Free Oracle 1Z0-1127-25 Exam Questions

Become Oracle Certified with updated 1Z0-1127-25 exam questions and correct answers

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Total 234 Questions | Updated On: Oct 29, 2025

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Question 1

What is the purpose of the "stop sequence" parameter in the OCI Generative AI Generation models?

A : It specifies a string that tells the model to stop generating more content.

B : It assigns a penalty to frequently occurring tokens to reduce repetitive text.

C : It determines the maximum number of tokens the model can generate per response.

D : It controls the randomness of the model-s output, affecting its creativity.

Answer: A

Question 2

What is the purpose of Retrieval Augmented Generation (RAG) in text generation?

A : To generate text based only on the model-s internal knowledge without external data

B : To generate text using extra information obtained from an external data source

C : To store text in an external database without using it for generation

D : To retrieve text from an external source and present it without any modifications

Answer: B

Question 3

What differentiates a code model from a standard LLM?

A : Code models are specifically designed to generate different creative text formats.

B : Code models are trained on datasets containing code alongside natural language.

C : Code models require a different neural network architecture compared to LLMs.

D : Code models can only process and understand code, not natural language.

Answer: B

Question 4

When is fine-tuning an appropriate method for customizing a Large Language Model (LLM)?

A : When the LLM requires access to the latest data for generating outputs

B : When the LLM already understands the topics necessary for text generation

C : When the LLM does not perform well on a task and the data for prompt engineering is too large

Answer: C

Question 5

How can fine-tuning an LLM model with OCI Generative AI contribute to improved performance of semantic search in your application?

A : By enabling the LLM to directly generate documents based on semantic similarity.

B : By allowing the LLM to interpret the retrieved documents and provide more context-aware responses.

C : By improving the accuracy of vector representations generated for the documents in the database.

D : By optimizing the search algorithms used by the vector database for specific LLM tasks.

Answer: B

Page: 1 / 47

Total 234 Questions | Updated On: Oct 29, 2025

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