Istqb Certified Tester Ai Testing-Full Training + Mock Exam

Complete ISTQB AI Testing Course: Full Exam Prep, Certification Training and a Sample Exam

What you'll learn

Understand fundamental AI concepts, history, and real-world applications.

Learn key quality attributes such as adaptability, transparency, and performance in AI systems.

Explore different ML types, workflows, and considerations for selecting ML models.

Understand data preprocessing, bias, quality challenges, and data handling in AI systems.

Learn evaluation metrics like accuracy, precision, recall, and F1-score for ML models.

Gain insights into neural networks, coverage measures, and challenges in testing deep learning models.

Examine key test levels, risks, and methodologies for validating AI systems.

Explore AI-specific testing techniques focusing on bias, explainability, and robustness.

Learn various testing methods, including pairwise testing, metamorphic testing, and back-to-back testing.

Understand the infrastructure and tools required for AI system testing.

Discover how AI can automate and enhance software testing techniques.

Requirements

No programming experience required. Detailed guides provided for everything you need to know

Description

This ISTQB Certified Tester AI Testing course is a complete training program designed to help professionals understand and test AI-based systems effectively. Covering the ISTQB syllabus, this course provides a structured approach to AI fundamentals, machine learning, quality characteristics, and AI testing methodologies.Course Outline:Chapter 1: Introduction to AI – Understand AI concepts, types, and applications.Chapter 2: Quality Characteristics for AI-Based Systems – Explore AI-specific attributes like transparency, fairness, and robustness.Chapter 3: Machine Learning (ML) Overview – Learn ML fundamentals, supervised and unsupervised learning.Chapter 4: ML Data – Understand data preprocessing, feature engineering, and dataset quality.Chapter 5: ML Functional Performance Metrics – Explore key evaluation techniques.Chapter 6: ML Neural Networks and Testing – Learn about deep learning and neural networks.Chapter 7: Testing AI-Based Systems Overview – Understand AI testing challenges.Chapter 8: Testing AI-Specific Quality Characteristics – Focus on explainability, bias, and safety testing.Chapter 9: Methods and Techniques for AI Testing – Learn testing strategies.Chapter 10: Test Environments for AI-Based Systems – Explore test automation and tools.Chapter 11: Using AI for Testing – Learn how AI can enhance software testing.This course is ideal for QA professionals, testers, and AI practitioners preparing for the ISTQB AI Testing certification with practical examples and exam-focused insights.

Overview

Section 1: Introduction and Chapter 1

Lecture 1 Introduction to the course

Lecture 2 Chapter 1: Introduction to Artificial Intelligence (AI)

Lecture 3 AI Technologies

Lecture 4 Hardware for AI-Based Systems

Lecture 5 AI as a Service (AIaaS)

Lecture 6 Pre-Trained models and Transfer Learning

Lecture 7 Standards. Regulations and AI

Section 2: Chapter 2: Quality Characteristics for AI-Based Systems

Lecture 8 Overview of AI Specific Characteristics

Lecture 9 Flexibility and Adaptability

Lecture 10 Autonomy and Evolution

Lecture 11 Bias

Lecture 12 Types of Bias: Algorthmic and Sample

Lecture 13 Ethics, Side-effects and Reward Hacking

Lecture 14 Transparency, Interpretability, Explainability and Safety

Section 3: Chapter 3: Machine Learning (ML) – Overview

Lecture 15 Forms of Machine Learning

Lecture 16 Machine Learning Workflow

Section 4: Chapter 4: ML Data

Lecture 17 Data Preparation and Split

Lecture 18 Exercise, Dataset Quality Issues and Effects, Data Labelling

Section 5: Chapter 5: ML Functional Performance Metrics

Lecture 19 Confusion Matrix and Accuracy

Lecture 20 Precision,Recall, F1-Score and Exercise

Lecture 21 Functional Performance Metrics: Limitations, Selections and Exercise

Lecture 22 Benchmark Suites for ML

Section 6: Chapter 6: ML – Neural Networks and Testing

Lecture 23 Overview of Neural Networks

Lecture 24 Exercise - Implement a Simple Perceptron and Training a Neural Network

Lecture 25 Coverage Measures and Criteria for Neural Networks

Section 7: Chapter 7: Testing AI-Based Systems Overview

Lecture 26 Test Levels for AI-Based Systems

Lecture 27 Automation Bias,Documentation, Cconcept Drift,Exercise, Risks and Mitigations

Section 8: Chapter 8: Testing AI-Specific Quality Characteristics

Lecture 28 Challenges Testing Self-Learning Systems

Lecture 29 Testing Autonomous AI-Based Systems, Algorithmic, Sample, and Inappropriate Bias

Lecture 30 Challenges Testing Probabilistic and Non-Deterministic AI-Based Systems

Lecture 31 Challenges Testing Complex AI-Based Systems

Lecture 32 Test Oracles for AI-Based Systems

Lecture 33 Test Objectives and Acceptance Criteria

Lecture 34 Testing the Transparency, Interpretability, and Explainability

Section 9: Chapter 9: Methods and Techniques for the Testing of AI-Based Systems

Lecture 35 Adversarial attacks and Data Poisoning

Lecture 36 Pairwise Testing

Lecture 37 Back-To-Back Testing and A/B Testing

Lecture 38 Metamorphic Testing

Lecture 39 Experience-Based Testing

Lecture 40 Exercise: Exploratory Testing and Exploratory Data Analysis (EDA)

Lecture 41 Selecting Test Techniques for AI-Based Systems

Section 10: Chapter 10: Test Environments for AI-Based Systems

Lecture 42 Test Environments for AI-Based Systems

Lecture 43 Benefits of Virtual Test Environments

Section 11: Chapter 11: Using AI for Testing

Lecture 44 Power of AI in Testing

Lecture 45 Using AI to Analyze Reported Defects

Lecture 46 Using AI for Test Case Generation

Lecture 47 Using AI for the Optimization of Regression Test Suites

Lecture 48 Exercise: Build a Defect Prediction System

Lecture 49 Using AI for Defect Prediction and Testing User Interfaces

Section 12: Final Sample Exam

This course is designed for professionals working with AI-based systems and AI-driven testing, including but not limited to: Testing & QA Professionals – Testers, test analysts, test engineers, test consultants, test managers, and user acceptance testers. Data & AI Specialists – Data analysts and professionals working with AI models. Software Development Teams – Developers, software engineers, and technical architects involved in AI testing. Project & Quality Managers – Business analysts, quality managers, and project managers looking to understand AI testing methodologies. IT & Operations Leaders – IT directors, operations team members, and management consultants involved in AI adoption. Prerequisite: Candidates must hold the Certified Tester Foundation Level (CTFL) certification to qualify for this course. This course is ideal for anyone looking to enhance their expertise in testing AI-based systems and leveraging AI for software testing.