To 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, Body of Knowledge (BOK), 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 AI - 105 minutes |
|
| Definition of AI and AI Effect | - Describe the AI effect and how it influences the definition of AI. |
| Narrow, General and Super AI | - Distinguish between narrow AI, general AI, and super AI. |
| AI-Based and Conventional Systems. | - Differentiate between AI-based systems and conventional systems. |
| AI Technologies | - Recognize the different technologies used to implement AI. |
| AI Development Frameworks | - Identify popular AI development frameworks. |
| Hardware for AI-Based Systems | - Compare the choices available for hardware to implement AI-based systems. |
| AI as a Service (AIaaS) | - Explain the concept of AI as a Service (AIaaS). |
| Pre-Trained Models | - Explain the use of pre-trained AI models and the risks associated with them. |
| Standards, Regulations and AI | - Describe how standards apply to AI-based systems. |
Quality Characteristics for AI-Based Systems - 105 minutes |
|
| Flexibility and Adaptability | - Explain the importance of flexibility and adaptability as characteristics of AI-based systems. |
| Autonomy | - Explain the relationship between autonomy and AI-based systems. |
| Evolution | - Explain the importance of managing evolution for AI-based systems. |
| Bias | - Describe the different causes and types of bias found in AI-based systems. |
| Ethics | - Discuss the ethical principles that should be respected in the development, deployment and use of AI-based systems. |
| Side Effects and Reward Hacking | - Explain the occurrence of side effects and reward hacking in AI-based systems. |
| Transparency, Interpretability and Explainability | - Explain how transparency, interpretability and explainability apply to AI-based systems. |
| Safety and AI | - Recall the characteristics that make it difficult to use AI-based systems in safetyrelated applications. |
Machine Learning (ML) - Overview - 145 minutes |
|
| Forms of ML |
- Describe classification and regression as part of supervised learning. - Describe clustering and association as part of unsupervised learning. - Describe reinforcement learning. |
| ML Workflow | - Summarize the workflow used to create an ML system. |
| Selecting a Form of ML | - Given a project scenario, identify an appropriate form of ML (from classification, regression, clustering, association, or reinforcement learning). |
| Factors involved in ML Algorithm Selection | - Explain the factors involved in the selection of ML algorithms. |
| Overfitting and Underfitting |
- Summarize the concepts of underfitting and overfitting. - Demonstrate underfitting and overfitting. |
ML - Data - 230 minutes |
|
| Data Preparation as part of the ML Workflow |
- Describe the activities and challenges related to data preparation. - Perform data preparation in support of the creation of an ML model. |
| Training, Validation and Test Datasets in the ML Workflow |
- Contrast the use of training, validation and test datasets in the development of an ML model. - Identify training and test datasets and create an ML model. |
| Dataset Quality Issues | - Describe typical dataset quality issues. |
| Data quality and its effect on the ML model | - Recognize how poor data quality can cause problems with the resultant ML model. |
| Data Labelling for Supervised Learning |
- Recall the different approaches to the labelling of data in datasets for supervised learning. - Recall reasons for the data in datasets being mislabeled. |
ML Functional Performance Metrics - 120 minutes |
|
| Confusion Matrix | - Calculate the ML functional performance metrics from a given set of confusion matrix data. |
| Additional ML Functional Performance Metrics for Classification, Regression and Clustering | - Contrast and compare the concepts behind the ML functional performance metrics for classification, regression and clustering methods. |
| Limitations of ML Functional Performance Metrics | - Summarize the limitations of using ML functional performance metrics to determine the quality of the ML system. |
| Selecting ML Functional Performance Metrics |
- Select appropriate ML functional performance metrics and/or their values for a given ML model and scenario. - Evaluate the created ML model using selected ML functional performance metrics |
| Benchmark Suites for ML | - Explain the use of benchmark suites in the context of ML |
ML - Neural Networks and Testing - 65 minutes |
|
| Neural Networks |
- Explain the structure and function of a neural network including a DNN. - Experience the implementation of a perceptron. |
