iSQI CT-AI Exam Dumps

The syllabus states:

''When testing probabilistic and non-deterministic systems, the same input may produce different outputs. Tests need to be run several times to produce statistically valid test results, ensuring that an appropriate number of answers are accurate.''

(Reference: ISTQB CT-AI Syllabus v1.0, Section 8.4, page 58 of 99)

The ISTQB CT-AI syllabus describes theML data preparation workflowin Section2.2 -- Data Preparation. Data preparation consists ofdata gathering,cleaning,transformation, andsampling. The syllabus emphasizes that one significant challenge duringdata gatheringis combining data frommultiple heterogeneous sources, which often differ in structure, quality, and format. Ensuring the resulting dataset is accurate, complete, and representative can be complex, making this a critical challenge in the ML workflow. This aligns directly with OptionC.

Option A is incorrect because erroneous data correction is part ofcleaning, not transformation. Option B contradicts the syllabus: while automation can help,not all steps should be automateddue to the need for expert oversight, especially in detecting subtle data quality issues. Option D is incorrect because sampling continues to involve risk---particularly around representativeness---and the syllabus emphasizes caution, not complacency.

Thus, OptionCis the only statement that accurately reflects the syllabus.

The ISTQB CT-AI syllabus explains that reusing pre-trained models is strongly related tosimilarity between the original task and the new task. Section1.8 -- Pre-trained Models and Transfer Learningstates that reuse is effective when the new task is similar to the original one, such as adapting a cat-classifier to classify dog breeds. The syllabus warns about risks related toinput differences,data preparation inconsistencies,inherited shortcomings, andexplainability issues. These are legitimate objections (matching options A, B, and C) because large differences in image inputs or patterns can undermine transfer learning; misclassification risk can increase; and explainability often decreases when reusing pre-trained models .

However,output differences are NOT a valid concernhere. Both the leaf-based and flower-based ML models classifythe same plant species, meaning theiroutputs are identical. The syllabus does not identify output mismatch as a transfer-learning risk. Real risks concerninputs,bias inheritance,model transparency, andtraining differences---not output labels. Therefore, OptionDdescribes an objection that can be safelyneglected, because output classes are the same and do not hinder reuse.

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)

Section2.4 -- Data Labeling Approachesof the ISTQB CT-AI syllabus explicitly definesAI-assisted data labelingas a hybrid process in which an automated tool performs the initial labeling and human annotators subsequently verify, correct, or complete the labels. This two-step process improves efficiency while retaining human oversight to ensure data quality. The syllabus describes this method as an effective compromise when manual labeling alone would be too slow or costly, and when initial automation can identify obvious patterns before a human provides the final authoritative labels .

Option A (internal labeling) refers to labeling conducted by the organization's own staff but does not imply automation. Option B (crowdsourced labeling) leverages a distributed workforce, typically without automation. Option C (outsourced labeling) transfers labeling tasks to external vendors but similarly does not involve an AI-first step. Only OptionDreflects the two-stage automated-then-human workflow described in the syllabus.

Therefore,AI-assisted data labeling(Option D) is unequivocally correct.