1Z0-1127-25 LATEST MATERIALS, 1Z0-1127-25 RELIABLE TEST BOOTCAMP

1Z0-1127-25 Latest Materials, 1Z0-1127-25 Reliable Test Bootcamp

1Z0-1127-25 Latest Materials, 1Z0-1127-25 Reliable Test Bootcamp

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While making revisions and modifications to the Oracle Cloud Infrastructure 2025 Generative AI Professional (1Z0-1127-25) practice exam, our team takes reports from over 90,000 professionals worldwide to make the Oracle Cloud Infrastructure 2025 Generative AI Professional (1Z0-1127-25) exam questions foolproof. To make you capable of preparing for the Oracle 1Z0-1127-25 exam smoothly, we provide actual Oracle 1Z0-1127-25 exam dumps.

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
  • 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 (Q61-Q66):

NEW QUESTION # 61
Which statement best describes the role of encoder and decoder models in natural language processing?

  • A. Encoder models convert a sequence of words into a vector representation, and decoder models take this vector representation to generate a sequence of words.
  • B. Encoder models take a sequence of words and predict the next word in the sequence, whereas decoder models convert a sequence of words into a numerical representation.
  • C. Encoder models and decoder models both convert sequences of words into vector representations without generating new text.
  • D. Encoder models are used only for numerical calculations, whereas decoder models are used to interpret the calculated numerical values back into text.

Answer: A

Explanation:
Comprehensive and Detailed In-Depth Explanation=
In NLP (e.g., transformers), encoders convert input text into a vector representation (encoding meaning), while decoders generate text from such vectors (e.g., in translation or generation). This makes Option C correct. Option A is false-decoders generate text. Option B reverses roles-encoders don't predict next words, and decoders don't encode. Option D oversimplifies-encoders handle text, not just numbers. This is the foundation of seq2seq models.
OCI 2025 Generative AI documentation likely explains encoder-decoder roles under model architecture.


NEW QUESTION # 62
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 perform complex calculations unrelated to user interaction
  • D. To act as a static database for storing permanent records

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 # 63
Given the following code block:
history = StreamlitChatMessageHistory(key="chat_messages")
memory = ConversationBufferMemory(chat_memory=history)
Which statement is NOT true about StreamlitChatMessageHistory?

  • A. A given StreamlitChatMessageHistory will not be shared across user sessions.
  • B. StreamlitChatMessageHistory can be used in any type of LLM application.
  • C. StreamlitChatMessageHistory will store messages in Streamlit session state at the specified key.
  • D. A given StreamlitChatMessageHistory will NOT be persisted.

Answer: B

Explanation:
Comprehensive and Detailed In-Depth Explanation=
StreamlitChatMessageHistory integrates with Streamlit's session state to store chat history, tied to a specific key (Option A, true). It's not persisted beyond the session (Option B, true) and isn't shared across users (Option C, true), as Streamlit sessions are user-specific. However, it's designed specifically for Streamlit apps, not universally for any LLM application (e.g., non-Streamlit contexts), making Option D NOT true.
OCI 2025 Generative AI documentation likely references Streamlit integration under LangChain memory options.


NEW QUESTION # 64
How can the concept of "Groundedness" differ from "Answer Relevance" in the context of Retrieval Augmented Generation (RAG)?

  • A. Groundedness focuses on data integrity, whereas Answer Relevance emphasizes lexical diversity.
  • B. Groundedness measures relevance to the user query, whereas Answer Relevance evaluates data integrity.
  • C. Groundedness refers to contextual alignment, whereas Answer Relevance deals with syntactic accuracy.
  • D. Groundedness pertains to factual correctness, whereas Answer Relevance concerns query relevance.

Answer: D

Explanation:
Comprehensive and Detailed In-Depth Explanation=
In RAG, "Groundedness" assesses whether the response is factually correct and supported by retrieved data, while "Answer Relevance" evaluates how well the response addresses the user's query. Option A captures this distinction accurately. Option B is off-groundedness isn't just contextual alignment, and relevance isn't about syntax. Option C swaps the definitions. Option D misaligns-groundedness isn't solely data integrity, and relevance isn't lexical diversity. This distinction ensures RAG outputs are both true and pertinent.
OCI 2025 Generative AI documentation likely defines these under RAG evaluation metrics.


NEW QUESTION # 65
What does in-context learning in Large Language Models involve?

  • A. Pretraining the model on a specific domain
  • B. Adding more layers to the model
  • C. Training the model using reinforcement learning
  • D. Conditioning the model with task-specific instructions or demonstrations

Answer: D

Explanation:
Comprehensive and Detailed In-Depth Explanation=
In-context learning is a capability of LLMs where the model adapts to a task by interpreting instructions or examples provided in the input prompt, without additional training. This leverages the model's pre-trained knowledge, making Option C correct. Option A refers to domain-specific pretraining, not in-context learning. Option B involves reinforcement learning, a different training paradigm. Option D pertains to architectural changes, not learning via context.
OCI 2025 Generative AI documentation likely discusses in-context learning in sections on prompt-based customization.


NEW QUESTION # 66
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