Course Information
Registration period
Year-round Recruitment
Course Level
Study Mode
Duration
5 Hour(s) 58 Minute(s)
Language
English
Taught by
Terdia Consulting
Certificate
- Available
- *The delivery and distribution of the certificate are subject to the policies and arrangements of the course provider.
Rating
Course Overview
Master AI Testing with ISTQB Tester Training, complete syllabus, quizzes, real life examples and practice test by expert
***New Mock Quiz added *** - practice with 40 fresh exam-style questions!
This ISTQB Certified Tester AI Testing course is a complete training program designed to help professionals effectively understand, test, and certify their expertise in AI-based systems. Aligned closely with the official ISTQB syllabus, this course provides a structured and comprehensive approach covering AI fundamentals, machine learning concepts, quality characteristics, and advanced AI testing methodologies.
Course Highlights:
Chapter-wise quizzes to reinforce key learning objectives.
Practical exercises and scenario-based questions to apply concepts effectively.
A sample mock exam simulating the ISTQB AI Testing certification experience. (Publicly available)
Another mock exam (Newly added)
Proven tips and guidance on enhancing your professional profile and career opportunities post-certification.
Course Outline:
Chapter 1: Introduction to AI – Understand AI concepts, types, and practical applications.
Chapter 2: Quality Characteristics for AI-Based Systems – Explore AI-specific attributes like transparency, fairness, robustness, and ethics.
Chapter 3: Machine Learning (ML) Overview – Master ML fundamentals including supervised and unsupervised learning.
Chapter 4: ML Data – Delve into data preprocessing, feature engineering, dataset management, and quality assurance.
Chapter 5: ML Functional Performance Metrics – Learn key evaluation metrics to measure model effectiveness.
Chapter 6: ML Neural Networks and Testing – Gain insights into deep learning principles, neural network structures, and relevant testing techniques.
Chapter 7: Testing AI-Based Systems Overview – Understand unique challenges and strategies in testing AI applications.
Chapter 8: Testing AI-Specific Quality Characteristics – Special focus on explainability, interpretability, bias identification, and safety testing.
Chapter 9: Methods and Techniques for AI Testing – Explore proven testing strategies including pairwise, exploratory, and white-box testing.
Chapter 10: Test Environments for AI-Based Systems – Discover the automation tools and environments suitable for testing complex AI systems.
Chapter 11: Using AI for Testing – Leverage AI technologies to enhance traditional software testing practices.
Tips on enhancing your profile post certification
This course is ideal for QA professionals, software testers, test managers, developers transitioning into AI testing, and AI practitioners aiming for ISTQB AI Testing certification. Enroll now and accelerate your career with expert knowledge, practical insights, and certification-focused preparation.
Course Content
- 13 section(s)
- 50 lecture(s)
- Section 1 Introduction and Chapter 1
- Section 2 Chapter 2: Quality Characteristics for AI-Based Systems
- Section 3 Chapter 3: Machine Learning (ML) – Overview
- Section 4 Chapter 4: ML Data
- Section 5 Chapter 5: ML Functional Performance Metrics
- Section 6 Chapter 6: ML – Neural Networks and Testing
- Section 7 Chapter 7: Testing AI-Based Systems Overview
- Section 8 Chapter 8: Testing AI-Specific Quality Characteristics
- Section 9 Chapter 9: Methods and Techniques for the Testing of AI-Based Systems
- Section 10 Chapter 10: Test Environments for AI-Based Systems
- Section 11 Chapter 11: Using AI for Testing
- Section 12 Sample Exam
- Section 13 Mock Exam - New
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.
Skills covered in this course
Reviews
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CChristopher Anderson
From a certification perspective: Took the test yesterday. Passing score is a 65%. I got an 87%, with nothing other than this course and about 18 months OJT. For those curious, there was at least one question each on calculating Accuracy, Precision, and Recall, and at least one question about F1 score. The content in this course was more than enough to tackle those questions. From a knowledge perspective: I learned a lot! The meat is definitely in the last few chapters, and those are super useful in my opinion. I'll definitely be revisiting to brush up. From a value perspective: The included practice test is literally the same as the publicly available resources at https://atsqa.org/assets/documents/ISTQB_CT-AI_SampleExam-Questions_v1.0.pdf. There is zero value in including it, and it feels VERY scammy including it in the course content. If the rest of the content wasn't solid, I'd be tempted to give it 3-stars based on this alone. Very poor form.
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SSneha Patil
Explanation could have been better. Concepts are not explained in detail.
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LLuke Cameron
If you rely on this course to pass the exam you are out of luck.
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GGIRISANKAR GUNASEKARAN
Content is not detail enough....
