Overview
Understand fundamental AI concepts, history, and real-world applications., Explore different ML types, workflows, and considerations for selecting ML models., Learn evaluation metrics like accuracy, precision, recall, and F1-score for ML models., Examine key test levels, risks, and methodologies for validating AI systems.
This course is designed for professionals working with AI-based systems and AI-driven testing, including but not limited to: Testing & QA Professionals, This course is ideal for anyone looking to enhance their expertise in testing AI-based systems and leveraging AI for software testing., Prerequisite: Candidates must hold the Certified Tester Foundation Level (CTFL) certification to qualify for this course., Testers, test analysts, test engineers, test consultants, test managers, and user acceptance testers., IT & Operations Leaders: IT directors, operations team members, and management consultants involved in AI adoption., Business analysts, quality managers, and project managers looking to understand AI testing methodologies., Software Development Teams: Developers, software engineers, and technical architects involved in AI testing. Project & Quality Managers, Data & AI Specialists: Data analysts and professionals working with AI models.
Basic Understanding of Software Testing: Familiarity with foundational testing principles and practices is recommended.
This is the practice exams course to give you the winning edge.
This course is designed for QA Engineers, Software Testers and Professionals preparing for the ISTQB® Certified Tester – AI Testing (CT-AI) Certification. It includes over 600 High Quality Exam-style multiple-choice questions (100 per exam), each accompanied by correct answers and detailed explanations. All content is fully aligned with the official ISTQB® CT-AI Syllabus v1.0.
Special Access: Generative AI-Based Personalized Learning
All enrolled learners receive exclusive access to a Custom ChatGPT Expert for Personalized Learning that is designed and trained specifically for the ISTQB® CT-AI latest syllabus.
What You’ll Get
600 specially crafted practice questions
Detailed Explanation for correct and incorrect options
Breakdown by Learning Objectives (LO) and K-Levels (K1-K4)
Coverage of Real-world scenarios, including AI bias, transparency, ML metrics, test strategy, etc.
Progress tracking and scoring templates
Timed Mock Exam (ISTQB® style)
What You Get with the GPT Expert:
Syllabus-Based Q&A: Ask any question from the official syllabus (v1.0) – concepts, terms, techniques
Exam Booking Help: Find ISTQB exam providers based on your country or language preferences
Mock Exams On-Demand: Generate practice tests instantly by chapter, topic, or difficulty
Explain My Answer: Get immediate reasoning behind right/wrong answers – not just memorization
Syllabus Breakdown: Dive deep into each learning objective (K1–K3 levels) with examples
Exam Strategy Tips: Get test-taking techniques for time management and topic prioritization
Tool & Standard Guides: Ask for quick overviews on MLflow, DVC, SHAP, ISO/IEC 25010, EU AI Act, etc.
Progress Tracking (via GPT prompts): Simulate chapter mastery with prompts like “quiz me on Chapter 5 with 10 hard questions”
Miscellaneous: Flash Card, Quiz, PDFs for Last-Minute Review
Quality speaks for itself.
SAMPLE QUESTION:
How can AI improve test coverage efficiency?
A. By writing long test plans
B. By identifying test cases that cover unique execution paths
C. By disabling old test cases
D. By increasing test case execution speed
Correct Answer:
B: By identifying test cases that cover unique execution paths
Explanation: AI can analyze paths and remove redundancies.Reference: Chapter 11 | LO: AI-11.2.1 | K2
Incorrect Answers:
A: By writing long test plans
Explanation: Length doesn’t equal effectiveness.
C: By disabling old test cases
Explanation: Disabling tests may lead to missed regressions.
D: By increasing test case execution speed
Explanation: Faster execution doesn’t necessarily improve coverage.
What’s your guess? Scroll down to see the answer…
M Faizan Khan
Faizan is a seasoned Lead Software Quality Assurance (SQA) Engineer with proven expertise in Agile development and a deep commitment to delivering high-quality software products through best testing practices and standards wiith hands-on experience across global markets—including the United States, United Kingdom, Canada, UAE and more.
He is not only a quality advocate but also a mentor and educator, frequently guiding junior software engineers and SQA professionals. He regularly conducts workshops, leads research initiatives, and develops training programs that produce measurable results.
As a Program Manager, Faizan has successfully led national-level SQA initiatives through various NGOs, covering areas such as SQA trainings for Manual Testing, Generative AI integration in Software Testing, Test Automation and Performance Testing. He also served as a Program Lead for an ISTQB Certification Learning Program, designed to elevate the skills of a QA group, including both senior and junior team members. This initiative focused on instilling ISTQB-aligned processes and practices to build strong foundational and advanced QA competencies.
Faizan is also the author of a book "The Expert's Path to Software Quality Assurance", where he shares his insights, methodologies and practical knowledge to help professionals build expertise in QA and testing disciplines.
An emerging technologies enthusiast and writer, Faizan is particularly passionate about artificial intelligence and its transformative impact on the software industry and society at large.