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Valid 1Z0-1127-25 Test Syllabus - 1Z0-1127-25 Reliable Test Testking

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Oracle 1Z0-1127-25 Exam Syllabus Topics:
TopicDetails
Topic 1
  • 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 2
  • 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 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.

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Oracle Cloud Infrastructure 2025 Generative AI Professional Sample Questions (Q66-Q71):NEW QUESTION # 66
Which is a key characteristic of Large Language Models (LLMs) without Retrieval Augmented Generation (RAG)?
  • A. They use vector databases exclusively to produce answers.
  • B. They always use an external database for generating responses.
  • C. They cannot generate responses without fine-tuning.
  • D. They rely on internal knowledge learned during pretraining on a large text corpus.
Answer: D
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 # 67
Which statement describes the difference between "Top k" and "Top p" in selecting the next token in the OCI Generative AI Generation models?
  • A. "Top k" and "Top p" both select from the same set of tokens but use different methods to prioritize them based on frequency.
  • B. "Top k" considers the sum of probabilities of the top tokens, whereas "Top p" selects from the "Top k" tokens sorted by probability.
  • C. "Top k" and "Top p" are identical in their approach to token selection but differ in their application of penalties to tokens.
  • D. "Top k" selects the next token based on its position in the list of probable tokens, whereas "Top p" selects based on the cumulative probability of the top tokens.
Answer: D
Explanation:
Comprehensive and Detailed In-Depth Explanation=
"Top k" sampling selects from the k most probable tokens, based on their ranked position, while "Top p" (nucleus sampling) selects from tokens whose cumulative probability exceeds p, focusing on a dynamic probability mass-Option B is correct. Option A is false-they differ in selection, not penalties. Option C reverses definitions. Option D (frequency) is incorrect-both use probability, not frequency. This distinction affects diversity.
OCI 2025 Generative AI documentation likely contrasts Top k and Top p under sampling methods.

NEW QUESTION # 68
How does the integration of a vector database into Retrieval-Augmented Generation (RAG)-based Large Language Models (LLMs) fundamentally alter their responses?
  • A. It shifts the basis of their responses from pretrained internal knowledge to real-time data retrieval.
  • B. It limits their ability to understand and generate natural language.
  • C. It transforms their architecture from a neural network to a traditional database system.
  • D. It enables them to bypass the need for pretraining on large text corpora.
Answer: A
Explanation:
Comprehensive and Detailed In-Depth Explanation=
RAG integrates vector databases to retrieve real-time external data, augmenting the LLM's pretrained knowledge with current, specific information, shifting response generation to a hybrid approach-Option B is correct. Option A is false-architecture remains neural; only data sourcing changes. Option C is incorrect-pretraining is still required; RAG enhances it. Option D is wrong-RAG improves, not limits, generation. This shift enables more accurate, up-to-date responses.
OCI 2025 Generative AI documentation likely details RAG's impact under responsegeneration enhancements.

NEW QUESTION # 69
What is LangChain?
  • A. A JavaScript library for natural language processing
  • B. A Python library for building applications with Large Language Models
  • C. A Ruby library for text generation
  • D. A Java library for text summarization
Answer: B
Explanation:
Comprehensive and Detailed In-Depth Explanation=
LangChain is a Python library designed to simplify building applications with LLMs by providing tools for chaining operations, managing memory, and integrating external data (e.g., via RAG). This makes Option B correct. Options A, C, and D are incorrect, as LangChain is neither JavaScript, Java, nor Ruby-based, nor limited to summarization or generation alone-it's broader in scope. It's widely used for LLM-powered apps.
OCI 2025 Generative AI documentation likely introduces LangChain under supported frameworks.

NEW QUESTION # 70
What does a cosine distance of 0 indicate about the relationship between two embeddings?
  • A. They are similar in direction
  • B. They are completely dissimilar
  • C. They are unrelated
  • D. They have the same magnitude
Answer: A
Explanation:
Comprehensive and Detailed In-Depth Explanation=
Cosine distance measures the angle between two vectors, where 0 means the vectors point in the same direction (cosine similarity = 1), indicating high similarity in embeddings' semantic content-Option C is correct. Option A (dissimilar) aligns with a distance of 1. Option B is vague-directional similarity matters. Option D (magnitude) isn't relevant-cosine ignores magnitude. This is key for semantic comparison.
OCI 2025 Generative AI documentation likely explains cosine distance under vector database metrics.

NEW QUESTION # 71
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