* Technology Most Typically Used to Implement AI: Genetic algorithms are a well-known technique used in AI . They are inspired by the process of natural selection and are used to find approximate solutions to optimization and search problems. Unlike search engines, procedural programming, or case control structures, genetic algorithms are specifically designed for evolving solutions and are commonly employed in AI implementations.
* Reference: ISTQB_CT-AI_Syllabus_v1.0, Section 1.4 AI Technologies, which identifies different technologies used to implement AI.

Obtaining appropriate data from users is a significant challenge when testing AI-based systems, as user-provided data may be noisy, unstructured, or highly variable. This can introduce complexities in testing the AI model's robustness and generalizability.

The syllabus explains that input changes that arein the same domainas what was used for training are expected to be handled with adaptability:
"Adaptability refers to the ability of a system to adjust its behavior in response to changes in its environment or inputs. This includes changes to the inputs which are still within the expected operational range of the system, such as resolution changes in images or sensor data." (Reference: ISTQB CT-AI Syllabus v1.0, Section 7.6 and 8.2)

The correct answer is A . Dynamic testing for inappropriate bias may require testers to obtain additional attributes about the input data so that outcomes can be analyzed across relevant groups. The CT-AI syllabus explains that testers may need additional information about attributes potentially related to bias and then correlate those attributes with system results. This is particularly important where bias is based on "hidden" variables that are not explicitly present in the input data but are inferred by the algorithm.
Option B is partly relevant to static testing or review activity, but the question asks specifically about dynamic testing . Option C is incorrect because inappropriate bias may be present even when protected attributes are not explicit model inputs; the model may infer them indirectly from proxy variables. Option D is also incorrect because the syllabus states that measuring how changes in inputs affect outputs for bias-related groups may be performed in production as well as before release. Therefore, obtaining and correlating additional attributes is a valid and often necessary dynamic testing activity.
References/topics: CT-AI Syllabus Chapter 8, Section 8.3 "Testing for Algorithmic, Sample and Inappropriate Bias."
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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.