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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 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.
Topic 3	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 4	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.

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Oracle Cloud Infrastructure 2025 Generative AI Professional Sample Questions (Q48-Q53):

NEW QUESTION # 48
Given the following prompts used with a Large Language Model, classify each as employing the Chain-of-Thought, Least-to-Most, or Step-Back prompting technique:

A. "Solve a complex math problem by first identifying the formula needed, and then solve a simpler version of the problem before tackling the full question."
B. "To understand the impact of greenhouse gases on climate change, let's start by defining what greenhouse gases are. Next, we'll explore how they trap heat in the Earth's atmosphere."A. 1: Step-Back, 2: Chain-of-Thought, 3: Least-to-MostB. 1: Least-to-Most, 2: Chain-of-Thought, 3: Step-BackC. 1: Chain-of-Thought, 2: Step-Back, 3: Least-to-MostD. 1: Chain-of-Thought, 2: Least-to-Most, 3: Step-Back
C. "Calculate the total number of wheels needed for 3 cars. Cars have 4 wheels each. Then, use the total number of wheels to determine how many sets of wheels we can buy with $200 if one set (4 wheels) costs $50."

Answer: B

Explanation:
Comprehensive and Detailed In-Depth Explanation=
Prompt 1: Shows intermediate steps (3 × 4 = 12, then 12 ÷ 4 = 3 sets, $200 ÷ $50 = 4)-Chain-of-Thought.
Prompt 2: Steps back to a simpler problem before the full one-Step-Back.
Prompt 3: OCI 2025 Generative AI documentation likely defines these under prompting strategies.

NEW QUESTION # 49
What is the function of "Prompts" in the chatbot system?

A. They handle the chatbot's memory and recall abilities.
B. They are responsible for the underlying mechanics of the chatbot.
C. They are used to initiate and guide the chatbot's responses.
D. They store the chatbot's linguistic knowledge.

Answer: C

Explanation:
Comprehensive and Detailed In-Depth Explanation=
Prompts in a chatbot system are inputs provided to the LLM to initiate and steer its responses, often including instructions, context, or examples. They shape the chatbot's behavior without altering its core mechanics, making Option B correct. Option A is false, as knowledge is stored in the model's parameters. Option C relates to the model's architecture, not prompts. Option D pertains to memory systems, not prompts directly. Prompts are key for effective interaction.
OCI 2025 Generative AI documentation likely covers prompts under chatbot design or inference sections.

NEW QUESTION # 50
What issue might arise from using small datasets with the Vanilla fine-tuning method in the OCI Generative AI service?

A. Underfitting
B. Data Leakage
C. Model Drift
D. Overfitting

Answer: D

Explanation:
Comprehensive and Detailed In-Depth Explanation=
Vanilla fine-tuning updates all model parameters, and with small datasets, it can overfit-memorizing the data rather than generalizing-leading to poor performance on unseen data. Option A is correct. Option B (underfitting) is unlikely with full updates-overfitting is the risk. Option C (data leakage) depends on data handling, not size. Option D (model drift) relates to deployment shifts, not training. Small datasets exacerbate overfitting in Vanilla fine-tuning.
OCI 2025 Generative AI documentation likely warns of overfitting under Vanilla fine-tuning limitations.

NEW QUESTION # 51
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. A Retrieval Augmented Generation (RAG) model that uses text as input and output
B. A diffusion model that specializes in producing complex outputs.
C. A Large Language Model-based agent that focuses on generating textual responses
D. A language model that operates on a token-by-token output basis

Answer: B

Explanation:
Comprehensive and Detailed In-Depth Explanation=
The task requires bidirectional text-image capabilities: analyzing images to generate text and generating images from text. Diffusion models (e.g., Stable Diffusion) excel at complex generative tasks, including text-to-image and image-to-text with appropriate extensions, making Option A correct. Option B (LLM) is text-only. Option C (token-based LLM) lacks image handling. Option D (RAG) focuses on text retrieval, not image generation. Diffusion models meet both needs.
OCI 2025 Generative AI documentation likely discusses diffusion models under multimodal applications.

NEW QUESTION # 52
Which component of Retrieval-Augmented Generation (RAG) evaluates and prioritizes the information retrieved by the retrieval system?

A. Generator
B. Ranker
C. Retriever
D. Encoder-Decoder

Answer: B

Explanation:
Comprehensive and Detailed In-Depth Explanation=
In RAG, the Ranker evaluates and prioritizes retrieved information (e.g., documents) based on relevance to the query, refining what the Retriever fetches-Option D is correct. The Retriever (A) fetches data, not ranks it. Encoder-Decoder (B) isn't a distinct RAG component-it's part of the LLM. The Generator (C) produces text, not prioritizes. Ranking ensures high-quality inputs for generation.
OCI 2025 Generative AI documentation likely details the Ranker under RAG pipeline components.

NEW QUESTION # 53
......

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Oracle 1Z0-1127-25 Exam Collection | 1Z0-1127-25 Exam PDF

Oracle 1Z0-1127-25 Exam Syllabus Topics:

1Z0-1127-25 Exam PDF - 1Z0-1127-25 Trustworthy Pdf

Oracle Cloud Infrastructure 2025 Generative AI Professional Sample Questions (Q48-Q53):

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