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ISTQB CT-AI Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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 2
  • 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 3
  • Using AI for Testing: In this section, the exam topics cover categorizing the AI technologies used in software testing.
Topic 4
  • systems from those required for conventional systems.
Topic 5
  • 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 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
  • Test Environments for AI-Based Systems: This section is about factors that differentiate the test environments for AI-based
Topic 8
  • 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.

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

NEW QUESTION # 18
Which AI-specific test objective and acceptance criterion should be selected MOST LIKELY for testing GPT_Legal?

Answer: B

Explanation:
The ISTQB CT-AI syllabus introduces AI-specific quality characteristics, including evolution, functional safety, compatibility, andbias-related data quality. Section5.1 - AI-Specific Test Objectives explains that evolution refers 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. Option B therefore directly supports the described acceptance criteria for this evolving, self-learning application.


NEW QUESTION # 19
Which statement regarding data preparation in the ML workflow is correct?

Answer: A

Explanation:
The ISTQB CT-AI syllabus describes theML data preparation workflowin Section2.2 - Data Preparation. Data preparation consists ofdata gathering,cleaning, transformation, andsampling.
The syllabus emphasizes that one significant challenge duringdata gatheringis combining data frommultiple heterogeneous sources, which often differ in structure, quality, and format. Ensuring the resulting dataset is accurate, complete, and representative can be complex, making this a critical challenge in the ML workflow. This aligns directly with Option C.


NEW QUESTION # 20
Which of the following characteristics of AI-based systems make it more difficult to ensure they are safe?

Answer: A

Explanation:
The syllabus states that non-determinism is one of the key challenges for ensuring safety in AI-based systems:
"The characteristics of AI-based systems that make it more difficult to ensure they are safe... include:
complexity, non-determinism, probabilistic nature, self-learning, lack of transparency, interpretability and explainability, and lack of robustness." (Reference: ISTQB CT-AI Syllabus v1.0, Section 2.8, page 25 of 99)


NEW QUESTION # 21
In a conference on artificial intelligence (Al), a speaker made the statement, "The current implementation of Al using models which do NOT change by themselves is NOT true Al*. Based on your understanding of Al, is this above statement CORRECT or INCORRECT and why?

Answer: D

Explanation:
The term AI is dynamic and has evolved over the years. What is considered AI today might be viewed as standard computing in the future. Historically, as technologies become mainstream, they often cease to be considered "AI".


NEW QUESTION # 22
Which of the following is an example of an input change where it would be expected that the AI system should be able to adapt?

Answer: D

Explanation:
The syllabus explains that input changes that arein the same domainas what was used for training are expected to be handled with adaptability:
"Adaptability refers to the ability of a system to adjust its behavior in response to changes in its environment or inputs. This includes changes to the inputs which are still within the expected operational range of the system, such as resolution changes in images or sensor data." (Reference: ISTQB CT-AI Syllabus v1.0, Section 7.6 and 8.2)


NEW QUESTION # 23
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