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ISTQB CT-AI Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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 2
  • 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 3
  • Using AI for Testing: In this section, the exam topics cover categorizing the AI technologies used in software testing.
Topic 4
  • 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 5
  • 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 6
  • 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 7
  • systems from those required for conventional 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
  • Test Environments for AI-Based Systems: This section is about factors that differentiate the test environments for AI-based

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CT-AI Valid Test Duration | CT-AI Exam Lab Questions

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ISTQB Certified Tester AI Testing Exam Sample Questions (Q117-Q122):

NEW QUESTION # 117
A test engineer is planning testing for a wearable medical device using AI. The medical device will detect possible heart issues in patients and dispatch emergency services automatically. It is not expected that many patients will have heart issues, and this is reflected in the available data.
In this case, it is decided it is more important that emergency services are not sent un- necessarily, than it is to detect actual heart problems.
Which ONE of the following metrics should the test engineer choose to ensure that the emergency services are sent only when needed?

Answer: D

Explanation:
Precision is the most appropriate metric in this case, as it focuses on the proportion of true positive results (correctly identifying heart issues) out of all the positive predictions made by the model. Since it is more important that emergency services are not sent unnecessarily (false positives), optimizing for precision ensures that the model is more cautious about dispatching emergency services, minimizing false alarms.


NEW QUESTION # 118
Which assignment of AI techniques to testing support is BEST?
Choose ONE option (1 out of 4)

Answer: D

Explanation:
The ISTQB CT-AI syllabus (Section5.2 - AI for Testing) explains that various AI approaches can support testing activities. Probabilistic methods-one of the three major AI technique groups-are used topredict system failures, especially when dealing with uncertainty, likelihood estimation, and reliability analysis. This aligns precisely with OptionB.
Option A is incorrect because regression test optimization is typically performed usingsearch-based optimization, not classification. Option C is incorrect because fuzzy logic is more suited to reasoning under vagueness, not generating test cases. Option D is incorrect: defect prediction relies on statistical learning or classification models, not computational optimization.
Thus,Option Bis the most syllabus-consistent mapping of AI techniques to testing tasks.


NEW QUESTION # 119
Which of the following describes the AI effect?

Answer: D

Explanation:
The AI Effectis clearly defined in the ISTQB Certified Tester AI Testing Syllabus v1.0 under Section1.1 - Definition of AI and AI Effect. The document explains that society's understanding of what qualifies as "AI" changes over time. Technologies once considered AI--such as expert systems from the 1970s and 1980s or early chess-playing systems--are no longer viewed as AI today. This phenomenon is explicitly labeled the"AI Effect,"described as"the changing perception of what constitutes AI ."The syllabus states that as AI capabilities become routine or widely implemented, they often stop being perceived as true artificial intelligence .


NEW QUESTION # 120
A company producing consumable goods wants to identify groups of people with similar tastes for the purpose of targeting different products for each group. You have to choose and apply an appropriate ML type for this problem.
Which ONE of the following options represents the BEST possible solution for this above- mentioned task?

Answer: A

Explanation:
Clustering is an unsupervised learning method used to group similar data points based on their features. It is ideal for identifying groups of people with similar tastes without prior knowledge of the group labels. This technique will help the company segment its customer base effectively.


NEW QUESTION # 121
Which ONE of the following options for a test basis would give the LEAST coverage when using AI-based test generation?

Answer: C

Explanation:
An XML schema would give the least coverage when using AI-based test generation. While it defines the structure of data (e.g., tags, elements), it does not provide detailed information about the application functionality or behavior, which are essential for generating meaningful tests for AI systems. The other options (test model, web pages list, and pseudo-oracle) provide more comprehensive insights into the system's behavior and logic, which are better suited for effective AI-based test generation.


NEW QUESTION # 122
......

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