Istqb Ai Testing Prep Course With 1000+ Quizzes

Master AI Testing: Comprehensive Preparation for ISTQB CT-AI Certification with Hands-On Practice and Mock Exams

What you'll learn

Core Concepts of AI Testing: Understand the principles of AI-based systems and how they differ from traditional software systems.

Testing Techniques for AI Systems: Learn advanced methods like metamorphic testing, A/B testing, and testing machine learning models.

Managing AI-Specific Quality Challenges: Address key issues such as data bias, transparency, and safety in AI applications.

ISTQB CT-AI Exam Preparation: Master all syllabus topics, including practical knowledge required for the certification exam.

Hands-On Skills in AI Testing: Develop practical expertise in testing neural networks, handling training data, and ensuring AI quality standards.

Effective Test Strategies for AI: Build strategies to test and validate AI systems in real-world scenarios.

Requirements

Curiosity About AI: Ideal for those intrigued by how AI works and impacts systems.

Basic Testing Knowledge: Familiarity with test cases and testing concepts is beneficial.

Commitment to Practice: Be prepared for extensive practice with 1000+ quizzes.

Confidence in AI Testing: Build the skills to effectively manage and test AI systems.

Description

Course Description for ISTQB Certified Tester AI Testing (CT-AI): 5 Hours, 1000 Questions, 5 Mock ExamsPrepare effectively for the ISTQB Certified Tester AI Testing (CT-AI) certification with a comprehensive course designed to ensure your success.Key Features:Comprehensive Coverage of the ISTQB CT-AI SyllabusThis course thoroughly covers all examinable chapters, from AI fundamentals to advanced topics such as neural networks, ensuring you are well-prepared for the certification.1000 Practice QuestionsGain extensive practice with 1000 questions tailored to the cognitive levels (K1-K4) required for the exam. These questions are designed to enhance your understanding and test readiness.5 Full-Length Mock ExamsSimulate the exam environment with five comprehensive mock exams. Each mock exam is accompanied by detailed explanations to clarify any uncertainties and solidify your understanding.Hands-On Learning FocusBuild practical skills by exploring key topics such as testing neural networks, managing training data, and addressing quality concerns like bias, transparency, and safety.5 Hours of ContentEfficiently designed to deliver valuable knowledge in a manageable timeframe, this course provides concise yet thorough preparation for the CT-AI certification.Learning Objectives:Differentiate between AI-based and conventional systems with confidence.Test machine learning models while addressing critical quality aspects.Navigate and resolve issues related to bias, transparency, and safety in AI systems.Apply advanced testing techniques, including metamorphic and A/B testing.Leverage AI capabilities to enhance testing processes effectively.This course is a well-structured pathway to mastering AI testing concepts and achieving certification success. Enroll now and take a step closer to advancing your expertise in AI testing.

Overview

Section 1: 1 Introduction to AI

Lecture 1 1.1 Definition of AI and AI Effect

Lecture 2 1.2 Narrow, General and Super AI

Lecture 3 1.3 AI-Based and Conventional Systems.

Lecture 4 1.4 AI Technologies

Lecture 5 1.5 AI Development Frameworks

Lecture 6 1.6 Hardware for AI-Based Systems

Lecture 7 1.7 AI as a Service (AIaaS)

Lecture 8 1.8 Pre-Trained Models

Lecture 9 1.9 Standards, Regulations and AI

Section 2: 2 Quality Characteristics for AI-Based Systems

Lecture 10 2.1 Flexibility and Adaptability

Lecture 11 2.2 Autonomy

Lecture 12 2.3 Evolution

Lecture 13 2.4 Bias

Lecture 14 2.5 Ethics

Lecture 15 2.6 Side Effects and Reward Hacking

Lecture 16 2.7 Transparency, Interpretability and Explainability

Lecture 17 2.8 Safety and AI

Section 3: 3 Machine Learning (ML)

