iSQI CT-AI Exam Dumps

The syllabus defines overfitting as:

''Overfitting is when the ML model learns the training data so well that it is unable to generalize to accommodate new data.''

This occurs when the model memorizes the training data, including noise, instead of learning the general patterns.

(Reference: ISTQB CT-AI Syllabus v1.0, Section 3.5.1, page 31 of 99)

The ISTQB CT-AI syllabus (Section3.2 -- Model Evaluation) specifies the correct usage oftraining,validation, andtestdatasets. It emphasizes that thetest dataset must be representative of the real operational dataand must beequivalent in distribution to the training and validation sets, ensuring a fair and unbiased evaluation. Option D precisely matches this requirement.

Option A contradicts the syllabus because validation and test sets servedifferent purposes: validation is for tuning, test is for final evaluation. Combining them undermines the reliability of results. Option B is incorrect because even with limited data, the syllabus recommends maintaining a test set or using techniques such ascross-validationrather than eliminating testing. Option C is wrong because equal distribution (33/33/33) isnot recommended; typically, the training set is much larger (e.g., 70--80%).

Thus, OptionDis the only statement aligned with the syllabus' guidance.

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.

The ISTQB CT-AI syllabus emphasizes that testing AI-based systems requirescross-functional collaborationandexperience-based testingwhen parts of the team lack domain knowledge. In this scenario, the ML expert understands ML and dataset preparation but lacks knowledge ofcamera system behavior, the device's operational data pipeline, and end-user workflows. The remainder of the team understands the domain and system testing but not ML. Section4.4 -- Human Factors and AI Testingand4.3 -- System Testing of AI Componentshighlight that when domain understanding is unevenly distributed,experience-based testing conducted by the full team(testers, developers, domain experts) is the most effective approach. This ensures that AI outputs align with actual user expectations and system behavior. OptionCaligns exactly with this principle.

Option A is too limited and does not address the need to validate ML integration. Option B is incorrect because reusing old test cases overlooks AI-specific risks in the operating data pipeline. Option D is useful but focuses only on data representativeness, not system-level user validation. Therefore,Option Cis the best, syllabus-aligned test approach.

The syllabus lists multiple reasons for mislabelled data, including the lack of domain knowledge:

'Lack of required domain knowledge may lead to incorrect labelling.'

(Reference: ISTQB CT-AI Syllabus v1.0, Section 4.5.2, page 38 of 99)