Real ISTQB CT-AI PDF Questions [2026]-The Greatest Shortcut Towards Success

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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
  • Test Environments for AI-Based Systems: This section is about factors that differentiate the test environments for AI-based
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
  • 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 5
  • 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 6
  • 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 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.

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ISTQB Certified Tester AI Testing Exam Sample Questions (Q106-Q111):

NEW QUESTION # 106
A mobile app start-up company is implementing an AI-based chat assistant for e-commerce customers. In the process of planning the testing, the team realizes that the specifications are insufficient.
Which testing approach should be used to test this system?

Answer: C

Explanation:
When testing an AI-based chat assistant for e-commerce customers, the lack of sufficient specifications makes it difficult to use structured test techniques. The ISTQB CT-AI Syllabus recommends exploratory testing in such cases:
Exploratory testing is useful when specifications are incomplete or unclear. AI-based systems, particularly those using natural language processing (NLP), may not behave deterministically, making scripted test cases ineffective.
The tester interacts dynamically with the system, identifying unexpected behaviors not documented in the specification.


NEW QUESTION # 107
Which ONE of the following statements is MOST true in relation to A/B testing?

Answer: A

Explanation:
A/B testing is primarily used to compare two variants of a system to determine which one performs better. This method involves testing two different versions (A and B) to analyze their impact on specific metrics, often in a real-world environment. The other options do not fully align with the purpose of A/B testing.


NEW QUESTION # 108
Which ONE of the following statements about a system MOST describes an autonomous system?

Answer: B

Explanation:
An autonomous system is capable of performing tasks independently, without requiring human intervention. The self-driving car in option B is an example of an autonomous system because it automatically takes action (stopping the car) based on its internal decision-making, in response to the driver's behavior.


NEW QUESTION # 109
A ML engineer is trying to determine the correctness of the new open-source implementation *X", of a supervised regression algorithm implementation. R-Square is one of the functional performance metrics used to determine the quality of the model.
Which ONE of the following would be an APPROPRIATE strategy to achieve this goal?
SELECT ONE OPTION

Answer: B

Explanation:
* A. Add 10% of the rows randomly and create another model and compare the R-Square scores of both the models.
* Adding more data to the training set can affect the R-Square score, but it does not directly verify the correctness of the implementation.
* B. Train various models by changing the order of input features and verify that the R-Square score of these models vary significantly.
* Changing the order of input features should not significantly affect the R-Square score if the implementation is correct, but this approach is more about testing model robustness rather than correctness of the implementation.
* C. Compare the R-Square score of the model obtained using two different implementations that utilize two different programming languages while using the same algorithm and the same training and testing data.
* This approach directly compares the performance of two implementations of the same algorithm.
If both implementations produce similar R-Square scores on the same training and testing data, it suggests that the new implementation "X" is correct.
* D. Drop 10% of the rows randomly and create another model and compare the R-Square scores of both the models.
* Dropping data can lead to variations in the R-Square score but does not directly verify the correctness of the implementation.
Therefore, optionCis the most appropriate strategy because it directly compares the performance of the new implementation "X" with another implementation using the same algorithm and datasets, which helps in verifying the correctness of the implementation.


NEW QUESTION # 110
Which AI-specific test objective and acceptance criterion should be selected MOST LIKELY for testing GPT_Legal?
Choose ONE option (1 out of 4)

Answer: B

Explanation:
The ISTQB CT-AI syllabus introducesAI-specific quality characteristics, includingevolution,functional safety, compatibility, andbias-related data quality. Section5.1 - AI-Specific Test Objectivesexplains thatevolutionrefers to an AI system's capability to continue improving or at least maintain performance as it undergoes additional training. GPT_Legal is explicitly described as aself-learning systemexpected to:
continuously reduce false positives,
achieve weekly accuracy improvements of 10%,
reach and maintain 90% accuracy,
adapt to new environments (patent law firm # corporate legal department).
This aligns perfectly with the syllabus definition ofevidence of evolution: ensuring the model doesnot degradeas additional training data is introduced. OptionBtherefore directly supports the described acceptance criteria for this evolving, self-learning application.
Option A (functional safety) is irrelevant because patent searching and drafting do not constitute safety- critical domains. Option C (compatibility) is necessary but not the primary AI-specific objective. Option D addresses bias, which is important but not central to the described performance and continuous-learning expectations.
Thus,Option Bis the most appropriate AI-specific test objective.


NEW QUESTION # 111
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