1Z0-1127-25 RELEVANT QUESTIONS & 1Z0-1127-25 EXAMCOLLECTION DUMPS

1Z0-1127-25 Relevant Questions & 1Z0-1127-25 Examcollection Dumps

1Z0-1127-25 Relevant Questions & 1Z0-1127-25 Examcollection Dumps

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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.

>> 1Z0-1127-25 Relevant Questions <<

1Z0-1127-25 Relevant Questions Will Be Your Sharpest Sword to Pass Oracle Cloud Infrastructure 2025 Generative AI Professional

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Oracle Cloud Infrastructure 2025 Generative AI Professional Sample Questions (Q48-Q53):

NEW QUESTION # 48
What does the term "hallucination" refer to in the context of Large Language Models (LLMs)?

  • A. The process by which the model visualizes and describes images in detail
  • B. A technique used to enhance the model's performance on specific tasks
  • C. The phenomenon where the model generates factually incorrect information or unrelated content as if it were true
  • D. The model's ability to generate imaginative and creative content

Answer: C

Explanation:
Comprehensive and Detailed In-Depth Explanation=
In LLMs, "hallucination" refers to the generation of plausible-sounding but factually incorrect or irrelevant content, often presented with confidence. This occurs due to the model's reliance on patterns in training data rather than factual grounding, making Option D correct. Option A describes a positive trait, not hallucination. Option B is unrelated, as hallucination isn't a performance-enhancing technique. Option C pertains to multimodal models, not the general definition of hallucination in LLMs.
OCI 2025 Generative AI documentation likely addresses hallucination under model limitations or evaluation metrics.


NEW QUESTION # 49
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 allowing updates across all layers of the model
  • C. By restricting updates to only a specific group of transformer layers
  • D. By incorporating additional layers to the base model

Answer: C

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 # 50
What issue might arise from using small datasets with the Vanilla fine-tuning method in the OCI Generative AI service?

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

Answer: A

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 # 51
Which component of Retrieval-Augmented Generation (RAG) evaluates and prioritizes the information retrieved by the retrieval system?

  • A. Encoder-Decoder
  • B. Generator
  • C. Ranker
  • D. Retriever

Answer: C

Explanation:
Comprehensive and Detailed In-Depth Explanation=
In RAG, the Ranker evaluates and prioritizes retrieved information (e.g., documents) based on relevance to the query, refining what the Retriever fetches-Option D is correct. The Retriever (A) fetches data, not ranks it. Encoder-Decoder (B) isn't a distinct RAG component-it's part of the LLM. The Generator (C) produces text, not prioritizes. Ranking ensures high-quality inputs for generation.
OCI 2025 Generative AI documentation likely details the Ranker under RAG pipeline components.


NEW QUESTION # 52
Which is NOT a category of pretrained foundational models available in the OCI Generative AI service?

  • A. Generation models
  • B. Embedding models
  • C. Translation models
  • D. Summarization models

Answer: C

Explanation:
Comprehensive and Detailed In-Depth Explanation=
OCI Generative AI typically offers pretrained models for summarization (A), generation (B), and embeddings (D), aligning with common generative tasks. Translation models (C) are less emphasized in generative AI services, often handled by specialized NLP platforms, making C the NOT category. While possible, translation isn't a core OCI generative focus based on standard offerings.
OCI 2025 Generative AI documentation likely lists model categories under pretrained options.


NEW QUESTION # 53
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