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ISTQB CT-AI Exam Syllabus Topics:

Topic	Details
Topic 1	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 2	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 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	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 5	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 6	systems from those required for conventional systems.
Topic 7	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 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.

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ISTQB Certified Tester AI Testing Exam Sample Questions (Q135-Q140):

NEW QUESTION # 135
Which assignment of AI techniques to testing support is BEST?

A. Probabilistic methods for the prediction of system failures
B. Computational optimization techniques for defect prediction
C. Fuzzy logic for the generation of test cases
D. Classification for the optimization of regression test cases

Answer: A

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 Option B.

NEW QUESTION # 136
Before deployment of an AI based system, a developer is expected to demonstrate in a test environment how decisions are made. Which of the following characteristics does decision making fall under?

A. Autonomy
B. Self-learning
C. Explainability
D. Non-determinism

Answer: C

Explanation:
Explainability in AI-based systems refers to the ease with which users can determine how the system reaches a particular result. It is a crucial aspect when demonstrating AI decision-making, as it ensures that decisions made by AI models are transparent, interpretable, and understandable by stakeholders.
Before deploying an AI-based system, a developer must validate how decisions are made in a test environment. This process falls under the characteristic of explainability because it involves clarifying how an AI model arrives at its conclusions, which helps build trust in the system and meet regulatory and ethical requirements.
* ISTQB CT-AI Syllabus (Section 2.7: Transparency, Interpretability, and Explainability)
* "Explainability is considered to be the ease with which users can determine how the AI-based system comes up with a particular result".
* "Most users are presented with AI-based systems as 'black boxes' and have little awareness of how these systems arrive at their results. This ignorance may even apply to the data scientists who built the systems. Occasionally, users may not even be aware they are interacting with an AI- based system".
* ISTQB CT-AI Syllabus (Section 8.6: Testing the Transparency, Interpretability, and Explainability of AI-based Systems)
* "Testing the explainability of AI-based systems involves verifying whether users can understand and validate AI-generated decisions. This ensures that AI systems remain accountable and do not make incomprehensible or biased decisions".
* Contrast with Other Options:
* Autonomy (B): Autonomy relates to an AI system's ability to operate independently without human oversight. While decision-making is a key function of autonomy, the focus here is on demonstrating the reasoning behind decisions, which falls under explainability rather than autonomy.
* Self-learning (C): Self-learning systems adapt based on previous data and experiences, which is different from making decisions understandable to humans.
* Non-determinism (D): AI-based systems are often probabilistic and non-deterministic, meaning they do not always produce the same output for the same input. This can make testing and validation more challenging, but it does not relate to explaining the decision-making process.
Supporting References from ISTQB Certified Tester AI Testing Study Guide:Conclusion:Since the question explicitly asks about the characteristic under which decision-making falls when being demonstrated before deployment,explainability is the correct choicebecause it ensures that AI decisions are transparent, understandable, and accountable to stakeholders.

NEW QUESTION # 137
Which of the following is a dataset issue that can be resolved using pre-processing?

A. Invalid data
B. Insufficient data
C. Numbers stored as strings
D. Wanted outliers

Answer: C

Explanation:
The syllabus describes that data pre-processing includes cleaning (e.g., fixing or removing invalid data) and transforming data (e.g., changing data types such as numbers stored as strings).
"Transformation: The format of the given data is changed... converting categorical data into numerical data, changing image formats..."

NEW QUESTION # 138
Which of the following is correct regarding the layers of a deep neural network?

A. There is only an input and output layer
B. The output layer is not connected with the other layers to maintain integrity
C. There is at least one internal hidden layer
D. There must be a minimum of five total layers to be considered deep

Answer: C

Explanation:
A deep neural network (DNN) is a type of artificial neural network that consists of multiple layers between the input and output layers. The ISTQB Certified Tester AI Testing (CT-AI) Syllabus outlines the following characteristics of a DNN:
Structure of a Deep Neural Network:
A DNN comprises at least three types of layers:
Input layer: Receives the input data.
Hidden layers: Perform complex feature extraction and transformations.
Output layer: Produces the final prediction or classification.

NEW QUESTION # 139
Which statement regarding data preparation in the ML workflow is correct?

A. One challenge of data gathering is obtaining high-quality data from multiple sources.
B. Since data preparation is time-consuming, all steps should be automated.
C. A key challenge in data transformation is the removal or correction of erroneous data.
D. Sampling is so well researched that it is no longer considered risky.

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 # 140
......

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