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AIF-C01 practice question: GenAI capabilities, limits and value (1 of 2)

Fundamentals of Generative AI A free question from our original AIF-C01 practice exam. Try it, then open the answer to see why each option is right or wrong.

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A travel firm's customer chatbot tells a guest, in fluent and confident language, about a spa at a hotel that does not exist.

No one checked the reply before it was sent.

Which generative AI limitation does this error describe?

Choose one answer.

  1. A. Overfitting, where a model memorizes its training examples too closely
  2. B. Latency, the time a model takes to return a response
  3. C. Data drift, a gradual change in the data the model sees in production
  4. D. Hallucination, output that sounds plausible but is false or unsupported
Show the answer and explanation

Answer: D

  • A. Wrong: Overfitting is a training problem in which a model learns its training examples too closely and then performs poorly on new inputs. It does not describe a confident, invented answer to a customer question.
  • B. Wrong: Latency measures how long a response takes to arrive. The problem in this chatbot is the content of the reply, not how quickly it came back.
  • C. Wrong: Data drift is a change over time in the inputs a deployed model receives, which can lower its accuracy. It describes a pattern across many inputs, not one fabricated fact.
  • D. Right: Correct. A hallucination is output that reads as fluent and certain but is false or not supported by any source. Grounding answers in trusted data and checking responses reduce the risk.

Explanation

Generative models predict likely text, so they can produce fluent statements that are not true. AWS's overview of generative AI notes that these systems can sometimes produce inaccurate or misleading information. Grounding answers in trusted data and checking responses reduce the risk.

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