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Domain 4Objective 6

Approaches to Data Labelling CT-AI Practice Questions (Page 7)

Part of the Domain 4: ML - Data domain, which makes up ~12% of our current practice bank. ISTQB does not publish an official question count, but from its 60-minute exam (~25–40 total, ~3–5 in this domain), expect 1–1 from this objective — we provide 37 practice questions to prepare you well beyond it. (estimate)

37questions here
8free pages
10concepts

Questions 31–35

  1. 31foundation · medium

    Which of the following is a disadvantage of manual labelling?

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  2. 32foundation · medium

    Which of the following is a common challenge when using crowdsourcing for labelling?

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  3. 33foundation · medium

    What is a common trade-off of automated labelling compared to manual labelling?

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  4. 34expert · hard

    A team is labelling video data for a model that detects actions in surveillance footage. They need to label the start and end times of each action, as well as the type of action. What is the most appropriate labelling approach, and what is a key challenge?

    Select an answer first
  5. 35expert · hard

    A team is labelling medical images for a model that detects tumors. They have two expert radiologists and a group of trained technicians. The radiologists label a small subset, and the technicians label the rest. To ensure the technicians' labels are consistent with the radiologists' labels, what is the best approach?

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