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Certified Tester Testing with Generative AI

Domain 3Objective 1

Identify Hallucinations, Reasoning Errors and Biases in LLM Output CT-GENAI Practice Questions (Page 2)

Part of the Managing Risks of Generative AI in Software Testing domain, which makes up ~18% of our current practice bank. ISTQB does not publish an official question count, but from its 60-minute exam (~25–40 total, ~5–7 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
12concepts

Questions 6–10

  1. 6application · medium

    An LLM is asked to generate a test case for a login function. It outputs: 'Enter valid credentials and click Login. The system should display an error message if the password is incorrect.' The team notices the output is logically sound but omits the expected behavior for a locked account. How should this output be classified?

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  2. 7foundation · easy

    Why is it important to recognize biases in LLM outputs when using them for software testing?

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  3. 8foundation · easy

    Which of the following is an example of gender bias in an LLM output?

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  4. 9application · medium

    An LLM is used to generate test oracles for a sorting algorithm. The LLM outputs: 'If the input array is [3,1,2], the expected output is [1,2,3].' The team verifies this is correct. However, the LLM also states: 'If the input array is [5,4,3], the expected output is [3,4,5] because the algorithm uses a stable sort.' The algorithm is actually an unstable sort. How should the second statement be classified?

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  5. 10foundation · easy

    Which type of hallucination occurs when an LLM produces a statement that contradicts the information provided in the prompt?

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