| Coverage Measures for Neural Networks | - Describe the different coverage measures for neural networks. |
Testing AI-Based Systems Overview - 115 minutes |
|
| Specification of AI-Based Systems | - Explain how system specifications for AI-based systems can create challenges in testing. |
| Test Levels for AI-Based Systems | - Describe how AI-based systems are tested at each test level |
| Test Data for Testing AI-Based Systems | - Recall those factors associated with test data that can make testing AI-based systems difficult. |
| Testing for Automation Bias in AI-Based Systems | - Explain automation bias and how this affects testing. |
| Documenting an ML Model | - Describe the documentation of an AI component and understand how documentation supports the testing of AI-based systems. |
| Testing for Concept Drift | - Explain the need for frequently testing the trained model to handle concept drift. |
| Selecting a Test Approach for an ML System | - For a given scenario determine a test approach to be followed when developing an ML system. |
Testing AI-Specific Quality Characteristics - 150 minutes |
|
| Challenges Testing Self-Learning Systems | - Explain the challenges in testing created by the self-learning of AI-based systems. |
| Testing Autonomous AI-Based Systems | - Describe how autonomous AI-based systems are tested |
| Testing for Algorithmic, Sample and Inappropriate Bias | - Explain how to test for bias in an AI-based system. |
| Challenges Testing Probabilistic and Non-Deterministic AI-Based Systems | - Explain the challenges in testing created by the probabilistic and non-deterministic nature of AI-based systems. |
| Challenges Testing Complex AI-based Systems | - Explain the challenges in testing created by the complexity of AI-based systems. |
| Testing the Transparency, Interpretability and Explainability of AI-based Systems |
- Describe how the transparency, interpretability and explainability of AI-based systems can be tested. - Use a tool to show how explainability can be used by testers. |
| Test Oracles for AI-Based Systems | - Explain the challenges in creating test oracles resulting from the specific characteristics of AI-based systems. |
| Test Objectives and Acceptance Criteria | - Select appropriate test objectives and acceptance criteria for the AI-specific quality characteristics of a given AI-based system. |
Methods and Techniques for the Testing of AI-Based Systems - 245 minutes |
|
| Adversarial Attacks and Data Poisoning | - Explain how the testing of ML systems can help prevent adversarial attacks and data poisoning. |
| Pairwise Testing |
- Explain how pairwise testing is used for AI-based systems. - Apply pairwise testing to derive and execute test cases for an AI-based system. |
| Back-to-Back Testing | - Explain how back-to-back testing is used for AI-based systems. |
| A/B Testing | - Explain how A/B testing is applied to the testing of AI-based systems. |
| Metamorphic Testing |
- Apply metamorphic testing for the testing of AI-based systems. - Apply metamorphic testing to derive test cases for a given scenario and execute them. |
| Experience-Based Testing of AI-Based Systems |
- Explain how experience-based testing can be applied to the testing of AI-based systems. - Apply exploratory testing to an AI-based system. |
| Selecting Test Techniques for AI-Based Systems | - For a given scenario, select appropriate test techniques when testing an AI-based system. |
Test Environments for AI-Based Systems - 30 minutes |
|
| Test Environments for AI-Based Systems | - Describe the main factors that differentiate the test environments for AI-based systems from those required for conventional systems. |
| Virtual Test Environments for Testing AI-Based Systems | - Describe the benefits provided by virtual test environments in the testing of AI-based systems. |
Using AI for Testing - 195 minutes |
|
| AI Technologies for Testing |
- Categorize the AI technologies used in software testing. - Discuss, using examples, those activities in testing where AI is less likely to be used. |
| Using AI to Analyze Reported Defects | - Explain how AI can assist in supporting the analysis of new defects. |
| Using AI for Test Case Generation | - Explain how AI can assist in test case generation. |
| Using AI for the Optimization of Regression Test Suites | - Explain how AI can assist in optimization of regression test suites |
| Using AI for Defect Prediction |
- Explain how AI can assist in defect prediction. - Implement a simple AI-based defect prediction system. |
| Using AI for Testing User Interfaces | - Explain the use of AI in testing user interfaces |
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.