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ISTQB CT-AI Exam Syllabus Topics:

TopicDetails
Topic 1
  • Test Environments for AI-Based Systems: This section is about factors that differentiate the test environments for AI-based
Topic 2
  • 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 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
  • systems from those required for conventional systems.
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
  • 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 9
  • 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 10
  • 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.

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ISTQB Certified Tester AI Testing Exam Sample Questions (Q18-Q23):

NEW QUESTION # 18
Which ONE of the following options for a test basis would give the LEAST coverage when using AI based test generation?

Answer: B

Explanation:
The correct answer is C. A pseudo-oracle . The syllabus states that AI-based test generation can use sources such as source code, the user interface, and a machine-readable test model . Some tools also generate tests from low-level system behaviour observed through instrumentation or log files. A test model describing required behaviour is particularly strong because it can guide functional coverage and expected behaviour.
A list of web pages may provide structural coverage of the web application, though it is weaker than a behavioural model. An XML schema may also support systematic generation of valid and invalid structured data tests. A pseudo-oracle, however, is not a strong input for generating test coverage. Its role is to help decide whether a result is acceptable, typically by comparing the SUT's output with another system. The syllabus explicitly presents pseudo-oracles as a solution to the test oracle problem, not as a primary coverage- oriented test basis. Therefore, it would provide the least coverage for AI-based test generation.
References/topics: CT-AI Syllabus Chapter 11, Section 11.3 "Using AI for Test Case Generation"; Chapter 9, Section 9.3 "Back-to-Back Testing."
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NEW QUESTION # 19
Which of the following describes the AI effect?

Answer: C

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 # 20
Which ONE of the following statements BEST describes a testing challenge that specifically applied to a self-learning system?

Answer: A

Explanation:
A key challenge in testing self-learning systems is that, as the system learns and adapts over time, the results of previously passing tests may change. This is because the system's behavior evolves as it learns from new data, potentially altering how it responds to test inputs. This dynamic nature of self-learning systems makes it challenging to maintain consistent and reliable test results.


NEW QUESTION # 21
An e-commerce developer built an application for automatic classification of online products in order to allow customers to select products faster. The goal is to provide more relevant products to the user based on prior purchases. Which of the following factors is necessary for a supervised machine learning algorithm to be successful?

Answer: D

Explanation:
The syllabus explains that supervised learning requires correctly labeled data so the algorithm can learn the relationship between input features and output labels:
"In supervised learning, the algorithm creates the ML model from labeled data during the training phase. The labeled data is used to infer the relationship between the input data and output labels."


NEW QUESTION # 22
Which ONE of the following statements is MOST true about black-box adversarial testing?

Answer: B

Explanation:
In black-box adversarial testing, the tester does not have access to the internal workings of the model (such as the algorithm or the training data). The approach relies on the transferability of the attacks, where adversarial examples generated for one model are used to test the robustness of another model, even without direct access to its internal details.


NEW QUESTION # 23
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