ISTQB CT-AI Exam With Confidence Using Practice Dumps

The syllabus states:

"Concept drift occurs when the operational environment changes without the trained model changing correspondingly. The outputs of the model become less accurate and less useful. Therefore, the operational model should be regularly evaluated against its acceptance criteria."

(Reference: ISTQB CT-AI Syllabus v1.0, Section 7.6, Page 54 of 99)

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)

The ISTQB CT-AI syllabus describes severaldata quality aspectsthat affect ML performance. In Section2.2 – Data Preparation, it explains that datasets may suffer from issues such asincomplete data,irrelevant data,incorrect data,unbalanced data, or data lacking preprocessing. “Incomplete data” means thatportions of the required data are missing, often because some time periods, records, or sources were not captured. This aligns exactly with Option A, which correctly identifies missing intervals as incomplete data.

Option B is incorrect: “data not preprocessed” refers to data that has not undergone normalization, cleaning, or transformation—not data recorded incorrectly. Option C is wrong because irrelevant datadoesnegatively affect ML models by introducing noise and unnecessary features. The syllabus explicitly states that including irrelevant features can degrade model learning. Option D is incorrect: “unbalanced data” relates todisproportionate class distribution, not recency or freshness of data.

Thus, OptionAis the only statement that correctly matches the syllabus definition of this data quality aspect.