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[General] New 1Z0-1127-25 Test Voucher & Exam 1Z0-1127-25 Fees

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【General】 New 1Z0-1127-25 Test Voucher & Exam 1Z0-1127-25 Fees

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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.

Oracle Cloud Infrastructure 2025 Generative AI Professional Sample Questions (Q83-Q88):NEW QUESTION # 83
Which technique involves prompting the Large Language Model (LLM) to emit intermediate reasoning steps as part of its response?
  • A. Chain-of-Thought
  • B. Step-Back Prompting
  • C. In-Context Learning
  • D. Least-to-Most Prompting
Answer: A
Explanation:
Comprehensive and Detailed In-Depth Explanation=
Chain-of-Thought (CoT) prompting explicitly instructs an LLM to provide intermediate reasoning steps, enhancing complex task performance-Option B is correct. Option A (Step-Back) reframes problems, not emits steps. Option C (Least-to-Most) breaks tasks into subtasks, not necessarily showing reasoning. Option D (In-Context Learning) uses examples, not reasoning steps. CoT improves transparency and accuracy.
OCI 2025 Generative AI documentation likely covers CoT under advanced prompting techniques.

NEW QUESTION # 84
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 keyword search-based AI that responds based on specific keywords identified in customer queries.
  • B. A pre-trained LLM model from Cohere or OpenAI.
  • C. An LLM dedicated to generating text responses without external data integration.
  • D. An LLM enhanced with Retrieval-Augmented Generation (RAG) for dynamic information retrieval and response generation.
Answer: D
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 # 85
What does a higher number assigned to a token signify in the "Show Likelihoods" feature of the language model token generation?
  • A. The token will be the only one considered in the next generation step.
  • B. The token is more likely to follow the current token.
  • C. The token is unrelated to the current token and will not be used.
  • D. The token is less likely to follow the current token.
Answer: B
Explanation:
Comprehensive and Detailed In-Depth Explanation=
In "Show Likelihoods," a higher number (probability score) indicates a token's greater likelihood of following the current token, reflecting the model's prediction confidence-Option B is correct. Option A (less likely) is the opposite. Option C (unrelated) misinterprets-likelihood ties tokens contextually. Option D (only one) assumes greedy decoding, not the feature's purpose. This helps users understand model preferences.
OCI 2025 Generative AI documentation likely explains "Show Likelihoods" under token generation insights.

NEW QUESTION # 86
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 randomly penalize some tokens to increase the diversity of the text
  • C. To penalize tokens that have already appeared, based on the number of times they have been used
  • D. To ensure that tokens that appear frequently are used more often
Answer: C
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 # 87
In the simplified workflow for managing and querying vector data, what is the role of indexing?
  • A. To compress vector data for minimized storage usage
  • B. To categorize vectors based on their originating data type (text, images, audio)
  • C. To map vectors to a data structure for faster searching, enabling efficient retrieval
  • D. To convert vectors into a non-indexed format for easier retrieval
Answer: C
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 # 88
......
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