Lecture 18 3.1 Forms of ML

Lecture 19 3.2 ML Workflow

Lecture 20 3.3 Selecting a Form of ML

Lecture 21 3.4 Factors involved in ML Algorithm Selection

Lecture 22 3.5 Overfitting and Underfitting

Section 4: 4 ML - Data

Lecture 23 4.1 Data Preparation as part of the ML Workflow

Lecture 24 4.2 Training, Validation and Test Datasets in the ML Workflow

Lecture 25 4.3 Dataset Quality Issues

Lecture 26 4.4 Data quality and its effect on the ML model

Lecture 27 4.5 Data Labelling for Supervised Learning

Section 5: 5 ML Functional Performance Metrics

Lecture 28 5.1 Confusion Matrix

Lecture 29 5.2 Additional ML Functional Performance Metrics for Classification, Regression

Lecture 30 5.3 Limitations of ML Functional Performance Metrics

Lecture 31 5.4 Selecting ML Functional Performance Metrics

Lecture 32 5.5 Benchmark Suites for ML

Section 6: 6 ML - Neural Networks and Testing

Lecture 33 6.1 Neural Networks

Lecture 34 6.2 Coverage Measures for Neural Networks

Section 7: 7 Testing AI-Based Systems Overview

Lecture 35 7.1 Specification of AI-Based Systems

Lecture 36 7.2 Test Levels for AI-Based Systems

Lecture 37 7.3 Test Data for Testing AI-Based Systems

Lecture 38 7.4 Testing for Automation Bias in AI-Based Systems

Lecture 39 7.5 Documenting an ML Model

Lecture 40 7.6 Testing for Concept Drift

Lecture 41 7.7 Selecting a Test Approach for an ML System

Section 8: 8 Testing AI-Specific Quality Characteristics

Lecture 42 8.1 Challenges Testing Self-Learning Systems

Lecture 43 8.2 Testing Autonomous AI-Based Systems

Lecture 44 8.3 Testing for Algorithmic, Sample and Inappropriate Bias

Lecture 45 8.4 Challenges Testing Probabilistic and Non-Deterministic AI-Based Systems

Lecture 46 8.5 Challenges Testing Complex AI-based Systems

Lecture 47 8.6 Testing AI Transparency & Explainability

Lecture 48 8.7 Test Oracles for AI-Based Systems

Lecture 49 8.8 Test Objectives and Acceptance Criteria

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

Lecture 50 9.1 Adversarial Attacks and Data Poisoning

Lecture 51 9.2 Pairwise Testing

Lecture 52 9.3 Back-to-Back Testing

Lecture 53 9.4 A/B Testing

Lecture 54 9.5 Metamorphic Testing (MT)

Lecture 55 9.6 Experience-Based Testing of AI-Based Systems

Lecture 56 9.7 Selecting Test Techniques for AI-Based Systems

Section 10: 10 Test Environments for AI-Based Systems

Lecture 57 10.1 Test Environments for AI-Based Systems

Lecture 58 10.2 Virtual Test Environments for Testing AI-Based Systems

Section 11: 11 Using AI for Testing

Lecture 59 11.1 AI Technologies for Testing

Lecture 60 11.2 Using AI to Analyze Reported Defects

Lecture 61 11.3 Using AI for Test Case Generation

Lecture 62 11.4 Using AI for the Optimization of Regression Test Suites

Lecture 63 11.5 Using AI for Defect Prediction

Lecture 64 11.6 Using AI for Testing User Interfaces

Section 12: ISTQB Certified Tester AI Testing (CT-AI) Mock Exams

Aspiring AI Testing Specialists: Perfect for those looking to build a strong foundation in understanding and testing AI systems.,Experienced Software Testers: Ideal for professionals transitioning from traditional testing to AI-focused testing practices.,Technology Enthusiasts: Designed for individuals interested in topics like data bias, transparency, and AI model quality.,Professionals Seeking a Competitive Edge: Gain expertise in AI testing methodologies to stand out in meetings and discussions.,ISTQB Certification Candidates: Tailored for those preparing for the CT-AI exam to achieve an internationally recognized certification.