ISTQB Certified Tester Foundation Level AI Testing Training in Chester

ISTQB Certified Tester Foundation Level AI Testing Training Course Outline

Module 1: Introduction to Artificial Intelligence

• AI-Based and Conventional Systems
• Narrow AI, General AI, and Super AI
• Different Types of AI Technologies
• Generative AI
• Hardware for Machine Learning Systems
• Development and Hosting of AI Models
• Machine Learning Development Frameworks
• Regulations and Standards for AI

Module 2: Quality Characteristics for AI-Based Systems

• Quality Characteristics for AI-Based Systems  
  • AI-Specific Quality Characteristics
  • AI and Safety
• Acceptance Criteria for AI-Based Systems

Module 3: Machine Learning

• Introduction to Machine Learning 
  • Different Forms of Machine Learning
  • Machine Learning Workflow
  • Pretrained Models, Fine-Tuning and Retrieval-Augmented Generation
• Data for Machine Learning 
  • Activities in Data Preparation
• ML Functional Performance Metrics for Classification 
  • Calculation of Machine Learning Functional Performance Metrics
• Neural Networks 
  • Structure and Working of a Deep Neural Network
  • Coverage Measures for Neural Networks

Module 4: Testing AI-Based Systems

• Introduction to Testing AI-Based Systems 
  • Locked and Adaptive AI-Based Systems
  • Rationale for a Statistical Approach to Testing AI-Based Systems
  • Test Oracles for AI-Based Systems
• Testing Generative AI and Large Language Models 
  • Testing Generative AI
  • Red Teaming
• Test Levels and Machine Learning Systems 
  • Test Levels for Machine Learning Systems
  • Risk-Based Testing of Machine Learning Systems

Module 5: Input Data Testing for Machine Learning Systems

• Input Data Risks and Mitigations
• Testing for Bias
• Data Pipeline Testing
• Testing for Data Representativeness
• Dataset Constraint Testing
• Label Correctness Testing

Module 6: Model Testing for Machine Learning Systems

• Machine Learning Model Risks and Mitigations
• Machine Learning Model Documentation and Review
• ML Functional Performance Testing of Probabilistic Machine Learning Systems
• Adversarial Testing of Machine Learning Systems
• Metamorphic Testing
• Drift Testing
• Testing for Overfitting and Underfitting
• A/B Testing
• Back-to-Back Testing

Module 7: Machine Learning Development Testing

• Machine Learning Development Risks and Mitigations
• Machine Learning System Deployment Testing

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 

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