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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 (Q26-Q31):

NEW QUESTION # 26
How do Dot Product and Cosine Distance differ in their application to comparing text embeddings in natural language processing?

A. Dot Product assesses the overall similarity in content, whereas Cosine Distance measures topical relevance.
B. Dot Product is used for semantic analysis, whereas Cosine Distance is used for syntactic comparisons.
C. Dot Product calculates the literal overlap of words, whereas Cosine Distance evaluates the stylistic similarity.
D. Dot Product measures the magnitude and direction of vectors, whereas Cosine Distance focuses on the orientation regardless of magnitude.

Answer: D

Explanation:
Comprehensive and Detailed In-Depth Explanation=
Dot Product computes the raw similarity between two vectors, factoring in both magnitude and direction, while Cosine Distance (or similarity) normalizes for magnitude, focusing solely on directional alignment (angle), making Option C correct. Option A is vague-both measure similarity, not distinct content vs. topicality. Option B is false-both address semantics, not syntax. Option D is incorrect-neither measures word overlap or style directly; they operate on embeddings. Cosine is preferred for normalized semantic comparison.
OCI 2025 Generative AI documentation likely explains these metrics under vector similarity in embeddings.

NEW QUESTION # 27
In the context of generating text with a Large Language Model (LLM), what does the process of greedy decoding entail?

A. Choosing the word with the highest probability at each step of decoding
B. Picking a word based on its position in a sentence structure
C. Using a weighted random selection based on a modulated distribution
D. Selecting a random word from the entire vocabulary at each step

Answer: A

Explanation:
Comprehensive and Detailed In-Depth Explanation=
Greedy decoding selects the word with the highest probability at each step, aiming for locally optimal choices without considering future tokens. This makes Option C correct. Option A (random selection) describes sampling, not greedy decoding. Option B (position-based) isn't how greedy decoding works-it's probability-driven. Option D (weighted random) aligns with top-k or top-p sampling, not greedy. Greedy decoding is fast but can lack diversity.
OCI 2025 Generative AI documentation likely explains greedy decoding under decoding strategies.

NEW QUESTION # 28
When is fine-tuning an appropriate method for customizing a Large Language Model (LLM)?

A. When the LLM already understands the topics necessary for text generation
B. When you want to optimize the model without any instructions
C. When the LLM requires access to the latest data for generating outputs
D. When the LLM does not perform well on a task and the data for prompt engineering is too large

Answer: D

Explanation:
Comprehensive and Detailed In-Depth Explanation=
Fine-tuning is suitable when an LLM underperforms on a specific task and prompt engineering alone isn't feasible due to large, task-specific data that can't be efficiently included in prompts. This adjusts the model's weights, making Option B correct. Option A suggests no customization is needed. Option C favors RAG for latest data, not fine-tuning. Option D is vague-fine-tuning requires data and goals, not just optimization without direction. Fine-tuning excels with substantial task-specific data.
OCI 2025 Generative AI documentation likely outlines fine-tuning use cases under customization strategies.

NEW QUESTION # 29
Which is a key characteristic of Large Language Models (LLMs) without Retrieval Augmented Generation (RAG)?

A. They always use an external database for generating responses.
B. They rely on internal knowledge learned during pretraining on a large text corpus.
C. They cannot generate responses without fine-tuning.
D. They use vector databases exclusively to produce answers.

Answer: B

Explanation:
Comprehensive and Detailed In-Depth Explanation=
LLMs without Retrieval Augmented Generation (RAG) depend solely on the knowledge encoded in their parameters during pretraining on a large, general text corpus. They generate responses basedon this internal knowledge without accessing external data at inference time, making Option B correct. Option A is false, as external databases are a feature of RAG, not standalone LLMs. Option C is incorrect, as LLMs can generate responses without fine-tuning via prompting or in-context learning. Option D is wrong, as vector databases are used in RAG or similar systems, not in basic LLMs. This reliance on pretraining distinguishes non-RAG LLMs from those augmented with real-time retrieval.
OCI 2025 Generative AI documentation likely contrasts RAG and non-RAG LLMs under model architecture or response generation sections.

NEW QUESTION # 30
How are fine-tuned customer models stored to enable strong data privacy and security in the OCI Generative AI service?

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

Answer: D

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 # 31
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