[2026] Pass Key features of 1Z0-1127-25 Course with Updated 90 Questions [Q21-Q39]

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[2026] Pass Key features of 1Z0-1127-25 Course with Updated 90 Questions

1Z0-1127-25 Sample Practice Exam Questions 2026 Updated Verified

Oracle 1Z0-1127-25 Exam Syllabus Topics:

Topic	Details
Topic 1	Implement RAG Using OCI Generative AI Service: This section tests the knowledge of Knowledge Engineers and Database Specialists in implementing Retrieval-Augmented Generation (RAG) workflows using OCI Generative AI services. It covers integrating LangChain with Oracle Database 23ai, document processing techniques like chunking and embedding, storing indexed chunks in Oracle Database 23ai, performing similarity searches, and generating responses using OCI Generative AI.
Topic 2	Using OCI Generative AI RAG Agents Service: This domain measures the skills of Conversational AI Developers and AI Application Architects in creating and managing RAG agents using OCI Generative AI services. It includes building knowledge bases, deploying agents as chatbots, and invoking deployed RAG agents for interactive use cases. The focus is on leveraging generative AI to create intelligent conversational systems.
Topic 3	Fundamentals of Large Language Models (LLMs): This section of the exam measures the skills of AI Engineers and Data Scientists in understanding the core principles of large language models. It covers LLM architectures, including transformer-based models, and explains how to design and use prompts effectively. The section also focuses on fine-tuning LLMs for specific tasks and introduces concepts related to code models, multi-modal capabilities, and language agents.
Topic 4	Using OCI Generative AI Service: This section evaluates the expertise of Cloud AI Specialists and Solution Architects in utilizing Oracle Cloud Infrastructure (OCI) Generative AI services. It includes understanding pre-trained foundational models for chat and embedding, creating dedicated AI clusters for fine-tuning and inference, and deploying model endpoints for real-time inference. The section also explores OCI’s security architecture for generative AI and emphasizes responsible AI practices.

NO.21 How are prompt templates typically designed for language models?

As complex algorithms that require manual compilation

As predefined recipes that guide the generation of language model prompts

To be used without any modification or customization

To work only with numerical data instead of textual content

NO.22 What does accuracy measure in the context of fine-tuning results for a generative model?

The number of predictions a model makes, regardless of whether they are correct or incorrect

The proportion of incorrect predictions made by the model during an evaluation

How many predictions the model made correctly out of all the predictions in an evaluation

The depth of the neural network layers used in the model

NO.23 In the simplified workflow for managing and querying vector data, what is the role of indexing?

To convert vectors into a non-indexed format for easier retrieval

To map vectors to a data structure for faster searching, enabling efficient retrieval

To compress vector data for minimized storage usage

To categorize vectors based on their originating data type (text, images, audio)

NO.24 An AI development company is working on an advanced AI assistant capable of handling queries in a seamless manner. Their goal is to create an assistant that can analyze images provided by users and generate descriptive text, as well as take text descriptions and produce accurate visual representations. Considering the capabilities, which type of model would the company likely focus on integrating into their AI assistant?

A diffusion model that specializes in producing complex outputs.

A Large Language Model-based agent that focuses on generating textual responses

A language model that operates on a token-by-token output basis

A Retrieval Augmented Generation (RAG) model that uses text as input and output

NO.25 What is the main advantage of using few-shot model prompting to customize a Large Language Model (LLM)?

It allows the LLM to access a larger dataset.

It eliminates the need for any training or computational resources.

It provides examples in the prompt to guide the LLM to better performance with no training cost.

It significantly reduces the latency for each model request.

NO.26 An LLM emits intermediate reasoning steps as part of its responses. Which of the following techniques is being utilized?

In-context Learning

Step-Back Prompting

Least-to-Most Prompting

Chain-of-Thought

NO.27 Which is NOT a built-in memory type in LangChain?

ConversationImageMemory

ConversationBufferMemory

ConversationSummaryMemory

ConversationTokenBufferMemory

NO.28 What does a cosine distance of 0 indicate about the relationship between two embeddings?

They are completely dissimilar

They are unrelated

They are similar in direction

They have the same magnitude

NO.29 Which is a cost-related benefit of using vector databases with Large Language Models (LLMs)?

They require frequent manual updates, which increase operational costs.

They offer real-time updated knowledge bases and are cheaper than fine-tuned LLMs.

They increase the cost due to the need for real-time updates.

They are more expensive but provide higher quality data.

NO.30 Which component of Retrieval-Augmented Generation (RAG) evaluates and prioritizes the information retrieved by the retrieval system?

Encoder-Decoder

Ranker

Generator

Retriever

NO.31 Given the following code block:
history = StreamlitChatMessageHistory(key=”chat_messages”)
memory = ConversationBufferMemory(chat_memory=history)
Which statement is NOT true about StreamlitChatMessageHistory?

StreamlitChatMessageHistory will store messages in Streamlit session state at the specified key.

A given StreamlitChatMessageHistory will NOT be persisted.

A given StreamlitChatMessageHistory will not be shared across user sessions.

StreamlitChatMessageHistory can be used in any type of LLM application.

NO.32 What is the primary function of the “temperature” parameter in the OCI Generative AI Generation models?

Controls the randomness of the model’s output, affecting its creativity

Specifies a string that tells the model to stop generating more content

Assigns a penalty to tokens that have already appeared in the preceding text

Determines the maximum number of tokens the model can generate per response

NO.33 How do Dot Product and Cosine Distance differ in their application to comparing text embeddings in natural language processing?

Dot Product assesses the overall similarity in content, whereas Cosine Distance measures topical relevance.

Dot Product is used for semantic analysis, whereas Cosine Distance is used for syntactic comparisons.

Dot Product measures the magnitude and direction of vectors, whereas Cosine Distance focuses on the orientation regardless of magnitude.

Dot Product calculates the literal overlap of words, whereas Cosine Distance evaluates the stylistic similarity.

NO.34 How does a presence penalty function in language model generation when using OCI Generative AI service?

It penalizes all tokens equally, regardless of how often they have appeared.

It only penalizes tokens that have never appeared in the text before.

It applies a penalty only if the token has appeared more than twice.

It penalizes a token each time it appears after the first occurrence.

NO.35 What differentiates Semantic search from traditional keyword search?

It relies solely on matching exact keywords in the content.

It depends on the number of times keywords appear in the content.

It involves understanding the intent and context of the search.

It is based on the date and author of the content.

NO.36 What is the function of the Generator in a text generation system?

To collect user queries and convert them into database search terms

To rank the information based on its relevance to the user’s query

To generate human-like text using the information retrieved and ranked, along with the user’s original query

To store the generated responses for future use

NO.37 What is LangChain?

A JavaScript library for natural language processing

A Python library for building applications with Large Language Models

A Java library for text summarization

A Ruby library for text generation

NO.38 What is the purpose of Retrieval Augmented Generation (RAG) in text generation?

To generate text based only on the model’s internal knowledge without external data

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

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

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

NO.39 When is fine-tuning an appropriate method for customizing a Large Language Model (LLM)?

When the LLM already understands the topics necessary for text generation

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

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

When you want to optimize the model without any instructions

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Oracle 1Z0-1127-25 Exam Syllabus Topics:

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