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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	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 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	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.

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Oracle Cloud Infrastructure 2025 Generative AI Professional Sample Questions (Q15-Q20):

NEW QUESTION # 15
How are fine-tuned customer models stored to enable strong data privacy and security in the OCI Generative AI service?

A. Stored in Object Storage encrypted by default
B. Shared among multiple customers for efficiency
C. Stored in Key Management service
D. Stored in an unencrypted form in Object Storage

Answer: A

Explanation:
Comprehensive and Detailed In-Depth Explanation=
In OCI, fine-tuned models are stored in Object Storage, encrypted by default, ensuring privacy and security per cloud best practices-Option B is correct. Option A (shared) violates privacy. Option C (unencrypted) contradicts security standards. Option D (Key Management) stores keys, not models. Encryption protects customer data.
OCI 2025 Generative AI documentation likely details storage security under fine-tuning workflows.

NEW QUESTION # 16
Which is a cost-related benefit of using vector databases with Large Language Models (LLMs)?

A. They increase the cost due to the need for real-time updates.
B. They are more expensive but provide higher quality data.
C. They offer real-time updated knowledge bases and are cheaper than fine-tuned LLMs.
D. They require frequent manual updates, which increase operational costs.

Answer: C

Explanation:
Comprehensive and Detailed In-Depth Explanation=
Vector databases enable real-time knowledge retrieval for LLMs (e.g., in RAG), avoiding the high computational and data costs of fine-tuning an LLM for every update. They store embeddings efficiently, making them a cost-effective alternative to retraining, thus Option B is correct. Option A is false-updates are automated, not manual. Option C misrepresents-real-time capability reduces, not increases, costs compared to fine-tuning. Option D is incorrect-vector databases aren't inherently more expensive; they optimize cost and performance. This makes them economical for dynamic applications.
OCI 2025 Generative AI documentation likely highlights vector database cost benefits under RAG or data management sections.

NEW QUESTION # 17
How are prompt templates typically designed for language models?

A. To be used without any modification or customization
B. To work only with numerical data instead of textual content
C. As predefined recipes that guide the generation of language model prompts
D. As complex algorithms that require manual compilation

Answer: C

Explanation:
Comprehensive and Detailed In-Depth Explanation=
Prompt templates are predefined, reusable structures (e.g., with placeholders for variables) that guide LLM prompt creation, streamlining consistent input formatting. This makes Option B correct. Option A is false, as templates aren't complex algorithms but simple frameworks. Option C is incorrect, as templates are customizable. Option D is wrong, as they handle text, not just numbers.Templates enhance efficiency in prompt engineering.
OCI 2025 Generative AI documentation likely covers prompt templates under prompt engineering or LangChain tools.
Here is the next batch of 10 questions (21-30) from your list, formatted as requested with detailed explanations. The answers are based on widely accepted principles in generative AI and Large Language Models (LLMs), aligned with what is likely reflected in the Oracle Cloud Infrastructure (OCI) 2025 Generative AI documentation. Typographical errors have been corrected for clarity.

NEW QUESTION # 18
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 store text in an external database without using it for generation
C. To generate text using extra information obtained from an external data source
D. To retrieve text from an external source and present it without any modifications

Answer: C

Explanation:
Comprehensive and Detailed In-Depth Explanation=
RAG enhances text generation by combining an LLM's internal knowledge with external data retrieved from sources (e.g., vector databases), improving accuracy and relevance. This makes Option B correct. Option A describes standalone LLMs, not RAG. Option C misrepresents RAG's purpose-data is used, not just stored. Option D is incorrect-RAG generates new text, not just retrieves. RAG is ideal for dynamic, informed responses.
OCI 2025 Generative AI documentation likely explains RAG under advanced generation techniques.

NEW QUESTION # 19
Which is a characteristic of T-Few fine-tuning for Large Language Models (LLMs)?

A. It selectively updates only a fraction of the model's weights.
B. It does not update any weights but restructures the model architecture.
C. It updates all the weights of the model uniformly.
D. It increases the training time as compared to Vanilla fine-tuning.

Answer: A

Explanation:
Comprehensive and Detailed In-Depth Explanation=
T-Few fine-tuning, a Parameter-Efficient Fine-Tuning (PEFT) method, updates only a small fraction of an LLM's weights, reducing computational cost and overfitting risk compared to Vanilla fine-tuning (all weights). This makes Option C correct. Option A describes Vanilla fine-tuning. Option B is false-T-Few updates weights, not architecture. Option D is incorrect-T-Few typically reduces training time. T-Few optimizes efficiency.
OCI 2025 Generative AI documentation likely highlights T-Few under fine-tuning options.

NEW QUESTION # 20
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