ISTQB CT-AI Exam With Confidence Using Practice Dumps

 Challenges for Acquiring Test Data in ML Systems:Compliance needs, the changing nature of data over time, and sourcing data from public sources are significant challenges. Data being generated quickly is generally not a challenge; it can actually be beneficial as it provides more data for training and testing.

 Reference:ISTQB_CT-AI_Syllabus_v1.0, Sections on Data Preparation and Data Quality Issues​​​​.

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 EDA can be used to analyze data to identify outliers and unusual patterns, which can indicate adversarial attacks like data poisoning:

"Testing to detect data poisoning is possible using EDA, as poisoned data may show up as outliers."

(Reference: ISTQB CT-AI Syllabus v1.0, Section 9.1.2, page 67 of 99)