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ISTQB CT-AI Exam Syllabus Topics:

TopicDetails
Topic 1
  • systems from those required for conventional systems.
Topic 2
  • Test Environments for AI-Based Systems: This section is about factors that differentiate the test environments for AI-based
Topic 3
  • 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 4
  • ML Functional Performance Metrics: In this section, the topics covered include how to calculate the ML functional performance metrics from a given set of confusion matrices.
Topic 5
  • 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 6
  • 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 7
  • 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.
Topic 8
  • 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 9
  • 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.

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ISTQB Certified Tester AI Testing Exam Sample Questions (Q29-Q34):

NEW QUESTION # 29
Which of the following statements about reinforcement learning is correct?

Answer: C

Explanation:
Section1.6.3 - Reinforcement Learningof the ISTQB CT-AI syllabus states that reinforcement learning (RL) is based on anagent interacting with an environment, performing actions, and receivingrewards or penalties. The core concept is thereward function, which guides the agent's learning process. The syllabus emphasizes that training in RL isdriven by rewards, and the agent aims to maximize cumulative reward over time. Therefore, Option C directly reflects the correct description: the agent learns by being rewarded for successful actions .


NEW QUESTION # 30
Which of the following statements regarding experience-based testing for AI-based systems is correct?
Choose ONE option (1 out of 4)

Answer: A

Explanation:
The ISTQB CT-AI syllabus explains inSection 4.4 - Experience-Based Testing for AI Systemsthat AI- based systems frequently suffer frominsufficient specifications, unpredictable model behavior, andtest oracle problems, especially when outputs depend on probabilistic or learned patterns. The syllabus explicitly states thatexploratory testingis especially valuable in such contexts because it allows testers to investigate the system interactively, observe unexpected behavior, and evaluate system responses that cannot be fully predicted beforehand. Thus, OptionCaccurately reflects the role and justification of exploratory testing for AI systems.
Option A describes data analysis rather than intuitive test design. Option B is incorrect because checklist- based testing does not dynamically adapt test cases; instead, it follows predetermined checklists. Option D incorrectly defines "tour-based testing"; tours refer to structured exploratory approaches, not biased datasets.
Therefore,Option Cis the syllabus-aligned correct statement.


NEW QUESTION # 31
Which ONE of the below is NOT an input to an activation function in a neural network?

Answer: D

Explanation:
The correct answer is C . In a neural network, each neuron calculates its activation value using an activation function. The CT-AI syllabus explains that the activation function receives as input the activation values from all neurons in the previous layer , the weights assigned to the connections between neurons, and the individual bias of the neuron.
Therefore, options A, B, and D are valid inputs to the activation function. Bias is a preset constant associated with the neuron. The activation values from the previous layer represent the incoming signal. The weights assigned to the connections determine how strongly those incoming activation values influence the neuron's calculation. Option C is not an input because activation values from the current layer are the outputs of activation functions being computed in that layer, not inputs to the same computation. In feed-forward neural network logic, information passes from the input layer through hidden layers toward the output layer; each layer computes its activation values based on the previous layer, not on activation values simultaneously generated in the current layer.
References/topics: CT-AI Syllabus Chapter 6, Section 6.1 "Neural Networks"; Appendix B "Activation function" and "Activation value."
=========


NEW QUESTION # 32
"AllerEgo" is a product that uses sell-learning to predict the behavior of a pilot under combat situation for a variety of terrains and enemy aircraft formations. Post training the model was exposed to the real- world data and the model was found to be behaving poorly. A lot of data quality tests had been performed on the data to bring it into a shape fit for training and testing.
Which ONE of the following options is least likely to describes the possible reason for the fall in the performance, especially when considering the self-learning nature of the Al system?
SELECT ONE OPTION
* The difficulty of defining criteria for improvement before the model can be accepted.
* The fast pace of change did not allow sufficient time for testing.
* The unknown nature and insufficient specification of the operating environment might have caused the poor performance.
* There was an algorithmic bias in the Al system.

Answer:

Explanation:
* A. The difficulty of defining criteria for improvement before the model can be accepted.
* Defining criteria for improvement is a challenge in the acceptance of AI models, but it is not directly related to the performance drop in real-world scenarios. It relates more to the evaluation and deployment phase rather than affecting the model's real-time performance post-deployment.
* B. The fast pace of change did not allow sufficient time for testing.
* This can significantly affect the model's performance. If the system is self-learning, it needs to adapt quickly, and insufficient testing time can lead to incomplete learning and poor performance.
* C. The unknown nature and insufficient specification of the operating environment might have caused the poor performance.
* This is highly likely to affect performance. Self-learning AI systems require detailed specifications of the operating environment to adapt and learn effectively. If the environment is insufficiently specified, the model may fail to perform accurately in real-world scenarios.
* D. There was an algorithmic bias in the AI system.
* Algorithmic bias can significantly impact the performance of AI systems. If the model has biases, it will not perform well across different scenarios and data distributions.
Given the context of the self-learning nature and the need for real-time adaptability, optionAis least likely to describe the fall in performance because it deals with acceptance criteria rather than real-time performance issues.


NEW QUESTION # 33
Which of the following is THE LEAST appropriate tests to be performed for testing a feature related to autonomy?
SELECT ONE OPTION

Answer: C

Explanation:
* Testing Autonomy: Testing for human handover when it should not be relinquishing control is the least appropriate because it contradicts the very definition of autonomous systems. The other tests are relevant to ensuring smooth operation and transitions between human and AI control.
* Reference: ISTQB_CT-AI_Syllabus_v1.0, Sections on Testing Autonomous AI-Based Systems and Testing for Human-AI Interaction.


NEW QUESTION # 34
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