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[General] 2026 1Z0-1127-25 Certification Dumps | High Hit-Rate 1Z0-1127-25 100% Free Guara

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【General】 2026 1Z0-1127-25 Certification Dumps | High Hit-Rate 1Z0-1127-25 100% Free Guara

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

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Oracle Cloud Infrastructure 2025 Generative AI Professional Sample Questions (Q17-Q22):NEW QUESTION # 17
How are documents usually evaluated in the simplest form of keyword-based search?
  • A. According to the length of the documents
  • B. Based on the presence and frequency of the user-provided keywords
  • C. By the complexity of language used in the documents
  • D. Based on the number of images and videos contained in the documents
Answer: B
Explanation:
Comprehensive and Detailed In-Depth Explanation=
In basic keyword-based search, documents are evaluated by matching user-provided keywords, with relevance often determined by their presence and frequency (e.g., term frequency in TF-IDF). This makes Option C correct. Option A (language complexity) is unrelated to simple keyword search. Option B (multimedia) isn't considered in text-based keyword methods. Option D (length) may influence scoring indirectly but isn't the primary metric. Keyword search prioritizes exact matches.
OCI 2025 Generative AI documentation likely contrasts keyword search with semantic search under retrieval methods.

NEW QUESTION # 18
How does the temperature setting in a decoding algorithm influence the probability distribution over the vocabulary?
  • A. Decreasing the temperature broadens the distribution, making less likely words more probable.
  • B. Increasing the temperature flattens the distribution, allowing for more varied word choices.
  • C. Increasing the temperature removes the impact of the most likely word.
  • D. Temperature has no effect on probability distribution; it only changes the speed of decoding.
Answer: B
Explanation:
Comprehensive and Detailed In-Depth Explanation=
Temperature adjusts the softmax distribution in decoding. Increasing it (e.g., to 2.0) flattens the curve, giving lower-probability words a better chance, thus increasing diversity-Option C is correct. Option A exaggerates-top words still have impact, just less dominance. Option B is backwards-decreasing temperature sharpens, not broadens. Option D is false-temperature directly alters distribution, not speed. This controls output creativity.
OCI 2025 Generative AI documentation likely reiterates temperature effects under decoding parameters.

NEW QUESTION # 19
When does a chain typically interact with memory in a run within the LangChain framework?
  • A. After user input but before chain execution, and again after core logic but before output
  • B. Only after the output has been generated
  • C. Continuously throughout the entire chain execution process
  • D. Before user input and after chain execution
Answer: A
Explanation:
Comprehensive and Detailed In-Depth Explanation=
In LangChain, a chain interacts with memory after receiving user input (to retrieve context) but before execution (to inform processing), and again after core logic (to update memory) but before output (to maintain state). This makes Option C correct. Option A misses pre-execution context. Option B misplaces timing. Option D overstates-interaction is at specific stages, not continuous. Memory ensures context-aware responses.
OCI 2025 Generative AI documentation likely details memory interaction under LangChain chain execution.

NEW QUESTION # 20
What is LCEL in the context of LangChain Chains?
  • A. A programming language used to write documentation for LangChain
  • B. An older Python library for building Large Language Models
  • C. A declarative way to compose chains together using LangChain Expression Language
  • D. A legacy method for creating chains in LangChain
Answer: C
Explanation:
Comprehensive and Detailed In-Depth Explanation=
LCEL (LangChain Expression Language) is a declarative syntax in LangChain for composing chains-sequences of operations involving LLMs, tools, and memory. It simplifies chain creation with a readable, modular approach, making Option C correct. Option A is false, as LCEL isn't fordocumentation. Option B is incorrect, as LCEL is current, not legacy. Option D is wrong, as LCEL is part of LangChain, not a standalone LLM library. LCEL enhances flexibility in application design.
OCI 2025 Generative AI documentation likely mentions LCEL under LangChain integration or chain composition.

NEW QUESTION # 21
What is the purpose of memory in the LangChain framework?
  • A. To store various types of data and provide algorithms for summarizing past interactions
  • B. To retrieve user input and provide real-time output only
  • C. To act as a static database for storing permanent records
  • D. To perform complex calculations unrelated to user interaction
Answer: A
Explanation:
Comprehensive and Detailed In-Depth Explanation=
In LangChain, memory stores contextual data (e.g., chat history) and provides mechanisms to summarize or recall past interactions, enabling coherent, context-aware conversations. This makes Option B correct. Option A is too limited, as memory does more than just input/output handling. Option C is unrelated, as memory focuses on interaction context, not abstract calculations. Option D is inaccurate, as memory is dynamic, not a static database. Memory is crucial for stateful applications.
OCI 2025 Generative AI documentation likely discusses memory under LangChain's context management features.

NEW QUESTION # 22
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