100% PASS 2025 ORACLE 1Z0-1127-25: LATEST VALID ORACLE CLOUD INFRASTRUCTURE 2025 GENERATIVE AI PROFESSIONAL STUDY NOTES

100% Pass 2025 Oracle 1Z0-1127-25: Latest Valid Oracle Cloud Infrastructure 2025 Generative AI Professional Study Notes

100% Pass 2025 Oracle 1Z0-1127-25: Latest Valid Oracle Cloud Infrastructure 2025 Generative AI Professional Study Notes

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Tags: Valid 1Z0-1127-25 Study Notes, 1Z0-1127-25 Test Answers, New Braindumps 1Z0-1127-25 Book, Reliable 1Z0-1127-25 Test Answers, Valid Braindumps 1Z0-1127-25 Free

The Oracle 1Z0-1127-25 certification brings multiple career benefits. Reputed firms happily hire you for well-paid jobs when you earn the Oracle Cloud Infrastructure 2025 Generative AI Professional. If you are already an employee of a tech company, you get promotions and salary hikes upon getting the 1Z0-1127-25 credential. All these career benefits come when you crack the Oracle 1Z0-1127-25 certification examination. To pass the 1Z0-1127-25 test, you need to prepare well from updated practice material such as real Oracle 1Z0-1127-25 Exam Questions.

Oracle 1Z0-1127-25 Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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 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
  • 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 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 (Q20-Q25):

NEW QUESTION # 20
When is fine-tuning an appropriate method for customizing a Large Language Model (LLM)?

  • A. When you want to optimize the model without any instructions
  • B. When the LLM already understands the topics necessary for text generation
  • C. When the LLM does not perform well on a task and the data for prompt engineering is too large
  • D. When the LLM requires access to the latest data for generating outputs

Answer: C

Explanation:
Comprehensive and Detailed In-Depth Explanation=
Fine-tuning is suitable when an LLM underperforms on a specific task and prompt engineering alone isn't feasible due to large, task-specific data that can't be efficiently included in prompts. This adjusts the model's weights, making Option B correct. Option A suggests no customization is needed. Option C favors RAG for latest data, not fine-tuning. Option D is vague-fine-tuning requires data and goals, not just optimization without direction. Fine-tuning excels with substantial task-specific data.
OCI 2025 Generative AI documentation likely outlines fine-tuning use cases under customization strategies.


NEW QUESTION # 21
In the simplified workflow for managing and querying vector data, what is the role of indexing?

  • A. To convert vectors into a non-indexed format for easier retrieval
  • B. To categorize vectors based on their originating data type (text, images, audio)
  • C. To compress vector data for minimized storage usage
  • D. To map vectors to a data structure for faster searching, enabling efficient retrieval

Answer: D

Explanation:
Comprehensive and Detailed In-Depth Explanation=
Indexing in vector databases maps high-dimensional vectors to a data structure (e.g., HNSW,Annoy) to enable fast, efficient similarity searches, critical for real-time retrieval in LLMs. This makes Option B correct. Option A is backwards-indexing organizes, not de-indexes. Option C (compression) is a side benefit, not the primary role. Option D (categorization) isn't indexing's purpose-it's about search efficiency. Indexing powers scalable vector queries.
OCI 2025 Generative AI documentation likely explains indexing under vector database operations.


NEW QUESTION # 22
What is the characteristic of T-Few fine-tuning for Large Language Models (LLMs)?

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

Answer: A

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 # 23
Which is a distinctive feature of GPUs in Dedicated AI Clusters used for generative AI tasks?

  • A. GPUs are used exclusively for storing large datasets, not for computation.
  • B. GPUs are shared with other customers to maximize resource utilization.
  • C. Each customer's GPUs are connected via a public Internet network for ease of access.
  • D. The GPUs allocated for a customer's generative AI tasks are isolated from other GPUs.

Answer: D

Explanation:
Comprehensive and Detailed In-Depth Explanation=
In Dedicated AI Clusters (e.g., in OCI), GPUs are allocated exclusively to a customer for their generative AI tasks, ensuring isolation for security, performance, and privacy. This makes Option B correct. Option A describes shared resources, not dedicated clusters. Option C is false, as GPUs are for computation, not storage. Option D is incorrect, as public Internet connections would compromise security and efficiency.
OCI 2025 Generative AI documentation likely details GPU isolation under DedicatedAI Clusters.


NEW QUESTION # 24
Which role does a "model endpoint" serve in the inference workflow of the OCI Generative AI service?

  • A. Evaluates the performance metrics of the custom models
  • B. Updates the weights of the base model during the fine-tuning process
  • C. Serves as a designated point for user requests and model responses
  • D. Hosts the training data for fine-tuning custom models

Answer: C

Explanation:
Comprehensive and Detailed In-Depth Explanation=
A "model endpoint" in OCI's inference workflow is an API or interface where users send requests and receive responses from a deployed model-Option B is correct. Option A (weight updates) occurs during fine-tuning, not inference. Option C (metrics) is for evaluation, not endpoints. Option D (training data) relates to storage, not inference. Endpoints enable real-time interaction.
OCI 2025 Generative AI documentation likely describes endpoints under inference deployment.


NEW QUESTION # 25
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