\u003c\/p\u003e\n\u003cp\u003eYes, the ISTQB Certified AI Tester certification typically requires candidates to have completed the ISTQB Certified Tester Foundation Level (CTFL) as a prerequisite. This ensures that candidates have a basic understanding of software testing principles before advancing to AI-specific testing concepts.\u003c\/p\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eCertification Information\u003c\/strong\u003e\u003c\/p\u003e\n\u003cp\u003eUpon successfully passing the examination, participants will receive the ISTQB® Certified Tester AI Testing (CT-AI) certification, which is globally recognized and demonstrates expertise in AI testing.\u003c\/p\u003e\n\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/div\u003e\u003cdiv\u003e\u003ch3\u003eThe ISTQB® Certified Tester AI Testing (CT-AI) Training Outline\u003c\/h3\u003e\u003c\/div\u003e\u003cdiv\u003e\n\u003ch4\u003eLearning Objectives\u003c\/h4\u003e\n\u003cp\u003e\u003cb\u003eModule1: Introduction to AI \u003c\/b\u003e\u003c\/p\u003e\n\u003cp\u003e1.1 Definition of AI and AI Effect\u003c\/p\u003e\n\u003cp\u003e1.2 Narrow, General and Super AI\u003c\/p\u003e\n\u003cp\u003e1.3 AI-Based and Conventional Systems\u003c\/p\u003e\n\u003cp\u003e1.4 AI Technologies\u003c\/p\u003e\n\u003cp\u003e1.5 AI Development Frameworks\u003c\/p\u003e\n\u003cp\u003e1.6 Hardware for AI-Based Systems\u003c\/p\u003e\n\u003cp\u003e1.7 AI as a Service (AIaaS)\u003c\/p\u003e\n\u003cp\u003e1.7.1 Contracts for AI as a Service\u003c\/p\u003e\n\u003cp\u003e1.7.2 AIaaS Examples\u003c\/p\u003e\n\u003cp\u003e1.8 Pre-Trained Models\u003c\/p\u003e\n\u003cp\u003e1.8.1 Introduction to Pre-Trained Models\u003c\/p\u003e\n\u003cp\u003e1.8.2 Transfer Learning\u003c\/p\u003e\n\u003cp\u003e1.8.3 Risks of using Pre-Trained Models and Transfer Learning\u003c\/p\u003e\n\u003cp\u003e1.9 Standards, Regulations and AI\u003c\/p\u003e\n\u003cp\u003e \u003c\/p\u003e\n\u003cp\u003e\u003cb\u003eModule 2: Quality Characteristics for AI-Based Systems \u003c\/b\u003e\u003c\/p\u003e\n\u003cp\u003e2.1 Flexibility and Adaptability\u003c\/p\u003e\n\u003cp\u003e2.2 Autonomy\u003c\/p\u003e\n\u003cp\u003e2.3 Evolution\u003c\/p\u003e\n\u003cp\u003e2.4 Perspective Awareness \u003c\/p\u003e\n\u003cp\u003e2.5 Ethics\u003c\/p\u003e\n\u003cp\u003e2.6 Side Effects and Reward Hacking\u003c\/p\u003e\n\u003cp\u003e2.7 Transparency, Interpretability and Explainability\u003c\/p\u003e\n\u003cp\u003e2.8 Safety and AI\u003c\/p\u003e\n\u003cp\u003e \u003c\/p\u003e\n\u003cp\u003e\u003cb\u003eModule 3: Machine Learning (ML) – Overview \u003c\/b\u003e\u003c\/p\u003e\n\u003cp\u003e3.1 Forms of ML\u003c\/p\u003e\n\u003cp\u003e3.1.1 Supervised Learning\u003c\/p\u003e\n\u003cp\u003e3.1.2 Unsupervised Learning\u003c\/p\u003e\n\u003cp\u003e3.1.3 Reinforcement Learning\u003c\/p\u003e\n\u003cp\u003e3.2 ML Workflow\u003c\/p\u003e\n\u003cp\u003e3.3 Selecting a Form of ML\u003c\/p\u003e\n\u003cp\u003e3.4 Factors Involved in ML Algorithm Selection\u003c\/p\u003e\n\u003cp\u003e3.5 Overfitting and Underfitting\u003c\/p\u003e\n\u003cp\u003e3.5.1 Overfitting\u003c\/p\u003e\n\u003cp\u003e3.5.2 Underfitting\u003c\/p\u003e\n\u003cp\u003e3.5.3 Hands-On Exercise: Demonstrate Overfitting and