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[General] Ideal ISTQB CT-AI Exam Dumps [Updated 2026] For Quick Success

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【General】 Ideal ISTQB CT-AI Exam Dumps [Updated 2026] For Quick Success

Posted at yesterday 10:37      View：4 | Replies：1        Print      Only Author   [Copy Link] 1#
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ISTQB CT-AI Exam Syllabus Topics:
TopicDetails
Topic 1
  • Quality Characteristics for AI-Based Systems: This section covers topics covered how to explain the importance of flexibility and adaptability as characteristics of AI-based systems and describes the vitality of managing evolution for AI-based systems. It also covers how to recall the characteristics that make it difficult to use AI-based systems in safety-related applications.
Topic 2
  • 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 3
  • 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 4
  • 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 5
  • Using AI for Testing: In this section, the exam topics cover categorizing the AI technologies used in software testing.
Topic 6
  • systems from those required for conventional systems.

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ISTQB Certified Tester AI Testing Exam Sample Questions (Q70-Q75):NEW QUESTION # 70
Which option describes a reasonable application of AIB testing for a self-learning system after it has changed its behavior due to user input?
Choose ONE option (1 out of 4)
  • A. Comparing outputs before and after the change using different inputs
  • B. Comparing outputs before and after the change using identical inputs
  • C. Generating test cases for the system before and after the change, since neither has a test oracle
  • D. Comparing outputs of a non-self-learning system with those of the changed self-learning system
Answer: B
Explanation:
According to Section4.6 - AI Behaviour Testing (AIB Testing)of the ISTQB CT-AI syllabus, AIB testing is used to evaluate changes in the functional behavior of self-learning systems. The core principle iscomparing pre-change and post-change model behavior using the same test inputs, so that any difference in outputs can be attributed to the model's learning and not to differences in input data. This directly corresponds to OptionC.
Option A is incorrect because the absence of a test oracle does not justify generating new test cases; AIB relies onreusing identical inputsto detect behavioral drift. Option B is invalid because using different inputs prevents meaningful comparison. Option D is incorrect because comparing with an unrelated non-self- learning system does not allow evaluation of the same model's behavioral evolution.
Thus, OptionCaccurately represents the correct application of AIB testing: assessing model behavior changes by running identical test inputs before and after learning updates.

NEW QUESTION # 71
Which of the following options is an example of the concept of overfitting?
Choose ONE option (1 out of 4)
  • A. A model for the recognition of dogs was trained predominantly with pictures of dogs in parks. On pictures with other animals in parks, dogs are also falsely recognized.
  • B. A previously trained model for recognizing cars is adapted and extended so that it can also identify the make of the car beyond its original function.
  • C. A model for predicting IT system failures delivers too many false-negative predictions because the failures cannot be adequately explained via the log files used for training.
  • D. A model for predicting academic performance was trained with data from students at one university.
    The model shows low predictive accuracy when applied to other universities.
Answer: D
Explanation:
The ISTQB CT-AI syllabus definesoverfittingin Section3.2 - ML Model Evaluationas a condition where an ML model learns the training data too precisely-including noise and irrelevant detail-resulting in poor performance on unseen data. Overfitting is characterized byhigh accuracy on training data but low accuracy on validation or real-world data. OptionAperfectly matches this definition: a model trained only on one university's student data generalizes poorly to students from other universities. This is a textbook example of overfitting because the model has essentially memorized patterns unique to a narrow dataset, instead of learning generalizable relationships applicable across environments .
Option B instead describessample biasor inadequate training diversity, not overfitting. Option C involves transfer learningor model extension, unrelated to overfitting. Option D indicatesinsufficient training data qualityor lack of meaningful features, but not overfitting. Only Option A reflects the syllabus definition directly: overly specialized training leading to reduced predictive performance on new data.
Thus,Ais the correct and syllabus-aligned example of overfitting.

NEW QUESTION # 72
A system is to be developed to detect lung cancer using X-ray images.
Which statement BEST describes the difference between a conventional system and an AI system with supervised machine learning?
Choose ONE option (1 out of 4)
  • A. An AI system independently determines patterns in X-rays during training; a conventional system requires a human to program in those patterns.
  • B. The implementation of an AI system consists mainly of training data, whereas that of a conventional system consists of branches and loops.
  • C. The X-ray images that an AI system can analyze must be structurally different from X-ray images used in a conventional system.
  • D. The results of analyzing an X-ray for lung cancer using an AI system are more understandable than with a conventional system.
Answer: A
Explanation:
The syllabus explains the fundamental distinction betweenconventional systemsandAI-based systems using supervised machine learningin Section1.3 - AI-Based and Conventional Systems. A conventional system relies on human-programmed logic-such as branches, conditions, and explicit rules-to interpret input data.
The system behaves exactly as specified by its developers.
In contrast,AI systems using supervised learning automatically learn patternsfrom labeled data. The syllabus states that"patterns in data are used by the system to determine how it should react in the future...
The AI determines on its own what patterns or features in the data can be used". This aligns directly with Option C: an AI system identifies relevant diagnostic patterns in X-ray images during training, whereas a conventional system requires human experts to explicitly program those patterns.
Option A is incorrect because AI outputs are typicallylessexplainable, not more. Option B is incorrect because both systems can use thesame X-ray images; ML does not require structurally different images. Option D is oversimplified and not fully accurate; while training data is central to ML, AI systems also include architecture, algorithms, and preprocessing-not just data.
Thus,Option Cis the correct and syllabus-aligned answer.

NEW QUESTION # 73
Which of the following is a problem with AI-generated test cases that are generated from the requirements?
  • A. They are slow and will usually not be able to execute in the time allowed
  • B. They are defect-prone because they are unable to detect nuances in the requirements
  • C. They are usually missing the expected results, so verification is difficult or must resort to only detecting significant failures
  • D. They make debugging more complicated because the number of steps is usually high in order to induce the target failure
Answer: C
Explanation:
The syllabus mentions a drawback of AI-generated test cases:
"AI-based test generation tools can generate test cases... However, unless a test model that defines required behaviors is used as the basis of the tests, this form of test generation generally suffers from a test oracle problem because the AI-based tool does not know what the expected results should be." (Reference: ISTQB CT-AI Syllabus v1.0, Section 11.3, page 78 of 99)

NEW QUESTION # 74
Which of the following statements about reinforcement learning is correct?
Choose ONE option (1 out of 4)
  • A. From experience, the agent learns theoptimal reward function
  • B. The agent's training is based on a reward function that rewards successful attempts
  • C. The agent creates a model of the environment from labeled data during training
  • D. The approach is suitable when the application doesnotrequire interaction with the environment
Answer: B
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, OptionCdirectly reflects the correct description: the agent learns by being rewarded for successful actions .
Option A is incorrect because RL doesnotuse labeled data; that applies to supervised learning. Option B contradicts the syllabus definition: RL fundamentallyrequiresinteraction with the environment. Option D is incorrect because the reward function isdefined by humans, not learned by the agent; the agent learns apolicy
, not the reward function itself.
Thus, OptionCis the only statement consistent with RL as defined in the syllabus.

NEW QUESTION # 75
......
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Posted at yesterday 22:52        Only Author  2#
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