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ISTQB CT-AI Exam Syllabus Topics:

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

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ISTQB Certified Tester AI Testing Exam Sample Questions (Q138-Q143):

NEW QUESTION # 138
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 dynamically name the objects, altering the source code, so the object names will match the object names used in the automation.
B. It could modify the automation code to ignore unrecognizable objects to avoid failures.
C. It could identify the objects multiple ways and then determine the most commonly used and stable identification for each object.
D. It could generate a model that will anticipate developer changes and pre-alter the test automation code accordingly.

Answer: C

Explanation:
Object recognition issues in test automation often arise whendevelopers frequently change object identifiers in the GUI. AI can enhance the stability of GUI automation by:
* Using multiple criteria for object identification
* AI cantrack UI elements using multiple attributessuch asXPath, label, ID, class, and screen coordinatesrather than relying on a single identifier that may change over time.
* This approach makes the automationless brittle and more adaptive to changes in the UI.
* Why other options are incorrect?
* B (Ignore unrecognizable objects to avoid failures): Ignoring objects instead of identifying them properly wouldlead to incomplete or incorrect test execution.
* C (Dynamically name objects and alter source code): AI-based testing tools donot modify application source code; they work byadjusting the recognition strategy.
* D (Anticipate developer changes and pre-alter automation code): While AI can adapt,it does not predict future changes to the GUI, making this option unrealistic.
Thus,Option A is the best answer, as AI tools enhance object recognitionby dynamically selecting the most stable and persistent identification methods, improving test automation reliability.
Certified Tester AI Testing Study Guide References:
* ISTQB CT-AI Syllabus v1.0, Section 11.6.1 (Using AI to Test Through the Graphical User Interface (GUI))
* ISTQB CT-AI Syllabus v1.0, Section 11.6.2 (Using AI to Test the GUI).

NEW QUESTION # 139
Which statement regarding testing transparency, explainability, or interpretability is MOST correct?
Choose ONE option (1 out of 4)

A. Since different users have different backgrounds, interpretability testing depends on the comprehensibility of the ML algorithm
B. Dynamic testing is one way to quantify explainability; however, each method is specific to a particular model type
C. LIME can precisely state the decisive reason for a change in the output
D. Tests for explainability and transparency are comparable to exploratory testing and can be performed with little information about development

Answer: A

Explanation:
The ISTQB CT-AI syllabus states in Section2.10 - Explainability, Transparency, and Interpretabilitythat interpretability isuser-dependent, meaning different users understand explanations differently. This is because interpretability depends not only on the ML algorithm but also on the user's domain knowledge, experience, and expectations. OptionBdirectly reflects this syllabus principle: interpretability testing must consideruser background, and explanations must be comprehensible to the intended user group.
Option A is incorrect because explainability testing requires substantial information about the model, data, and expected behavior-not just exploratory effort. Option C is incorrect because explainability isnot generally quantifiable through dynamic testing alone, and the syllabus does not assert model-type specificity in this way. Option D exaggerates LIME's capabilities. LIME offers approximate local explanations, but cannotpreciselystate root causes; the syllabus emphasizes itslimitationsand that explanations are approximations, not exact reasons.
Therefore,Option Bis the most syllabus-aligned and correct statement.

NEW QUESTION # 140
Al-enabled medical devices are used nowadays for automating certain parts of the medical diagnostic processes. Since these are life-critical process the relevant authorities are considenng bringing about suitable certifications for these Al enabled medical devices. This certification may involve several facets of Al testing (I - V).
I . Autonomy
II . Maintainability
III . Safety
IV . Transparency
V . Side Effects
Which ONE of the following options contains the three MOST required aspects to be satisfied for the above scenario of certification of Al enabled medical devices?

A. Aspects II, III and IV
B. Aspects I, IV, and V
C. Aspects III, IV, and V
D. Aspects I, II, and III

Answer: C

Explanation:
For AI-enabled medical devices, the most required aspects for certification are safety, transparency, and side effects.
Safety (Aspect III): Critical for ensuring that the AI system does not cause harm to patients.
Transparency (Aspect IV): Important for understanding and verifying the decisions made by the AI system.
Side Effects (Aspect V): Necessary to identify and mitigate any unintended consequences of the AI system.

NEW QUESTION # 141
Which of the following is one of the reasons for data mislabelling?

A. Expert knowledge
B. Small datasets
C. Interoperability error
D. Lack of domain knowledge

Answer: D

Explanation:
The syllabus lists multiple reasons for mislabelled data, including the lack of domain knowledge:
"Lack of required domain knowledge may lead to incorrect labelling."

NEW QUESTION # 142
Which ONE of the below statements BEST describes how combinatorial testing can be applied to AI-based systems?

A. Inputs to the system and environment factors can be considered parameters for pairwise tests
B. Combinatorial testing cannot yet be applied to AI
C. Each neuron can be treated as a parameter for pairwise tests
D. Two variants of the system can be used and compared

Answer: A

Explanation:
Combinatorial testing involves testing different combinations of input parameters to ensure coverage of various interactions. In the case of AI-based systems, inputs to the system (such as data features) and environment factors (like conditions under which the system operates) can be considered parameters for pairwise tests. This ensures that different combinations of inputs and environmental factors are tested for their effect on the system's behavior.

NEW QUESTION # 143
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