TOP VALID 1Z0-1127-25 STUDY GUIDE & PERFECT 1Z0-1127-25 PDF QUESTIONS & FANTASTIC DUMPS 1Z0-1127-25 VCE

Top Valid 1Z0-1127-25 Study Guide & Perfect 1Z0-1127-25 PDF Questions & Fantastic Dumps 1Z0-1127-25 Vce

Top Valid 1Z0-1127-25 Study Guide & Perfect 1Z0-1127-25 PDF Questions & Fantastic Dumps 1Z0-1127-25 Vce

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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.

Oracle Cloud Infrastructure 2025 Generative AI Professional Sample Questions (Q65-Q70):

NEW QUESTION # 65
What is the function of "Prompts" in the chatbot system?

  • A. They handle the chatbot's memory and recall abilities.
  • B. They are responsible for the underlying mechanics of the chatbot.
  • C. They are used to initiate and guide the chatbot's responses.
  • D. They store the chatbot's linguistic knowledge.

Answer: C

Explanation:
Comprehensive and Detailed In-Depth Explanation=
Prompts in a chatbot system are inputs provided to the LLM to initiate and steer its responses, often including instructions, context, or examples. They shape the chatbot's behavior without altering its core mechanics, making Option B correct. Option A is false, as knowledge is stored in the model's parameters. Option C relates to the model's architecture, not prompts. Option D pertains to memory systems, not prompts directly. Prompts are key for effective interaction.
OCI 2025 Generative AI documentation likely covers prompts under chatbot design or inference sections.


NEW QUESTION # 66
In which scenario is soft prompting especially appropriate compared to other training styles?

  • A. When there is a need to add learnable parameters to a Large Language Model (LLM) without task-specific training.
  • B. When the model requires continued pre-training on unlabeled data.
  • C. When the model needs to be adapted to perform well in a different domain it was not originally trained on.
  • D. When there is a significant amount of labeled, task-specific data available.

Answer: A

Explanation:
Comprehensive and Detailed In-Depth Explanation=
Soft prompting (e.g., prompt tuning) involves adding trainable parameters (soft prompts) to an LLM's input while keeping the model's weights frozen, adapting it to tasks without task-specific retraining. This is efficient when fine-tuning or large datasets aren't feasible, making Option C correct. Option A suits full fine-tuning, not soft prompting, which avoids extensive labeled data needs. Option B could apply, but domain adaptation often requires more than soft prompting (e.g., fine-tuning). Option D describes continued pretraining, not soft prompting. Soft prompting excels in low-resource customization.
OCI 2025 Generative AI documentation likely discusses soft prompting under parameter-efficient methods.


NEW QUESTION # 67
Given the following code block:
history = StreamlitChatMessageHistory(key="chat_messages")
memory = ConversationBufferMemory(chat_memory=history)
Which statement is NOT true about StreamlitChatMessageHistory?

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

Answer: D

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 # 68
Given the following code:
chain = prompt | llm
Which statement is true about LangChain Expression Language (LCEL)?

  • A. LCEL is a declarative and preferred way to compose chains together.
  • B. LCEL is a legacy method for creating chains in LangChain.
  • C. LCEL is an older Python library for building Large Language Models.
  • D. LCEL is a programming language used to write documentation for LangChain.

Answer: A

Explanation:
Comprehensive and Detailed In-Depth Explanation=
LangChain Expression Language (LCEL) is a declarative syntax (e.g., using | to pipe components) for composing chains in LangChain, combining prompts, LLMs, and other elements efficiently-Option C is correct. Option A is false-LCEL isn't for documentation. Option B is incorrect-it's current, not legacy; traditional Python classes are older. Option D is wrong-LCEL is part of LangChain, not a standalone LLM library. LCEL simplifies chain design.
OCI 2025 Generative AI documentation likely highlights LCEL under LangChain chaincomposition.


NEW QUESTION # 69
What is the characteristic of T-Few fine-tuning for Large Language Models (LLMs)?

  • A. It updates all the weights of the model uniformly.
  • B. It selectively updates only a fraction of weights to reduce the number of parameters.
  • C. It selectively updates only a fraction of weights to reduce computational load and avoid overfitting.
  • D. It increases the training time as compared to Vanilla fine-tuning.

Answer: C

Explanation:
Comprehensive and Detailed In-Depth Explanation=
T-Few fine-tuning (a Parameter-Efficient Fine-Tuning method) updates a small subset of the model's weights, reducing computational cost and mitigating overfitting compared to Vanilla fine-tuning, which updates all weights. This makes Option C correct. Option A describes Vanilla fine-tuning, not T-Few. Option B is incomplete, as it omits the overfitting benefit. Option D is false, as T-Few typically reduces training time due to fewer updates. T-Few balances efficiency and performance.
OCI 2025 Generative AI documentation likely describes T-Few under fine-tuningoptions.


NEW QUESTION # 70
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