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ISTQB CT-AI Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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.
Topic 2
  • Methods and Techniques for the Testing of AI-Based Systems: In this section, the focus is on explaining how the testing of ML systems can help prevent adversarial attacks and data poisoning.
Topic 3
  • Neural Networks and Testing: This section of the exam covers defining the structure and function of a neural network including a DNN and the different coverage measures for neural networks.
Topic 4
  • ML: Data: This section of the exam covers explaining the activities and challenges related to data preparation. It also covers how to test datasets create an ML model and recognize how poor data quality can cause problems with the resultant ML model.
Topic 5
  • Using AI for Testing: In this section, the exam topics cover categorizing the AI technologies used in software testing.
Topic 6
  • Test Environments for AI-Based Systems: This section is about factors that differentiate the test environments for AI-based
Topic 7
  • Introduction to AI: This exam section covers topics such as the AI effect and how it influences the definition of AI. It covers how to distinguish between narrow AI, general AI, and super AI; moreover, the topics covered include describing how standards apply to AI-based systems.
Topic 8
  • Testing AI-Based Systems Overview: In this section, focus is given to how system specifications for AI-based systems can create challenges in testing and explain automation bias and how this affects testing.
Topic 9
  • Testing AI-Specific Quality Characteristics: In this section, the topics covered are about the challenges in testing created by the self-learning of AI-based systems.

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ISTQB Certified Tester AI Testing Exam Sample Questions (Q63-Q68):

NEW QUESTION # 63
Which ONE of the following tests is MOST likely to describe a useful test to help detect different kinds of biases in ML pipeline?
SELECT ONE OPTION

Answer: A

Explanation:
Detecting biases in the ML pipeline involves various tests to ensure fairness and accuracy throughout the ML process.
* Testing the distribution shift in the training data for inappropriate bias (A): This involves checking if there is any shift in the data distribution that could lead to bias in the model. It is an important test but not the most direct method for detecting biases.
* Test the model during model evaluation for data bias (B): This is a critical stage where the model is evaluated to detect any biases in the data it was trained on. It directly addresses potential data biases in the model.
* Testing the data pipeline for any sources for algorithmic bias (C): This test is crucial as it helps identify biases that may originate from the data processing and transformation stages within the pipeline. Detecting sources of algorithmic bias ensures that the model does not inherit biases from these processes.
* Check the input test data for potential sample bias (D): While this is an important step, it focuses more on the input data and less on the overall data pipeline.
Hence, the most likely useful test to help detect different kinds of biases in the ML pipeline isB. Test the model during model evaluation for data bias.
:
ISTQB CT-AI Syllabus Section 8.3 on Testing for Algorithmic, Sample, and Inappropriate Bias discusses various tests that can be performed to detect biases at different stages of the ML pipeline.
Sample Exam Questions document, Question #32 highlights the importance of evaluating the model for biases.


NEW QUESTION # 64
Which ONE of the following options describes the LEAST LIKELY usage of Al for detection of GUI changes due to changes in test objects?

Answer: D

Explanation:
Pixel comparison is a traditional method and does not involve AI . It compares images at the pixel level, which can be effective but is not an intelligent approach. It is not considered an AI usage and is the least likely usage of AI for detecting GUI changes.


NEW QUESTION # 65
Which of the following decisions is BEST as a test approach for the described situation?

Answer: A

Explanation:
The ISTQB CT-AI syllabus emphasizes that testing AI-based systems require scross-functional collaboration and experience-based testingwhen parts of the team lack domain knowledge. In this scenario, the ML expert understands ML and dataset preparation but lacks knowledge ofcamera system behavior, the device's operational data pipeline, and end-user workflows. The remainder of the team understands the domain and system testing but not ML. Section4.4 - Human Factors and AI Testingand4.3 - System Testing of AI Components highlight that when domain understanding is unevenly distributed,experience-based testing conducted by the full team(testers, developers, domain experts) is the most effective approach. This ensures that AI outputs align with actual user expectations and system behavior. Option C aligns exactly with this principle.


NEW QUESTION # 66
Which of the following neural network coverage criteria can be adapted for its application?

Answer: C

Explanation:
Section4.2 - Test Coverage Criteria for AI Modelsof the ISTQB CT-AI syllabus describes neural network-specific coverage methods. Among the techniques,threshold coverageis explicitly noted asadaptable, meaning testers may choose different thresholds to determine whether neuron activation is considered "covered." This flexibility makes threshold coverage adjustable to the model architecture, problem domain, and required test thoroughness.


NEW QUESTION # 67
Which ONE of the following requirements for an AI based system is MOST likely to cause a challenge for testing?

Answer: C

Explanation:
The correct answer is C . A requirement that an AI-based system be 100% accurate with no tolerance for failure creates a major testing challenge because many AI-based systems are probabilistic and may require accuracy tolerances rather than absolute expected outcomes. The CT-AI syllabus states that the probabilistic nature of many AI-based systems can make it necessary to specify tolerances for quality requirements such as prediction accuracy.
Option A is also challenging because natural language interfaces increase flexibility and make it difficult to document all possible interactions, but it is not as absolute as requiring perfect accuracy. Option B is a conventional performance efficiency requirement and can normally be tested using timing and resource measurements. Option D relates to autonomy and may require scenario-based testing, but it is still testable if the operational envelope and failure conditions are well specified. The decisive issue with option C is that it provides no tolerance range, no probabilistic acceptance model, and no practical allowance for statistical uncertainty. This creates both a specification problem and a test oracle problem.
References/topics: CT-AI Syllabus Chapter 7, Section 7.1 "Specification of AI-Based Systems"; Chapter
8, Section 8.4 "Challenges Testing Probabilistic and Non-Deterministic AI-Based Systems."
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NEW QUESTION # 68
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