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ISTQB CT-AI Exam Syllabus Topics:

Section	Objectives
AI System Lifecycle and Operations	- Deployment and monitoring 1. Model deployment strategies 2. Post-deployment monitoring - Continuous learning systems 1. Retraining strategies 2. Model drift detection
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
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. Non-determinism in AI systems 2. Data dependency issues
Data Quality and Bias	- Data quality assurance 1. Data completeness and consistency 2. Data labeling quality - Bias and fairness 1. Types of bias in AI systems 2. Fairness testing approaches
Ethics and Risk in AI Testing	- Ethical considerations 1. Privacy and security concerns 2. Responsible AI principles - Risk-based testing for AI 1. Risk identification in AI systems 2. Mitigation strategies
Machine Learning Fundamentals for Testing	- ML lifecycle 1. Data collection and preparation 2. Training, validation, and evaluation - Model types 1. Deep learning basics 2. Supervised and unsupervised learning
AI Quality Characteristics	- Quality attributes 1. Accuracy and robustness 2. Explainability and transparency

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ISTQB Certified Tester AI Testing Exam Sample Questions (Q88-Q93):

NEW QUESTION # 88
Which two test procedures are BEST suited for CleverPropose system testing?

A. Back-to-back testing
B. Adversarial testing
C. Exploratory data analysis
D. Pairwise testing
E. Metamorphic testing

Answer: A,E

Explanation:
The ISTQB CT-AI syllabus explains that AI-based decision-support systems benefit strongly fromback-to-back testingandmetamorphic testingwhen oracle problems exist or when limited regression tests are available. In this scenario, CleverPropose replaces an older advisory system.Back-to-back testing(Option A) is ideal because the outputs of the existing conventional system can serve as areference, enabling comparison against the new AI system. This is exactly what the syllabus recommends when AI is replacing a traditional deterministic system.
Metamorphic testing(Option C) is also appropriate, as stated in Section4.6 - Metamorphic Relations. With limited regression tests and complex decision logic, testers can define metamorphic relations such as "if customer income increases, risk rating should not worsen." These relations allow validation even when exact expected outputs are unavailable.

NEW QUESTION # 89
Which ONE of the below statements BEST describes how combinatorial testing can be applied to AI based systems?

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

Answer: D

Explanation:
The correct answer is C . The CT-AI syllabus explains that AI-based systems can have extremely high numbers of parameters, especially where they use big data or interact with the external world. Exhaustive testing of all parameter combinations is usually infeasible, so combinatorial techniques, especially pairwise testing, can reduce the number of required test cases while preserving useful defect detection capability.
The syllabus gives autonomous driving as an example, where high-level system tests should consider different operational environments and vehicle functions. Relevant parameters may include road types, surfaces, weather, traffic conditions, visibility, vehicle functions, and sensor effectiveness. This directly supports treating system inputs and environmental factors as parameters in pairwise tests.
Option A is not the intended application; neuron coverage is a neural-network coverage topic, not pairwise system scenario design. Option B describes A/B testing or back-to-back comparison, not combinatorial testing. Option D is false because the syllabus explicitly describes pairwise testing for AI-based systems and includes it as an examinable method.
References/topics: CT-AI Syllabus Chapter 9, Section 9.2 "Pairwise Testing."
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NEW QUESTION # 90
Which ONE of the following statements BEST describes a testing challenge that specifically applied to a self- learning system?

A. When systems change themselves the results of previously passing tests may change
B. It is necessary to test whether the system will relinquish control to a human at the right time
C. In can be difficult to explain the link between test inputs and outputs
D. External data sources might be required to ensure that the system is unbiased

Answer: A

Explanation:
The correct answer is A . A self-learning system can modify its own behaviour as it learns from operational data, environmental inputs, or its own decisions. The CT-AI syllabus identifies "unexpected change" as a major testing challenge for self-learning systems. It explains that when systems change themselves and their outputs, the results of previously passing tests can change, creating a regression testing and test design challenge. Testers may need tests that remain valid as the system evolves, and may also need new tests based on newly observed behaviours.
Option B relates more directly to autonomy , where the system must know when to return control to a human operator. Option C relates to testing for inappropriate bias, particularly when hidden or inferred attributes may influence outcomes. Option D relates to explainability, where the link between an input and a model output may be difficult to understand. These are important AI testing issues, but they are not the most specific self- learning challenge.
References/topics: CT-AI Syllabus Chapter 8, Section 8.1 "Challenges Testing Self-Learning Systems."
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NEW QUESTION # 91
Which ONE of the below statements BEST describes why test environments for autonomous systems might need to be different to other test environments?

A. Non-determinism may need to be introduced into the environment
B. Tools may be required to provide explanations of system behavior
C. AI-specific hardware may be required
D. Tools may be required to simulate extreme scenarios

Answer: D

Explanation:
Tools may be required to simulate extreme scenarios because autonomous systems often need to be tested under challenging or rare conditions that are difficult to replicate in a real-world environment. These scenarios, such as extreme weather, system failures, or unexpected behaviors, need to be simulated in a controlled environment to ensure the system can handle a wide range of situations safely and effectively. This makes the test environment for autonomous systems unique compared to other types of testing.

NEW QUESTION # 92
"BioSearch" is creating an Al model used for predicting cancer occurrence via examining X-Ray images. The accuracy of the model in isolation has been found to be good. However, the users of the model started complaining of the poor quality of results, especially inability to detect real cancer cases, when put to practice in the diagnosis lab, leading to stopping of the usage of the model. A testing expert was called in to find the deficiencies in the test planning which led to the above scenario.
Which ONE of the following options would you expect to MOST likely be the reason to be discovered by the test expert?

A. A lack of similarity between the training and testing data.
B. A lack of focus on choosing the right functional-performance metrics.
C. A lack of focus on non-functional requirements testing.
D. The input data has not been tested for quality prior to use for testing.

Answer: A

Explanation:
A lack of similarity between the training and testing data (A): This is a common issue in ML where the model performs well on training data but poorly on real-world data due to a lack of representativeness in the training data. This leads to poor generalization to new, unseen data.

NEW QUESTION # 93
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