iSQI CT-AI Exam Syllabus
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Before starting your CT-AI exam preparation, it is recommended to review the complete iSQI Certified Tester AI Testing exam syllabus and carefully go through the exam objectives listed below. Once you understand the exam structure and objectives, you should practice using our free CT-AI questions. We also provide premium CT-AI practice test, fully updated according to the latest exam objectives, to help you accurately assess your preparedness for the actual exam.
iSQI CT-AI Exam Objectives
| Section | Objectives |
|---|---|
| Introduction to AI | In this section, the exam topics covered include the impact of AI and its definition and the difference between narrow AI, general AI, and super AI. The topics also cover how standards apply to AI-based systems. |
| Quality Characteristics for AI-Based Systems | In this section, the focus is given to the importance of flexibility and adaptability as attributes of AI systems. It also covers how crucial the evolution of AI-based systems is. |
| Machine Learning ML | This section includes the classification and regression as part of supervised learning, explaining the factors involved in the selection of ML algorithms, and demonstrating underfitting and overfitting. |
| ML: Data | In this section, topics covered include the challenges of data preparation and how to test. It also covers how to create a machine learning framework and realize how poor data quality can be that may cause issues. |
| ML Functional Performance Metrics | This section deals with topics such as measuring the performance of machine learning metrics from a given set of confusion matrix. |
| Neural Networks and Testing | In this section of this exam, the major topics covered include describing the structure and function of neural networks. This also includes the role of a DNN and how coverage measures for neural networks. |
| Testing AI-Based Systems Overview | This particular sector In this section, the focus is given on how system specifications for AI-based systems can create challenges in testing and explain automation bias and how this affects testing. |
| Testing AI-Specific Quality Characteristic | In this section, the exam includes topics such as how certain challenges appear during testing and are created by the self-learning of AI-based systems. |
| Methods and Techniques for the Testing of AI-Based Systems: | In this exam section, the benefits of testing machine learning systems are explored and how using these methods can assist in reducing attacks and data poisoning. |
| Test Environments for AI-Based Systems | In this section of the exam, the candidates are tested for other know-how of various factors that distinguish between the test environments for AI-based systems. |
| Using AI for Testing | This section of the exam covers important topics such as classifying the AI technologies that are utilized in the process of testing software. |
| Official Information | https://www.istqb.org/certifications/artificial-inteligence-tester |
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