ISTQB Updated CT-AI Exam Questions and Answers by mikhail

When written requirements are given in text documents, the best way to generate test cases is by using Natural Language Processing (NLP). Here's why:

Natural Language Processing (NLP): NLP can analyze and understand human language. It can be used to process textual requirements to extract relevant information and generate test cases. This method is efficient in handling large volumes of textual data and identifying key elements necessary for testing.

Why Not Other Options:

Analyzing source code for generating test cases: This is more suitable for white-box testing where the code is available, but it doesn't apply to text-based requirements.

Machine learning on logs of execution: This approach is used for dynamic analysis based on system behavior during execution rather than static textual requirements.

GUI analysis by computer vision: This is used for testing graphical user interfaces and is not applicable to text-based requirements.

References:This aligns with the methodology discussed in the syllabus under the section on using AI for generating test cases from textual requirements​​.

Clustering is a form ofunsupervised learning, which groups data points based onsimilarities without predefined labels. According toISTQB CT-AI Syllabus, clustering is used in scenarios where:

The objective is to find natural groupings in data.

The dataset does not have labeled outputs.

Patterns and structures need to be identified automatically​.

Analyzing the answer choices:

A. Associating shoppers with their shopping tendencies → Correct

Shoppers can be grouped based on purchasing behaviors(e.g., luxury shoppers vs. budget-conscious shoppers), which is a typical clustering application in market segmentation.

B. Grouping individual fish together based on their types of fins → Incorrect

If thetypes of fins are labeled, it becomes aclassification problem, which requires supervised learning.

C. Classifying muffin purchases based on packaging attractiveness → Incorrect

Classification, not clustering, because attractiveness scores or labels must be predefined.

D. Estimating the expected purchase of cat food after an ad campaign → Incorrect

This is a prediction task, best suited forregression models, which are part of supervised learning.

Thus,Option A is the best answer, asclusteringis used togroup shoppers based on tendencies without predefined labels​.

Certified Tester AI Testing Study Guide References:

ISTQB CT-AI Syllabus v1.0, Section 3.1.2 (Unsupervised Learning - Clustering and Association)

ISTQB CT-AI Syllabus v1.0, Section 3.3 (Selecting a Form of ML - Clustering)​.

AI-based systems can sometimes behave likeblack boxes, where the internal decision-making process is unclear.Transparencyrefers to theability to inspect and understand the training data, algorithms, and decision-making processof the AI system.

Transparency ensures that testers and stakeholders can review how an AI system was trained.

Access totraining datais a key factor in transparency because it allows testers toanalyze biases, completeness, and representativenessof the dataset.

Transparency is an essential characteristic of explainable AI (XAI).

Having access to training data means that testers can investigate how data influences AI behavior.

Regulatory and ethical AI guidelines emphasize transparency.

Many AI ethics frameworks, such asGDPR and Trustworthy AI guidelines, recommend transparency to ensurefair and explainable AI decision-making.

(A) Autonomy❌

Autonomy refers to an AI system’s ability to make decisions independentlywithout human intervention. However,having access to training data does not relate to autonomy, which is more about self-learning and decision-making without human control.

(B) Explorability❌

Explorability refers to the ability to test AI systems interactivelyto understand their behavior, but it does not directly relate to accessing training data.

(D) Accessibility❌

Accessibility refers to the ease with which people can use the system, not the ability to inspect the training data.

Transparency is the ease with which the training data and algorithm used to generate a model can be understood."Transparency: This is considered to be the ease with which the algorithm and training data used to generate the model can be determined."​

Why is Option C Correct?Why Other Options are Incorrect?References from ISTQB Certified Tester AI Testing Study GuideThus,option C is the correct answer, astransparency involves access to training data, allowing testers to understand AI decision-making processes.

For AI-enabled medical devices, the most required aspects for certification are safety, transparency, and side effects. Here’s why:

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.

Why Not Other Options:

Autonomy and Maintainability (Aspects I and II): While important, they are secondary to the immediate concerns of safety, transparency, and managing side effects in life-critical processes.

References:This explanation is aligned with the critical quality characteristics for AI-based systems as mentioned in the ISTQB CT-AI syllabus, focusing on the certification of medical devices​​.