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ISTQB CT-AI Exam Syllabus Topics:

Section	Objectives
Topic 1: AI Quality Characteristics	- Quality attributes 1. Accuracy and robustness 2. Explainability and transparency
Topic 2: AI System Lifecycle and Operations	- Deployment and monitoring 1. Post-deployment monitoring 2. Model deployment strategies - Continuous learning systems 1. Retraining strategies 2. Model drift detection
Topic 3: Machine Learning Fundamentals for Testing	- ML lifecycle 1. Training, validation, and evaluation 2. Data collection and preparation - Model types 1. Supervised and unsupervised learning 2. Deep learning basics
Topic 4: Ethics and Risk in AI Testing	- Risk-based testing for AI 1. Risk identification in AI systems 2. Mitigation strategies - Ethical considerations 1. Privacy and security concerns 2. Responsible AI principles
Topic 5: Data Quality and Bias	- Data quality assurance 1. Data labeling quality 2. Data completeness and consistency - Bias and fairness 1. Fairness testing approaches 2. Types of bias in AI systems
Topic 6: Testing AI-Based Systems	- Test levels for AI systems 1. Model testing 2. System integration testing - Test design techniques 1. Data-driven test design 2. Metamorphic testing
Topic 7: Introduction to AI Testing	- AI systems overview 1. Differences between traditional and AI-based systems 2. What is AI and machine learning systems - Challenges in AI testing 1. Data dependency issues 2. Non-determinism in AI systems

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ISTQB Certified Tester AI Testing Exam Sample Questions (Q91-Q96):

NEW QUESTION # 91
Which of the following neural network coverage criteria can be adapted for its application?
Choose ONE option (1 out of 4)

A. Sign-Change coverage
B. Threshold coverage
C. Sign-Sign coverage
D. Neuron coverage

Answer: B

Explanation:
Section4.2 - Test Coverage Criteria for AI Modelsof the ISTQB CT-AI syllabus describes neural network- specific coverage methods. Among the techniques,threshold coverageis explicitly noted asadaptable, meaning testers may choose different thresholds to determine whether neuron activation is considered "covered." This flexibility makes threshold coverage adjustable to the model architecture, problem domain, and required test thoroughness.
Options A and B (Sign-Sign and Sign-Change coverage) are more rigid structural criteria and are not described as adaptable within the syllabus. They focus on sign patterns of neuron activations and do not allow altering thresholds. Option D, neuron coverage, measures the proportion of neurons activated at least once.
Although simple, it is not defined as an adaptable criterion. Its limitations are documented: it provides shallow insight and too easily achieves high coverage.
Onlythreshold coverageallows testers to adjust activation thresholds for more refined coverage measurement, makingOption Cthe correct choice.

NEW QUESTION # 92
Which statement describes factors related to test data that make testing AI-based systems difficult?
Choose ONE option (1 out of 4)

A. Creating and managing large amounts of test data can be difficult, especially when it needs to be representative
B. The input data must always be the same over time, especially in real-world systems
C. Artificially generated data requires legal approval and must be sanitized and encrypted
D. Using the same implementation for data acquisition by data scientists and testers prevents defect masking

Answer: A

Explanation:
Section2.2 - Data Preparationand4.1 - Challenges in Testing AI-Based Systemsdescribe difficulties in obtaining and managing large, representative datasets. AI-based systems requirerealistic, diverse, and representativedata reflecting real-world variations. The syllabus emphasizes that assembling such datasets is time-consuming, resource-intensive, and often constrained by availability, privacy, or domain complexity.
Option B directly corresponds to these documented challenges.
Option A is incorrect: using the same implementation risksdefect masking, not preventing it; the syllabus warns against this practice. Option C is incorrect because real-world data naturally evolves, and the syllabus notes thatdriftis normal; expecting stable input data contradicts operational reality. Option D is incorrect:
although data privacy is important, the syllabus does not claim that artificially generated data always requires legal approval, nor that sanitization/encryption is mandatory for synthetic data.
Thus,Option Baccurately reflects syllabus-defined difficulties in producing representative test data.

NEW QUESTION # 93
Which statement regarding flexibility and adaptability of AI-based systems is correct?

A. Adaptability and flexibility are important when the system needs to change its behavior and determine the change on its own.
B. Adaptability is considered to be the ability of the system to be used in unspecified situations.
C. Self-learning AI-based systems are classified according to whether they are adaptable only or flexible only.
D. Flexibility is considered to be the ease with which the system can be reprogrammed to a changed operating condition.

Answer: A

Explanation:
The ISTQB CT-AI syllabus defines these two concepts clearly inSection 2.1 - Flexibility and Adaptability. Flexibility is described as the ability of a system to operate in situationsnot explicitly covered in its original requirements, while adaptability refers to how easily the system can be modified to handle new environments or conditions. The syllabus stresses that both flexibility and adaptability are crucial, particularly inself-learning AI systemsthat may need to respond to changes in their environment and adjust their behavior accordingly. It states that systems must be capable of determining when and how to adjust behavior in evolving situations, especially when the operational environment is not fully known at deployment time . This directly aligns with Option A.

NEW QUESTION # 94
You have been developing test automation for an e-commerce system. One of the problems you are seeing is that object recognition in the GUI is having frequent failures. You have determined this is because the developers are changing the identifiers when they make code updates.
How could AI help make the automation more reliable?

A. It could modify the automation code to ignore unrecognizable objects to avoid failures.
B. It could identify the objects multiple ways and then determine the most commonly used and stable identification for each object.
C. It could dynamically name the objects, altering the source code, so the object names will match the object names used in the automation.
D. It could generate a model that will anticipate developer changes and pre-alter the test automation code accordingly.

Answer: B

NEW QUESTION # 95
Which statement about using AI to analyze reported defects is MOST correct?

A. ML models can support duplicate defect identification when checking defect criticality.
B. ML models identify developers who should handle a defect based on ticket content.
C. ML models can identify categories for a reported defect during assignment.
D. ML models trained with critical defect tickets can identify defects that cause serious consequences.

Answer: C

Explanation:
The ISTQB CT-AI syllabus (Section5.3 - AI Support for Defect Analysis) explains that AI can categorize defect reports using natural language processing or classification models.
Categorization helps route defects efficiently and determine which areas of the system are affected. Thus, Option C is correct: AI canidentify defect categories, supporting assignment and triage.

NEW QUESTION # 96
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