ISTQB CT-AI Exam With Confidence Using Practice Dumps

Bias detection at thedata levelis performed by reviewingdata acquisition and preprocessing steps, as explained in Section2.3 – Data Quality and Biasof the ISTQB CT-AI syllabus. Sample bias arises when data is distorted or when preprocessing introduces unintended shifts—for example, by filtering, normalization, or labeling steps that disproportionately affect subsets of the data. OptionBcorrectly reflects this: reviewers can identify whether preprocessing steps have altered the dataset in a way that introducessample distortions. This aligns perfectly with syllabus guidance on reviewing data pipelines for bias sources.

Option A is incorrect because algorithmic bias originates from themodel, not data collection procedures. Option C is incorrect because LIME is anexplainabilitymethod applied post-model, not in data reviews. Option D incorrectly states “algorithmic bias,” but preprocessing affectssample bias, not algorithmic bias.

Thus, OptionBcorrectly matches the syllabus’ definition of how bias can be detected during data-related reviews.

The ISTQB CT-AI syllabus defines these two concepts clearly inSection 2.1 – Flexibility and Adaptability. Flexibility is described as the ability of a system to operate in situationsnot explicitly covered in its original requirements, while adaptability refers to how easily the system can bemodifiedto handle new environments or conditions. The syllabus stresses that both flexibility and adaptability are crucial, particularly inself-learning AI systemsthat may need to respond to changes in their environment and adjust their behavior accordingly. It states that systems must be capable of determiningwhenandhowto adjust behavior in evolving situations, especially when the operational environment is not fully known at deployment time . This directly aligns with OptionA.

Option B reverses definitions—the syllabus states flexibility (not adaptability) relates to unspecified situations. Option C is incorrect: self-learning systems requirebothflexibility and adaptability; they are not categorized as one or the other. Option D incorrectly defines flexibility; the syllabus defines adaptability—not flexibility—as ease of modification.

Thus,Option Acorrectly reflects the syllabus.

The syllabus explains that supervised learning requires correctly labeled data so the algorithm can learn the relationship between input features and output labels:

"In supervised learning, the algorithm creates the ML model from labeled data during the training phase. The labeled data is used to infer the relationship between the input data and output labels."

(Reference: ISTQB CT-AI Syllabus v1.0, Section 3.1.1)