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[General] 1Z0-1127-25 Trusted Exam Resource - 1Z0-1127-25 Exam Questions Pdf

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【General】 1Z0-1127-25 Trusted Exam Resource - 1Z0-1127-25 Exam Questions Pdf

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

Oracle Cloud Infrastructure 2025 Generative AI Professional Sample Questions (Q84-Q89):NEW QUESTION # 84
What is the primary purpose of LangSmith Tracing?
  • A. To debug issues in language model outputs
  • B. To monitor the performance of language models
  • C. To analyze the reasoning process of language models
  • D. To generate test cases for language models
Answer: A
Explanation:
Comprehensive and Detailed In-Depth Explanation=
LangSmith Tracing is a tool for debugging and understanding LLM applications by tracking inputs, outputs, and intermediate steps, helping identify issues in complex chains. This makes Option C correct. Option A (test cases) is a secondary use, not primary. Option B (reasoning) overlaps but isn't the core focus-debugging is. Option D (performance) is broader-tracing targets specific issues. It's essential for development transparency.OCI 2025 Generative AI documentation likely covers LangSmith under debugging or monitoring tools.

NEW QUESTION # 85
Which is a cost-related benefit of using vector databases with Large Language Models (LLMs)?
  • A. They offer real-time updated knowledge bases and are cheaper than fine-tuned LLMs.
  • B. They require frequent manual updates, which increase operational costs.
  • C. They are more expensive but provide higher quality data.
  • D. They increase the cost due to the need for real-time updates.
Answer: A
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 # 86
Which statement is true about string prompt templates and their capability regarding variables?
  • A. They are unable to use any variables.
  • B. They support any number of variables, including the possibility of having none.
  • C. They require a minimum of two variables to function properly.
  • D. They can only support a single variable at a time.
Answer: B
Explanation:
Comprehensive and Detailed In-Depth Explanation=
String prompt templates (e.g., in LangChain) are flexible frameworks that can include zero, one, or multiple variables (placeholders) to customize prompts dynamically. They can be static (no variables) or complex (many variables), making Option C correct. Option A is too restrictive. Option B is false-variables are a core feature. Option D is incorrect, as no minimum is required. This flexibility aids prompt engineering.
OCI 2025 Generative AI documentation likely covers prompt templates under LangChain or prompt design.

NEW QUESTION # 87
Which statement is true about the "Top p" parameter of the OCI Generative AI Generation models?
  • A. "Top p" assigns penalties to frequently occurring tokens.
  • B. "Top p" limits token selection based on the sum of their probabilities.
  • C. "Top p" selects tokens from the "Top k" tokens sorted by probability.
  • D. "Top p" determines the maximum number of tokens per response.
Answer: B
Explanation:
Comprehensive and Detailed In-Depth Explanation=
"Top p" (nucleus sampling) selects tokens whose cumulative probability exceeds a threshold (p), limiting the pool to the smallest set meeting this sum, enhancing diversity-Option C is correct. Option A confuses it with "Top k." Option B (penalties) is unrelated. Option D (max tokens) is a different parameter. Top p balances randomness and coherence.
OCI 2025 Generative AI documentation likely explains "Top p" under sampling methods.
Here is the next batch of 10 questions (81-90) 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 # 88
What differentiates Semantic search from traditional keyword search?
  • A. It is based on the date and author of the content.
  • B. It depends on the number of times keywords appear in the content.
  • C. It involves understanding the intent and context of the search.
  • D. It relies solely on matching exact keywords in the content.
Answer: C
Explanation:
Comprehensive and Detailed In-Depth Explanation=
Semantic search uses embeddings and NLP to understand the meaning, intent, and context behind a query, rather than just matching exact keywords (as in traditional search). This enables more relevant results, even if exact terms aren't present, making Option C correct. Options A and B describe traditional keyword search mechanics. Option D is unrelated, as metadata like date or author isn't the primary focus of semantic search. Semantic search leverages vector representations for deeper understanding.
OCI 2025 Generative AI documentation likely contrasts semantic and keyword search under search or retrieval sections.

NEW QUESTION # 89
......
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