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[General] Use Oracle 1Z0-1127-25 Questions - Complete Study Material For Oracle Exam

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【General】 Use Oracle 1Z0-1127-25 Questions - Complete Study Material For Oracle Exam

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After purchasing the 1Z0-1127-25 exam dumps from PDFDumps, you will have access to three formats designed by PDFDumps for the preparation of the Oracle 1Z0-1127-25 exam. These Oracle Cloud Infrastructure 2025 Generative AI Professional exam dumps formats will provide actual Oracle 1Z0-1127-25 PDF Questions to help you prepare for the Oracle 1Z0-1127-25 exam.
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
  • 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 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
  • 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.

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Oracle Cloud Infrastructure 2025 Generative AI Professional Sample Questions (Q76-Q81):NEW QUESTION # 76
An AI development company is working on an AI-assisted chatbot for a customer, which happens to be an online retail company. The goal is to create an assistant that can best answer queries regarding the company policies as well as retain the chat history throughout a session. Considering the capabilities, which type of model would be the best?
  • A. A pre-trained LLM model from Cohere or OpenAI.
  • B. A keyword search-based AI that responds based on specific keywords identified in customer queries.
  • C. An LLM enhanced with Retrieval-Augmented Generation (RAG) for dynamic information retrieval and response generation.
  • D. An LLM dedicated to generating text responses without external data integration.
Answer: C
Explanation:
Comprehensive and Detailed In-Depth Explanation=
For a chatbot needing to answer policy queries (requiring up-to-date, specific data) and retain chat history (context awareness), an LLM with RAG is ideal. RAG integrates external data (e.g., policy documents) via retrieval and supports memory for session-long context, making Option B correct. Option A (keyword search) lacks reasoning and context retention. Option C (standalone LLM) can't dynamically fetch policy data. Option D (pre-trained LLM) is too vague and lacks RAG's capabilities. RAG meets both requirements effectively.
OCI 2025 Generative AI documentation likely highlights RAG for dynamic, context-aware applications.

NEW QUESTION # 77
How does a presence penalty function in language model generation when using OCI Generative AI service?
  • A. It only penalizes tokens that have never appeared in the text before.
  • B. It applies a penalty only if the token has appeared more than twice.
  • C. It penalizes a token each time it appears after the first occurrence.
  • D. It penalizes all tokens equally, regardless of how often they have appeared.
Answer: C
Explanation:
Comprehensive and Detailed In-Depth Explanation=
A presence penalty in LLMs (including OCI's service) reduces the probability of tokens that have already appeared in the output, applying the penalty each time they reoccur after their first use. This discourages repetition, making Option D correct. Option A is false, as penalties depend on prior appearance, not uniform application. Option B is the opposite-penalizing unused tokens isn't the goal. Option C is incorrect, as the penalty isn't threshold-based (e.g., more than twice) but applied per reoccurrence. This enhances output diversity.
OCI 2025 Generative AI documentation likely details presence penalty under generation parameters.

NEW QUESTION # 78
How does the integration of a vector database into Retrieval-Augmented Generation (RAG)-based Large Language Models (LLMs) fundamentally alter their responses?
  • A. It transforms their architecture from a neural network to a traditional database system.
  • B. It shifts the basis of their responses from pretrained internal knowledge to real-time data retrieval.
  • C. It enables them to bypass the need for pretraining on large text corpora.
  • D. It limits their ability to understand and generate natural language.
Answer: B
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 # 79
What is the primary function of the "temperature" parameter in the OCI Generative AI Generation models?
  • A. Specifies a string that tells the model to stop generating more content
  • B. Assigns a penalty to tokens that have already appeared in the preceding text
  • C. Controls the randomness of the model's output, affecting its creativity
  • D. Determines the maximum number of tokens the model can generate per response
Answer: C
Explanation:
Comprehensive and Detailed In-Depth Explanation=
The "temperature" parameter adjusts the randomness of an LLM's output by scaling the softmax distribution-low values (e.g., 0.7) make it more deterministic, high values (e.g., 1.5) increase creativity-Option A is correct. Option B (stop string) is the stop sequence. Option C (penalty) relates to presence/frequency penalties. Option D (max tokens) is a separate parameter. Temperature shapes output style.
OCI 2025 Generative AI documentation likely defines temperature under generation parameters.

NEW QUESTION # 80
What does the Loss metric indicate about a model's predictions?
  • A. Loss indicates how good a prediction is, and it should increase as the model improves.
  • B. Loss describes the accuracy of the right predictions rather than the incorrect ones.
  • C. Loss is a measure that indicates how wrong the model's predictions are.
  • D. Loss measures the total number of predictions made by a model.
Answer: C
Explanation:
Comprehensive and Detailed In-Depth Explanation=
Loss is a metric that quantifies the difference between a model's predictions and the actual target values, indicating how incorrect (or "wrong") the predictions are. Lower loss means better performance, making Option B correct. Option A is false-loss isn't about prediction count. Option C is incorrect-loss decreases as the model improves, not increases. Option D is wrong-loss measures overall error, not just correct predictions. Loss guides training optimization.
OCI 2025 Generative AI documentation likely defines loss under model training and evaluation metrics.

NEW QUESTION # 81
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