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:
|ISTQB Certified Tester AI Testing
|Number of Questions
|Multiple Choice Questions
|ISTQB Artificial Intelligence Tester Exam Sample Questions and Answers
|ISTQB Certified Tester AI Testing (CT-AI) Practice Test
ISTQB AI Testing Syllabus Topics:
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.
|- 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).
|- 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.
|- Explain the relationship between autonomy and AI-based systems.
|- Explain the importance of managing evolution for AI-based systems.
|- Describe the different causes and types of bias found in AI-based systems.
|- 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.
|- 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
|- 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
- 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.
- Explain how pairwise testing is used for AI-based systems.
- Apply pairwise testing to derive and execute test cases for an AI-based system.
|- Explain how back-to-back testing is used for AI-based systems.
|- Explain how A/B testing is applied to the testing of AI-based systems.
- 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.