1Z0-1127-25 TEST ASSESSMENT - 1Z0-1127-25 SAMPLE EXAM

1Z0-1127-25 Test Assessment - 1Z0-1127-25 Sample Exam

1Z0-1127-25 Test Assessment - 1Z0-1127-25 Sample Exam

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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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Quiz 2025 Professional Oracle 1Z0-1127-25: Oracle Cloud Infrastructure 2025 Generative AI Professional Test Assessment

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Oracle Cloud Infrastructure 2025 Generative AI Professional Sample Questions (Q84-Q89):

NEW QUESTION # 84
Which is a key characteristic of the annotation process used in T-Few fine-tuning?

  • A. T-Few fine-tuning involves updating the weights of all layers in the model.
  • B. T-Few fine-tuning uses annotated data to adjust a fraction of model weights.
  • C. T-Few fine-tuning requires manual annotation of input-output pairs.
  • D. T-Few fine-tuning relies on unsupervised learning techniques for annotation.

Answer: B

Explanation:
Comprehensive and Detailed In-Depth Explanation=
T-Few, a Parameter-Efficient Fine-Tuning (PEFT) method, uses annotated (labeled) data to selectively update a small fraction of model weights, optimizing efficiency-Option A is correct. Option B is false-manual annotation isn't required; the data just needs labels. Option C (all layers) describes Vanilla fine-tuning, not T-Few. Option D (unsupervised) is incorrect-T-Few typically uses supervised, annotated data. Annotation supports targeted updates.
OCI 2025 Generative AI documentation likely details T-Few's data requirements under fine-tuning processes.


NEW QUESTION # 85
Which technique involves prompting the Large Language Model (LLM) to emit intermediate reasoning steps as part of its response?

  • A. Step-Back Prompting
  • B. Chain-of-Thought
  • C. In-Context Learning
  • D. Least-to-Most Prompting

Answer: B

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 # 86
What distinguishes the Cohere Embed v3 model from its predecessor in the OCI Generative AI service?

  • A. Emphasis on syntactic clustering of word embeddings
  • B. Capacity to translate text in over 100 languages
  • C. Support for tokenizing longer sentences
  • D. Improved retrievals for Retrieval Augmented Generation (RAG) systems

Answer: D

Explanation:
Comprehensive and Detailed In-Depth Explanation=
Cohere Embed v3, as an advanced embedding model, is designed with improved performance for retrieval tasks, enhancing RAG systems by generating more accurate, contextually rich embeddings. This makes Option B correct. Option A (tokenization) isn't a primary focus-embedding quality is. Option C (syntactic clustering) is too narrow-semantics drives improvement. Option D (translation) isn't an embedding model's role. v3 boosts RAG effectiveness.
OCI 2025 Generative AI documentation likely highlights Embed v3 under supported models or RAG enhancements.


NEW QUESTION # 87
What is the purpose of memory in the LangChain framework?

  • A. To act as a static database for storing permanent records
  • B. To perform complex calculations unrelated to user interaction
  • C. To retrieve user input and provide real-time output only
  • D. To store various types of data and provide algorithms for summarizing past interactions

Answer: D

Explanation:
Comprehensive and Detailed In-Depth Explanation=
In LangChain, memory stores contextual data (e.g., chat history) and provides mechanisms to summarize or recall past interactions, enabling coherent, context-aware conversations. This makes Option B correct. Option A is too limited, as memory does more than just input/output handling. Option C is unrelated, as memory focuses on interaction context, not abstract calculations. Option D is inaccurate, as memory is dynamic, not a static database. Memory is crucial for stateful applications.
OCI 2025 Generative AI documentation likely discusses memory under LangChain's context management features.


NEW QUESTION # 88
Which is a cost-related benefit of using vector databases with Large Language Models (LLMs)?

  • A. They require frequent manual updates, which increase operational costs.
  • B. They are more expensive but provide higher quality data.
  • C. They increase the cost due to the need for real-time updates.
  • D. They offer real-time updated knowledge bases and are cheaper than fine-tuned LLMs.

Answer: D

Explanation:
Comprehensive and Detailed In-Depth Explanation=
Vector databases enable real-time knowledge retrieval for LLMs (e.g., in RAG), avoiding the high computational and data costs of fine-tuning an LLM for every update. They store embeddings efficiently, making them a cost-effective alternative to retraining, thus Option B is correct. Option A is false-updates are automated, not manual. Option C misrepresents-real-time capability reduces, not increases, costs compared to fine-tuning. Option D is incorrect-vector databases aren't inherently more expensive; they optimize cost and performance. This makes them economical for dynamic applications.
OCI 2025 Generative AI documentation likely highlights vector database cost benefits under RAG or data management sections.


NEW QUESTION # 89
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