ISTQB Certified Tester Foundation Level AI Testing Training in Bracknell

ISTQB Certified Tester Foundation Level AI Testing Training Course Outline

Module 1: Introduction to AI

• Definition of AI and AI Effect
• Narrow, General and Super AI
• AI-Based and Conventional Systems
• AI Technologies
• AI Development Frameworks
• Hardware for AI-Based Systems
• AI as a Service (AlaaS)
  • Contracts for AI as a Service
  • AlaaS Examples
• Pre-Trained Models
  • Introduction to Pre-Trained Models
  • Transfer Learning
  • Risks of using Pre-Trained Models and Transfer Learning
• Standards, Regulations and AI

Module 2: Quality Characteristics for AI-Based Systems

• Flexibility and Adaptability
• Autonomy
• Evolution
• Bias
• Ethics
• Side Effects and Reward Hacking
• Transparency, Interpretability and Explainability
• Safety and AI

Module 3: Machine Learning (ML)

• Forms of ML
  • Supervised Learning
  • Unsupervised Learning
  • Reinforcement Learning
• ML Workflow
• Selecting a Form of ML
• Factors Involved in ML Algorithm Selection
• Overfitting and Underfitting
  • Overfitting
  • Underfitting
  • Hands-On Exercise: Demonstrate Overfitting and Underfitting

Module 4: ML - Data

• Data Preparation as Part of the ML Workflow
  • Challenges in Data Preparation
  • Hands-On Exercise: Data Preparation for ML
• Training, Validation and Test Datasets in the ML Workflow
  • Hands-On Exercise: Identify Training and Test Data and Create an ML Model
• Dataset Quality Issues
• Data Quality and its Effect on the ML Model
• Data Labelling for Supervised Learning
  • Approaches to Data Labelling
  • Mislabeled Data in Datasets

Module 5: ML Functional Performance Metrics

• Confusion Matrix
• Additional ML Functional Performance Metrics for Classification, Regression and Clustering
• Limitations of ML Functional Performance Metrics
• Selecting ML Functional Performance Metrics
  • Hands-On Exercise: Evaluate the Created ML Model
  • Benchmark Suites for ML

Module 6: ML - Neural Networks and Testing

• Neural Networks
  • Hands-On Exercise: Implement a Simple Perceptron
• Coverage Measures for Neural Networks

Module 7: Testing AI-Based Systems Overview

• Specification of AI-Based Systems
• Test Levels for AI-Based Systems
  • Input Data Testing
  • ML Model Testing
  • Component Testing
  • Component Integration Testing
  • System Testing
  • Acceptance Testing
• Test Data for Testing AI-based Systems
• Testing for Automation Bias in AI-Based Systems
• Documenting an AI Component
• Testing for Concept Drift
• Selecting a Test Approach for an ML System

Module 8: Testing AI-Specific Quality Characteristics

• Challenges Testing Self-Learning Systems
• Testing Autonomous AI-Based Systems
• Testing for Algorithmic, Sample and Inappropriate Bias
• Challenges Testing Probabilistic and Non-Deterministic AI-Based Systems
• Challenges Testing Complex AI-Based Systems
• Testing the Transparency, Interpretability and Explainability of AI-Based Systems
  • Hands-On Exercise: Model Explainability
• Test Oracles for AI-Based Systems
• Test Objectives and Acceptance Criteria

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

• Adversarial Attacks and Data Poisoning
  • Adversarial Attacks
  • Data Poisoning
• Pairwise Testing
  • Hands-On Exercise: Pairwise Testing
• Back-to-Back Testing
• A/B Testing
• Metamorphic Testing (MT)
  • Hands-On Exercise: Metamorphic Testing
• Experience-Based Testing of AI-Based Systems
  • Hands-On Exercise: Exploratory Testing and Exploratory Data Analysis (EDA
• Selecting Test Techniques for AI-Based Systems

Module 10: Test Environments for AI-Based Systems

• Test Environments for AI-Based Systems
• Virtual Test Environments for Testing AI-Based Systems

Module 11: Using AI for Testing

• AI Technologies for Testing
  • Hands-On Exercise: The Use of AI in Testing
• Using AI to Analyse Reported Defects
• Using AI for Test Case Generation
• Using AI for the Optimization of Regression Test Suites
• Using AI for Defect Prediction
  • Hands-On Exercise: Build a Defect Prediction System
• Using AI for Testing User Interfaces
  • Using AI to Test Through the Graphical User Interface
  • Using AI to Test the GUI

Who should attend this ISTQB Certified Tester Foundation Level AI Testing Training Course?

This ISTQB Certified Tester Foundation Level - AI Testing Training Course is ideal for individuals looking to deepen their understanding of testing AI-based systems and machine learning models. It is particularly beneficial for:

• Software Testers
• Test Managers
• AI Enthusiasts
• Automation Testers
• Cybersecurity Testers
• Machine Learning Engineers
• IT Consultants

Prerequisites ISTQB Certified Tester Foundation Level AI Testing Training Course

To take this exam, delegates must first obtain the ISTQB® Foundation Level certificate.

ISTQB Certified Tester Foundation Level AI Testing Training Course Overview

ISTQB is a globally recognized certification body for software testing. It is important as it ensures that individuals possess the required knowledge and skills to perform high-quality software testing, following industry standards. The training benefits organizations by enhancing their testing processes, reducing errors, and improving software quality. For individuals, the training provides a comprehensive understanding of testing principles, methodologies, and tools, empowering them to excel in their roles.

In this course, delegates will gain a comprehensive understanding of testing AI-based systems, including machine learning models, neural networks, and autonomous systems. They will learn about various testing techniques, performance metrics, and challenges in evaluating AI functionality. Delegates will explore the significance of data preparation, test environments, and the impact of AI-specific quality characteristics.

Course Objectives

• To understand the fundamentals of AI and AI-based systems
• To explore key AI quality characteristics and their testing challenges
• To learn about performance metrics for machine learning models
• To gain insights into testing methodologies for AI-based systems
• To develop skills in testing neural networks and deep learning systems
• To understand the importance of data preparation in AI testing

After attending this training, delegates will be able to test AI-based systems using the most appropriate testing techniques and tools. They will be able to identify and address AI-specific quality characteristics, ensuring that AI technologies function effectively. Delegates will gain the ability to apply machine learning performance metrics to evaluate system functionality and conduct various types of testing, such as adversarial and pairwise testing.

What's included in this ISTQB Certified Tester Foundation Level AI Testing Training Course?

• The ISTQB Certified Tester Foundation Level - AI Testing Examination
• World-Class Training Sessions from Experienced Instructors
• The ISTQB Certified Tester Foundation Level AI Testing Certificate
• Digital Delegate Pack 

ISTQB Certified Tester Foundation Level - AI Testing Exam Information

The ISTQB Certified Tester Foundation Level AI Testing Training exam rigorously assesses a candidate’s ability to design, prioritise, and execute effective testing activities specifically in the context of AI-based systems, based on defined requirements, risk factors, and quality attributes. The exam format is structured to evaluate practical understanding of AI-specific testing techniques, coverage strategies, and model behaviour validation. The format of the exam is as follows: 

• Question Type: Multiple-Choice 
• Total Questions: 40 
• Total Marks: 47 Marks 
• Pass Marks: 65%, or 31/47 Marks 
• Duration: 60 Minutes 
• Open Book/Closed Book: Closed Book