Underfitting\u003c\/p\u003e\n\u003cp\u003e\u003cb\u003e \u003c\/b\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cb\u003eModule 4: ML - Data \u003c\/b\u003e\u003c\/p\u003e\n\u003cp\u003e4.1 Data Preparation as Part of the ML Workflow\u003c\/p\u003e\n\u003cp\u003e4.1.1 Challenges in Data Preparation\u003c\/p\u003e\n\u003cp\u003e4.1.2 Hands-On Exercise: Data Preparation for ML, Validation and Test Datasets in the ML Workflow\u003c\/p\u003e\n\u003cp\u003e4.2.1 Hands-On Exercise: Identify Training and Test Data and Create an ML Model\u003c\/p\u003e\n\u003cp\u003e4.3 Dataset Quality Issues\u003c\/p\u003e\n\u003cp\u003e4.4 Data Quality and its Effect on the ML Model\u003c\/p\u003e\n\u003cp\u003e4.5 Data Labelling for Supervised Learning\u003c\/p\u003e\n\u003cp\u003e4.5.1 Approaches to Data Labelling\u003c\/p\u003e\n\u003cp\u003e4.5.2 Mislabeled Data in Datasets\u003c\/p\u003e\n\u003cp\u003e\u003cb\u003e \u003c\/b\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cb\u003eModule 5: ML Functional Performance Metrics \u003c\/b\u003e\u003c\/p\u003e\n\u003cp\u003e5.1 Confusion Matrix\u003c\/p\u003e\n\u003cp\u003e5.2 Additional ML Functional Performance Metrics for Classification, Regression and Clustering\u003c\/p\u003e\n\u003cp\u003e5.3 Limitations of ML Functional Performance Metrics\u003c\/p\u003e\n\u003cp\u003e5.4 Selecting ML Functional Performance Metrics\u003c\/p\u003e\n\u003cp\u003e5.4.1 Hands-On Exercise: Evaluate the Created ML Model\u003c\/p\u003e\n\u003cp\u003e5.5 Benchmark Suites for ML\u003c\/p\u003e\n\u003cp\u003e \u003c\/p\u003e\n\u003cp\u003e\u003cb\u003eModule 6: ML - Neural Networks and Testing\u003c\/b\u003e\u003c\/p\u003e\n\u003cp\u003e6.1 Neural Networks\u003c\/p\u003e\n\u003cp\u003e6.1.1 Hands-On Exercise: Implement a Simple Perceptron\u003c\/p\u003e\n\u003cp\u003e6.2 Coverage Measures for Neural Networks\u003c\/p\u003e\n\u003cp\u003e \u003c\/p\u003e\n\u003cp\u003e\u003cb\u003eModule 7: Testing AI-Based Systems Overview \u003c\/b\u003e\u003c\/p\u003e\n\u003cp\u003e7.1 Specification of AI-Based Systems\u003c\/p\u003e\n\u003cp\u003e7.2 Test Levels for AI-Based Systems\u003c\/p\u003e\n\u003cp\u003e7.2.1 Input Data Testing\u003c\/p\u003e\n\u003cp\u003e7.2.2 ML Model Testing\u003c\/p\u003e\n\u003cp\u003e7.2.3 Component Testing\u003c\/p\u003e\n\u003cp\u003e7.2.4 Component Integration Testing\u003c\/p\u003e\n\u003cp\u003e7.2.5 System Testing\u003c\/p\u003e\n\u003cp\u003e7.2.6 Acceptance Testing\u003c\/p\u003e\n\u003cp\u003e7.3 Test Data for Testing AI-based Systems\u003c\/p\u003e\n\u003cp\u003e7.4 Testing for Automation Perspective Awareness in AI-Based Systems\u003c\/p\u003e\n\u003cp\u003e7.5 Documenting an AI Component\u003c\/p\u003e\n\u003cp\u003e7.6 Testing for Concept Drift\u003c\/p\u003e\n\u003cp\u003e7.7 Selecting a Test Approach for an ML System\u003c\/p\u003e\n\u003cp\u003e \u003c\/p\u003e\n\u003cp\u003e\u003cb\u003eModule 8: Testing AI-Specific Quality Characteristics \u003c\/b\u003e\u003c\/p\u003e\n\u003cp\u003e8.1 Challenges Testing Self-Learning Systems\u003c\/p\u003e\n\u003cp\u003e8.2 Testing Autonomous AI-Based Systems\u003c\/p\u003e\n\u003cp\u003e8.3 Testing for Algorithmic, Sample and Inappropriate Perspective Awareness \u003c\/p\u003e\n\u003cp\u003e8.4 Challenges