1Z0-1127-25 Free Exam Questions & Answers (Updated 2026)

What is the role of temperature in the decoding process of a Large Language Model (LLM)?

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To adjust the sharpness of probability distribution over vocabulary when selecting the next word

Which statement accurately reflects the differences between these approaches in terms of the number of parameters modified and the type of data used?

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Fine - tuning and continuous pretraining both modify all parameters and use labeled, task - specific data.

Parameter Efficient Fine - Tuning and Soft Prompting modify all parameters of the model using unlabeled data.

Fine - tuning modifies all parameters using labeled, task - specific data, whereas Parameter Efficient Fine - Tuning updates a few, new parameters also with labeled, task - specific data.

Soft Prompting and continuous pretraining are both methods that require no modification to the original parameters of the model.

What is prompt engineering in the context of Large Language Models (LLMs)?

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What does the term "hallucination" refer to in the context of Large Language Models (LLMs)?

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The phenomenon where the model generates factually incorrect information or unrelated content as if it were true

What does in - context learning in Large Language Models involve?

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Oracle 1Z0-1127-25 Free Exam Questions (Updated 2026)