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[General] Pass Guaranteed Quiz 2026 1Z0-1127-25: Oracle Cloud Infrastructure 2025 Generati

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【General】 Pass Guaranteed Quiz 2026 1Z0-1127-25: Oracle Cloud Infrastructure 2025 Generati

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Oracle 1Z0-1127-25 Exam Syllabus Topics:
TopicDetails
Topic 1
  • 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 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
  • 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 (Q14-Q19):NEW QUESTION # 14
What does the Ranker do in a text generation system?
  • A. It sources information from databases to use in text generation.
  • B. It generates the final text based on the user's query.
  • C. It evaluates and prioritizes the information retrieved by the Retriever.
  • D. It interacts with the user to understand the query better.
Answer: C
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 # 15
What does "k-shot prompting" refer to when using Large Language Models for task-specific applications?
  • A. Limiting the model to only k possible outcomes or answers for a given task
  • B. The process of training the model on k different tasks simultaneously to improve its versatility
  • C. Providing the exact k words in the prompt to guide the model's response
  • D. Explicitly providing k examples of the intended task in the prompt to guide the model's output
Answer: D
Explanation:
Comprehensive and Detailed In-Depth Explanation=
"k-shot prompting" (e.g., few-shot) involves providing k examples of a task in the prompt to guide the LLM's output via in-context learning, without additional training. This makes Option B correct. Option A (k words) misinterprets-examples, not word count, matter. Option C (training) confuses prompting with fine-tuning. Option D (k outcomes) is unrelated-k refers to examples, not limits. k-shot leverages pre-trained knowledge efficiently.
OCI 2025 Generative AI documentation likely covers k-shot prompting under prompt engineering techniques.

NEW QUESTION # 16
What is the purpose of Retrieval Augmented Generation (RAG) in text generation?
  • A. To store text in an external database without using it for generation
  • B. To generate text using extra information obtained from an external data source
  • C. To retrieve text from an external source and present it without any modifications
  • D. To generate text based only on the model's internal knowledge without external data
Answer: B
Explanation:
Comprehensive and Detailed In-Depth Explanation=
RAG enhances text generation by combining an LLM's internal knowledge with external data retrieved from sources (e.g., vector databases), improving accuracy and relevance. This makes Option B correct. Option A describes standalone LLMs, not RAG. Option C misrepresents RAG's purpose-data is used, not just stored. Option D is incorrect-RAG generates new text, not just retrieves. RAG is ideal for dynamic, informed responses.
OCI 2025 Generative AI documentation likely explains RAG under advanced generation techniques.

NEW QUESTION # 17
What is the purpose of frequency penalties in language model outputs?
  • A. To reward the tokens that have never appeared in the text
  • B. To ensure that tokens that appear frequently are used more often
  • C. To randomly penalize some tokens to increase the diversity of the text
  • D. To penalize tokens that have already appeared, based on the number of times they have been used
Answer: D
Explanation:
Comprehensive and Detailed In-Depth Explanation=
Frequency penalties reduce the likelihood of repeating tokens that have already appeared in the output, based on their frequency, to enhance diversity and avoid repetition. This makes Option B correct. Option A is the opposite effect. Option C describes a different mechanism (e.g., presence penalty in some contexts). Option D is inaccurate, as penalties aren't random but frequency-based.
OCI 2025 Generative AI documentation likely covers frequency penalties under output control parameters.
Below is the next batch of 10 questions (11-20) from your list, formatted as requested with detailed explanations. These 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 # 18
What is the purpose of embeddings in natural language processing?
  • A. To create numerical representations of text that capture the meaning and relationships between words or phrases
  • B. To increase the complexity and size of text data
  • C. To translate text into a different language
  • D. To compress text data into smaller files for storage
Answer: A
Explanation:
Comprehensive and Detailed In-Depth Explanation=
Embeddings in NLP are dense, numerical vectors that represent words, phrases, or sentences in a way that captures their semantic meaning and relationships (e.g., "king" and "queen" being close in vector space). This enables models to process text mathematically, making Option C correct. Option A is false, as embeddings simplify processing, not increase complexity. Option B relates to translation, not embeddings' primary purpose. Option D is incorrect, as embeddings aren't primarily for compression but for representation.
OCI 2025 Generative AI documentation likely covers embeddings under data preprocessing or vector databases.

NEW QUESTION # 19
......
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