Free Oracle 1Z0-1127-25 Practice Test & Real Exam Questions

Exam Code/Number: 1Z0-1127-25
Exam Name/Title: Oracle Cloud Infrastructure 2025 Generative AI Professional
Certification Provider: Oracle
Corresponding Certification: Oracle Cloud Infrastructure

Exam Questions: 90
Updated On: Jul 01, 2026

Question #1

Why is normalization of vectors important before indexing in a hybrid search system?

A. It converts all sparse vectors to dense vectors.

B. It significantly reduces the size of the database.

C. It ensures that all vectors represent keywords only.

D. It standardizes vector lengths for meaningful comparison using metrics such as Cosine Similarity.

Discussion 0

Correct Answer: D Vote an answer

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Question #2

What is LCEL in the context of LangChain Chains?

A. A programming language used to write documentation for LangChain

B. A declarative way to compose chains together using LangChain Expression Language

C. A legacy method for creating chains in LangChain

D. An older Python library for building Large Language Models

Discussion 0

Correct Answer: B Vote an answer

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Question #3

How does the structure of vector databases differ from traditional relational databases?

A. It uses simple row-based data storage.

B. A vector database stores data in a linear or tabular format.

C. It is based on distances and similarities in a vector space.

D. It is not optimized for high-dimensional spaces.

Discussion 0

Correct Answer: C Vote an answer

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Question #4

What is the function of "Prompts" in the chatbot system?

A. They are responsible for the underlying mechanics of the chatbot.

B. They are used to initiate and guide the chatbot's responses.

C. They handle the chatbot's memory and recall abilities.

D. They store the chatbot's linguistic knowledge.

Discussion 0

Correct Answer: B Vote an answer

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Question #5

What distinguishes the Cohere Embed v3 model from its predecessor in the OCI Generative AI service?

A. Support for tokenizing longer sentences

B. Improved retrievals for Retrieval Augmented Generation (RAG) systems

C. Capacity to translate text in over 100 languages

D. Emphasis on syntactic clustering of word embeddings

Discussion 0

Correct Answer: B Vote an answer

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Question #6

When should you use the T-Few fine-tuning method for training a model?

A. For datasets with hundreds of thousands to millions of samples

B. For models that require their own hosting dedicated AI cluster

C. For complicated semantic understanding improvement

D. For datasets with a few thousand samples or less

Discussion 0

Correct Answer: D Vote an answer

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Question #7

Which statement is true about Fine-tuning and Parameter-Efficient Fine-Tuning (PEFT)?

A. Both Fine-tuning and PEFT require the model to be trained from scratch on new data, making them equally data and computationally intensive.

B. Fine-tuning requires training the entire model on new data, often leading to substantial computational costs, whereas PEFT involves updating only a small subset of parameters, minimizing computational requirements and data needs.

C. Fine-tuning and PEFT do not involve model modification; they differ only in the type of data used for training, with Fine-tuning requiring labeled data and PEFT using unlabeled data.

D. PEFT requires replacing the entire model architecture with a new one designed specifically for the new task, making it significantly more data-intensive than Fine-tuning.

Discussion 0

Correct Answer: B Vote an answer

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Question #8

Which is NOT a category of pretrained foundational models available in the OCI Generative AI service?

A. Summarization models

B. Generation models

C. Embedding models

D. Translation models

Discussion 0

Correct Answer: D Vote an answer

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Question #9

In which scenario is soft prompting especially appropriate compared to other training styles?

A. When there is a significant amount of labeled, task-specific data available.

B. When the model requires continued pre-training on unlabeled data.

C. When the model needs to be adapted to perform well in a different domain it was not originally trained on.

D. When there is a need to add learnable parameters to a Large Language Model (LLM) without task-specific training.

Discussion 0

Correct Answer: D Vote an answer

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