100% PASS 2025 1Z0-1127-25: ORACLE CLOUD INFRASTRUCTURE 2025 GENERATIVE AI PROFESSIONAL LATEST LATEST TEST REPORT

100% Pass 2025 1Z0-1127-25: Oracle Cloud Infrastructure 2025 Generative AI Professional Latest Latest Test Report

100% Pass 2025 1Z0-1127-25: Oracle Cloud Infrastructure 2025 Generative AI Professional Latest Latest Test Report

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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
  • 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 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
  • 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.

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Oracle Cloud Infrastructure 2025 Generative AI Professional Sample Questions (Q72-Q77):

NEW QUESTION # 72
How does the utilization of T-Few transformer layers contribute to the efficiency of the fine-tuning process?

  • A. By excluding transformer layers from the fine-tuning process entirely
  • B. By incorporating additional layers to the base model
  • C. By allowing updates across all layers of the model
  • D. By restricting updates to only a specific group of transformer layers

Answer: D

Explanation:
Comprehensive and Detailed In-Depth Explanation=
T-Few fine-tuning enhances efficiency by updating only a small subset of transformer layers or parameters (e.g., via adapters), reducing computational load-Option D is correct. Option A (adding layers) increases complexity, not efficiency. Option B (all layers) describes Vanilla fine-tuning. Option C (excluding layers) is false-T-Few updates, not excludes. This selective approach optimizes resource use.
OCI 2025 Generative AI documentation likely details T-Few under PEFT methods.


NEW QUESTION # 73
How does a presence penalty function in language model generation?

  • A. It penalizes a token each time it appears after the first occurrence.
  • B. It penalizes only tokens that have never appeared in the text before.
  • C. It applies a penalty only if the token has appeared more than twice.
  • D. It penalizes all tokens equally, regardless of how often they have appeared.

Answer: A

Explanation:
Comprehensive and Detailed In-Depth Explanation=
A presence penalty reduces the probability of tokens that have already appeared in the output, applying the penalty each time they reoccur after their first use, to discourage repetition. This makes Option D correct. Option A (equal penalties) ignores prior appearance. Option B is the opposite-penalizing unused tokens isn't the intent. Option C (more than twice) adds an arbitrary threshold not typically used. Presence penalty enhances output variety.OCI 2025 Generative AI documentation likely details presence penalty under generation control parameters.


NEW QUESTION # 74
What issue might arise from using small datasets with the Vanilla fine-tuning method in the OCI Generative AI service?

  • A. Data Leakage
  • B. Model Drift
  • C. Overfitting
  • D. Underfitting

Answer: C

Explanation:
Comprehensive and Detailed In-Depth Explanation=
Vanilla fine-tuning updates all model parameters, and with small datasets, it can overfit-memorizing the data rather than generalizing-leading to poor performance on unseen data. Option A is correct. Option B (underfitting) is unlikely with full updates-overfitting is the risk. Option C (data leakage) depends on data handling, not size. Option D (model drift) relates to deployment shifts, not training. Small datasets exacerbate overfitting in Vanilla fine-tuning.
OCI 2025 Generative AI documentation likely warns of overfitting under Vanilla fine-tuning limitations.


NEW QUESTION # 75
What does the Ranker do in a text generation system?

  • A. It evaluates and prioritizes the information retrieved by the Retriever.
  • B. It sources information from databases to use in text generation.
  • C. It interacts with the user to understand the query better.
  • D. It generates the final text based on the user's query.

Answer: A

Explanation:
Comprehensive and Detailed In-Depth Explanation=
In systems like RAG, the Ranker evaluates and sorts the information retrieved by the Retriever (e.g., documents or snippets) based on relevance to the query, ensuring the most pertinent data is passed to the Generator. This makes Option C correct. Option A is the Generator's role. Option B describes the Retriever. Option D is unrelated, as the Ranker doesn't interact with users but processes retrieved data. The Ranker enhances output quality by prioritizing relevant content.
OCI 2025 Generative AI documentation likely details the Ranker under RAG pipeline components.


NEW QUESTION # 76
How are prompt templates typically designed for language models?

  • A. As complex algorithms that require manual compilation
  • B. As predefined recipes that guide the generation of language model prompts
  • C. To work only with numerical data instead of textual content
  • D. To be used without any modification or customization

Answer: B

Explanation:
Comprehensive and Detailed In-Depth Explanation=
Prompt templates are predefined, reusable structures (e.g., with placeholders for variables) that guide LLM prompt creation, streamlining consistent input formatting. This makes Option B correct. Option A is false, as templates aren't complex algorithms but simple frameworks. Option C is incorrect, as templates are customizable. Option D is wrong, as they handle text, not just numbers.Templates enhance efficiency in prompt engineering.
OCI 2025 Generative AI documentation likely covers prompt templates under prompt engineering or LangChain tools.
Here is the next batch of 10 questions (21-30) from your list, formatted as requested with detailed explanations. The answers are based on widely accepted principles in generative AI and Large Language Models (LLMs), aligned with what is likely reflected in the Oracle Cloud Infrastructure (OCI) 2025 Generative AI documentation. Typographical errors have been corrected for clarity.


NEW QUESTION # 77
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