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ISTQB CT-AI Exam Syllabus Topics:

Topic	Details
Topic 1	Test Environments for AI-Based Systems: This section is about factors that differentiate the test environments for AI-based
Topic 2	Using AI for Testing: In this section, the exam topics cover categorizing the AI technologies used in software testing.
Topic 3	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 4	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 5	systems from those required for conventional systems.
Topic 6	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 7	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 8	ML: Data: This section of the exam covers explaining the activities and challenges related to data preparation. It also covers how to test datasets create an ML model and recognize how poor data quality can cause problems with the resultant ML model.

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ISTQB Certified Tester AI Testing Exam Sample Questions (Q129-Q134):

NEW QUESTION # 129
Which of the following is an example of overfitting?

A. The model is too simplistic for the data
B. The model is not able to generalize to accommodate new types of data
C. The model is missing relationships between the inputs and outputs
D. The model discards data it considers to be noise or outliers

Answer: B

Explanation:
The syllabus defines overfitting as:
"Overfitting is when the ML model learns the training data so well that it is unable to generalize to accommodate new data." This occurs when the model memorizes the training data, including noise, instead of learning the general patterns.
(Reference: ISTQB CT-AI Syllabus v1.0, Section 3.5.1, page 31 of 99)

NEW QUESTION # 130
A system is to be developed to detect lung cancer using X-ray images. Which statement BEST describes the difference between a conventional system and an AI system with supervised machine learning?

A. The results of analyzing an X-ray for lung cancer using an AI system are more understandable than with a conventional system.
B. The X-ray images that an AI system can analyze must be structurally different from X-ray images used in a conventional system.
C. The implementation of an AI system consists mainly of training data, whereas that of a conventional system consists of branches and loops.
D. An AI system independently determines patterns in X-rays during training; a conventional system requires a human to program in those patterns.

Answer: D

Explanation:
The syllabus explains the fundamental distinction between conventional systems and AI-based systems using supervised machine learningin Section1.3 - AI-Based and Conventional Systems.
A conventional system relies on human-programmed logic--such as branches, conditions, and explicit rules--to interpret input data. The system behaves exactly as specified by its developers.
In contrast, AI systems using supervised learning automatically learn patterns from labeled data.
The syllabus states that"patterns in data are used by the system to determine how it should react in the future... The AI determines on its own what patterns or features in the data can be used".
This aligns directly with Option C: an AI system identifies relevant diagnostic patterns in X-ray images during training, whereas a conventional system requires human experts to explicitly program those patterns.

NEW QUESTION # 131
A system was developed for screening the X-rays of patients for potential malignancy detection (skin cancer).
A workflow system has been developed to screen multiple cancers by using several individually trained ML models chained together in the workflow.
Testing the pipeline could involve multiple kind of tests (I - III):
I.Pairwise testing of combinations
II.Testing each individual model for accuracy
III.A/B testing of different sequences of models
Which ONE of the following options contains the kinds of tests that would be MOST APPROPRIATE to include in the strategy for optimal detection?
SELECT ONE OPTION

A. Only II
B. I and II
C. I and III
D. Only III

Answer: B

Explanation:
The question asks which combination of tests would be most appropriate to include in the strategy for optimal detection in a workflow system using multiple ML models.
* Pairwise testing of combinations (I): This method is useful for testing interactions between different components in the workflow to ensure they work well together, identifying potential issues in the integration.
* Testing each individual model for accuracy (II): Ensuring that each model in the workflow performs accurately on its own is crucial before integrating them into a combined workflow.
* A/B testing of different sequences of models (III): This involves comparing different sequences to determine which configuration yields the best results. While useful, it might not be as fundamental as pairwise and individual accuracy testing in the initial stages.
References:
* ISTQB CT-AI Syllabus Section 9.2 on Pairwise Testing and Section 9.3 on Testing ML Models emphasize the importance of testing interactions and individual model accuracy in complex ML workflows.

NEW QUESTION # 132
Which statement about testing to prevent data poisoning and adversarial attacks is correct?
Choose ONE option (1 out of 4)

A. Using AIB testing to identify data poisoning can better identify outliers than exploratory data analysis.
B. Adversarial testing consists of using adversarial attacks to identify vulnerabilities so that they can be eliminated.
C. Regression testing can be used to verify data sourcing policies to ensure the source of training data.
D. The adversarial examples identified during adversarial testing must not be added to the training data so that they do not poison the model.

Answer: B

Explanation:
The ISTQB CT-AI syllabus explains inSection 4.5 - Testing AI-Specific Risksthat adversarial testing is a structured test activity in which testers applyadversarial attacks-crafted or perturbed inputs-to intentionally expose weaknesses in the ML model. The purpose is to identify vulnerabilities that could be exploited throughdata poisoning,evasion attacks, orinput manipulation. OptionCcorrectly reflects this syllabus definition: adversarial testing is aboutusing attacks to locate weaknesses so they can be removed or mitigated.
Option A is incorrect because regression testing does not verify data sourcing policies; it verifies unchanged functionality after modifications. Option B is incorrect because adversarial examples are oftenaddedto training datasets to improverobustness(a practice called adversarial training), not excluded. Option D is incorrect because AIB testing is not described as superior to exploratory data analysis in outlier detection; both have different purposes, and EDA remains essential for data quality assessment.
Thus,Option Cis consistent with syllabus-defined adversarial testing.

NEW QUESTION # 133
Which ONE of the following activities is MOST relevant when addressing the scenario where you have more than the required amount of data available for the training?

A. Data labeling
B. Data sampling
C. Data augmentation
D. Feature selection

Answer: B

Explanation:
Data sampling involves selecting a representative subset of the data for training. When there is more data than needed, sampling can be used to create a manageable dataset that maintains the statistical properties of the full dataset.

NEW QUESTION # 134
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