ISTQB CT-AI Certification Exam Syllabus

CT-AI dumps PDF, ISTQB CT-AI Braindumps, free Artificial Intelligence Tester dumps, AI Testing dumps free downloadTo achieve the professional designation of ISTQB Certified Tester AI Testing from the ISTQB, candidates must clear the CT-AI Exam with the minimum cut-off score. For those who wish to pass the ISTQB AI Testing certification exam with good percentage, please take a look at the following reference document detailing what should be included in ISTQB Artificial Intelligence Tester Exam preparation.

The ISTQB CT-AI Exam Summary, Sample Question Bank and Practice Exam provide the basis for the real ISTQB Certified Tester AI Testing (CT-AI) exam. We have designed these resources to help you get ready to take ISTQB Certified Tester AI Testing (CT-AI) exam. If you have made the decision to become a certified professional, we suggest you take authorized training and prepare with our online premium ISTQB AI Testing Practice Exam to achieve the best result.

ISTQB CT-AI Exam Summary:

Exam Name ISTQB Certified Tester AI Testing
Exam Code CT-AI
Exam Fee USD $199
Exam Duration 60 Minutes
Number of Questions 40
Passing Score 31 / 47
Format Multiple Choice Questions
Schedule Exam Pearson VUE
Sample Questions ISTQB Artificial Intelligence Tester Exam Sample Questions and Answers
Practice Exam ISTQB Certified Tester AI Testing (CT-AI) Practice Test

ISTQB AI Testing Syllabus Topics:

Topic Details

Introduction to Artificial Intelligence - 120 minutes
Introduction to AI
- Differentiate between AI-based systems and conventional systems
- Distinguish between narrow AI, general AI, and super AI
- Explain the different types of AI technologies
- Explain generative AI
- Compare the choices available for hardware to implement machine learning systems
- Compare the options for the development and hosting of AI models
- Summarize the functionality provided by ML development frameworks
- Explain how regulations and standards affect the development and testing of AI-based systems

Quality Characteristics for AI-Based Systems - 45 minutes
Quality Characteristics for AI-Based System
- Classify behaviors of AI-based systems according to the quality characteristics defined in ISO/IEC 25059
- Explain the special considerations that arise when AI is used in safety-related systems
Acceptance Criteria for AI Based Systems - Give examples of acceptance criteria for AI-based systems

Machine Learning - 375 minutes
Introduction to Machine Learning

- Distinguish between the different forms of ML
- Summarize the workflow used to create an ML system
- Create an ML model
- Summarize the use of pretrained models, fine-tuning, and retrieval augmented generation
Data for Machine Learning
- Explain the activities related to data preparation
- Perform data preparation to support the creation of an ML model
- Contrast the use of training, validation, and test datasets in the development of an ML model
ML Functional Performance Metrics for Classification
- Calculate common ML functional performance metrics from a given set of confusion matrix data
- Evaluate an ML model using selected ML functional performance metrics
- Show the impact of different ML models and dataset combinations on the training and behavior of the models
Neural Networks
- Explain the structure and working of a deep neural network
- Experience the implementation of a perceptron
- Describe the different coverage measures for neural networks

Testing AI-Based Systems – 195 minutes
Introduction to Testing AI-Based Systems
- Compare the testability of locked and adaptive AI-based systems
- Explain why a statistical approach is often needed when testing AI-based systems
- Explain the challenges and solutions relating to test oracles for AI-based systems
Testing Generative AI and LLM
- Explain how generative AI can be tested
- Implement red teaming for GenAI systems
- Apply exploratory testing to an LLM performing boundary value analysis
Test Levels and Machine Learning Systems
- Summarize the test levels used to develop machine learning systems
- Explain how risk-based testing is applied to machine learning systems

Input Data Testing for Machine Learning Systems – 180 minutes
Input Data Testing for Machine Learning Systems
- Give examples of test approaches used for the risk mitigation of input data for a machine learning system
- Explain how to test for bias
- Summarize the various forms of data pipeline testing
- Explain how to test for data representativeness
- Apply dataset constraint testing
- Explain label correctness testing
- Perform input data testing for ML datasets

Model Testing for Machine Learning Systems – 225 minutes 
Model Testing for Machine Learning Systems
- Give examples of test approaches used for risk mitigation of ML models
- Explain the purpose and focus of reviewing ML model documentation
- Explain how ML functional performance testing is carried out for probabilistic machine learning systems
- Summarize adversarial testing of machine learning systems
- Use metamorphic testing to derive test cases for a given scenario
- Apply metamorphic testing
- Explain how drift testing is used on operational machine learning systems
- Explain how overfitting and underfitting are detected by testing
- Explain how A/B testing is used in the context of machine learning systems
- Explain how back-to-back testing is used in the context of machine learning systems

Machine Learning Development Testing – 30 minutes 
Machine Learning Development Testing
- Give examples of test approaches used for risk mitigation of ML development
- Explain the various forms of ML system deployment testing

Both ISTQB and veterans who’ve earned multiple certifications maintain that the best preparation for a ISTQB CT-AI professional certification exam is practical experience, hands-on training and practice exam. This is the most effective way to gain in-depth understanding of ISTQB Artificial Intelligence Tester concepts. When you understand techniques, it helps you retain ISTQB AI Testing knowledge and recall that when needed.

Your rating: None Rating: 5 / 5 (81 votes)