Testing Probabilistic and Non-Deterministic AI-Based Systems\u003c\/p\u003e\n\u003cp\u003e8.5 Challenges Testing Complex AI-Based Systems\u003c\/p\u003e\n\u003cp\u003e8.6 Testing the Transparency, Interpretability and Explainability of AI-Based Systems\u003c\/p\u003e\n\u003cp\u003e8.6.1 Hands-On Exercise: Model Explainability\u003c\/p\u003e\n\u003cp\u003e8.7 Test Oracles for AI-Based Systems\u003c\/p\u003e\n\u003cp\u003e8.8 Test Objectives and Acceptance Criteria\u003c\/p\u003e\n\u003cp\u003e \u003c\/p\u003e\n\u003cp\u003e\u003cb\u003eModule 9:  Methods and Techniques for the Testing of AI-Based Systems \u003c\/b\u003e\u003c\/p\u003e\n\u003cp\u003e9.1 Adversarial Attacks and Data Poisoning\u003c\/p\u003e\n\u003cp\u003e9.1.1 Adversarial Attacks\u003c\/p\u003e\n\u003cp\u003e9.1.2 Data Poisoning\u003c\/p\u003e\n\u003cp\u003e9.2 Pairwise Testing\u003c\/p\u003e\n\u003cp\u003e9.2.1 Hands-On Exercise: Pairwise Testing....\u003c\/p\u003e\n\u003cp\u003e9.3 Back-to-Back Testing\u003c\/p\u003e\n\u003cp\u003e9.4 A\/B Testing\u003c\/p\u003e\n\u003cp\u003e9.5 Metamorphic Testing (MT)\u003c\/p\u003e\n\u003cp\u003e9.5.1 Hands-On Exercise: Metamorphic Testing\u003c\/p\u003e\n\u003cp\u003e9.6 Experience-Based Testing of AI-Based Systems\u003c\/p\u003e\n\u003cp\u003e9.6.1 Hands-On Exercise: Exploratory Testing and Exploratory Data Analysis (EDA)\u003c\/p\u003e\n\u003cp\u003e 9.7 Selecting Test Techniques for AI-Based Systems\u003c\/p\u003e\n\u003cp\u003e\u003cb\u003e \u003c\/b\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cb\u003eModule 10: Test Environments for AI-Based Systems\u003c\/b\u003e\u003c\/p\u003e\n\u003cp\u003e10.1 Test Environments for AI-Based Systems\u003c\/p\u003e\n\u003cp\u003e10.2 Virtual Test Environments for Testing AI-Based Systems\u003c\/p\u003e\n\u003cp\u003e \u003c\/p\u003e\n\u003cp\u003e\u003cb\u003eModule 11: Using AI for Testing \u003c\/b\u003e\u003c\/p\u003e\n\u003cp\u003e11.1 AI Technologies for Testing\u003c\/p\u003e\n\u003cp\u003e11.1.1 Hands-On Exercise:The Use of AI in Testing\u003c\/p\u003e\n\u003cp\u003e11.2 Using AI to Analyze Reported Defects\u003c\/p\u003e\n\u003cp\u003e11.3 Using AI for Test Case Generation\u003c\/p\u003e\n\u003cp\u003e11.4 Using AI for the Optimization of Regression Test Suites\u003c\/p\u003e\n\u003cp\u003e11.5 Using AI for Defect Prediction\u003c\/p\u003e\n\u003cp\u003e11.5.1 Hands-On Exercise: Build a Defect Prediction System\u003c\/p\u003e\n\u003cp\u003e11.6 Using AI for Testing User Interfaces\u003c\/p\u003e\n\u003cp\u003e11.6.1 Using AI to Test Through the Graphical User Interface (GUI)\u003c\/p\u003e\n\u003cp\u003e11.6.2 Using AI to Test the GUI.\u003c\/p\u003e\n\u003c\/div\u003e","brand":"Learning Tree","offers":[{"title":"267A82US \/ 2026-07-07T09:00:00 \/ Herndon, VA","offer_id":47534202716379,"sku":"US-1288-IL","price":2512.0,"currency_code":"USD","in_stock":true},{"title":"271B27US \/ 2027-01-12T09:00:00 \/ Herndon, VA","offer_id":48216561647835,"sku":"US-1288-IL","price":2512.0,"currency_code":"USD","in_stock":true}],"url":"https:\/\/learningtreeinternational-dirinfosec-hhs.myshopify.com\/products\/the-istqb%c2%ae-certified-tester-ai-testing-ct-ai","provider":"Learning Tree International","version":"1.0","type":"link"}