Which strategy can help mitigate hallucinations in LLM outputs?

Increasing the model's temperature setting

Cross-checking the output with reliable external sources

Ignoring the output and using it as-is

What is the key difference between a reasoning error and a hallucination in LLM outputs?

A reasoning error involves incorrect logic, while a hallucination involves fabricated facts.

A reasoning error is always intentional, while a hallucination is accidental.

A reasoning error is easier to detect than a hallucination.

A reasoning error is a type of hallucination.

Which approach is effective for mitigating biases in LLM outputs?

Using diverse and representative training data

Using the model only for simple tasks

Avoiding the use of any external data

Increasing the model's context window

A test manager wants to reduce the risk of hallucinated test data in a critical release. The team uses an LLM to generate user profiles for performance testing. Which mitigation strategy is most effective?

Ask the LLM to self-review its output before submission

Use a smaller model to reduce the chance of overfitting

Implement a validation step that checks generated data against a known-good dataset

Increase the LLM's temperature to generate more diverse data

Certified Tester Testing with Generative AI

Domain 3Objective 1

Identify Hallucinations, Reasoning Errors and Biases in LLM Output CT-GENAI Practice Questions (Page 6)

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 26–30

26foundation · easy
Which strategy can help mitigate hallucinations in LLM outputs?
Select an answer first
27foundation · easy
What is the key difference between a reasoning error and a hallucination in LLM outputs?
Select an answer first
28foundation · easy
Which approach is effective for mitigating biases in LLM outputs?
Select an answer first
29application · medium
A test manager wants to reduce the risk of hallucinated test data in a critical release. The team uses an LLM to generate user profiles for performance testing. Which mitigation strategy is most effective?
Select an answer first
30foundation · easy
Which technique is used to detect bias in LLM outputs?
Select an answer first

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