TESTKING 1Z0-1127-25 EXAM QUESTIONS | EXAM 1Z0-1127-25 REVISION PLAN

Testking 1Z0-1127-25 Exam Questions | Exam 1Z0-1127-25 Revision Plan

Testking 1Z0-1127-25 Exam Questions | Exam 1Z0-1127-25 Revision Plan

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Tags: Testking 1Z0-1127-25 Exam Questions, Exam 1Z0-1127-25 Revision Plan, Test 1Z0-1127-25 Sample Online, Valid Dumps 1Z0-1127-25 Pdf, Exam 1Z0-1127-25 Flashcards

The Oracle 1Z0-1127-25 exam questions are being offered in three different formats. These formats are Oracle 1Z0-1127-25 PDF dumps files, desktop practice test software, and web-based practice test software. All these three Oracle 1Z0-1127-25 Exam Dumps formats contain the real Oracle Cloud Infrastructure 2025 Generative AI Professional (1Z0-1127-25) exam questions that assist you in your 1Z0-1127-25 practice exam preparation and finally, you will be confident to pass the final 1Z0-1127-25 exam easily.

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
  • 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 4
  • 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.

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

NEW QUESTION # 84
When is fine-tuning an appropriate method for customizing a Large Language Model (LLM)?

  • A. When the LLM requires access to the latest data for generating outputs
  • B. When you want to optimize the model without any instructions
  • C. When the LLM already understands the topics necessary for text generation
  • D. When the LLM does not perform well on a task and the data for prompt engineering is too large

Answer: D

Explanation:
Comprehensive and Detailed In-Depth Explanation=
Fine-tuning is suitable when an LLM underperforms on a specific task and prompt engineering alone isn't feasible due to large, task-specific data that can't be efficiently included in prompts. This adjusts the model's weights, making Option B correct. Option A suggests no customization is needed. Option C favors RAG for latest data, not fine-tuning. Option D is vague-fine-tuning requires data and goals, not just optimization without direction. Fine-tuning excels with substantial task-specific data.
OCI 2025 Generative AI documentation likely outlines fine-tuning use cases under customization strategies.


NEW QUESTION # 85
What is LangChain?

  • A. A JavaScript library for natural language processing
  • B. A Java library for text summarization
  • C. A Python library for building applications with Large Language Models
  • D. A Ruby library for text generation

Answer: C

Explanation:
Comprehensive and Detailed In-Depth Explanation=
LangChain is a Python library designed to simplify building applications with LLMs by providing tools for chaining operations, managing memory, and integrating external data (e.g., via RAG). This makes Option B correct. Options A, C, and D are incorrect, as LangChain is neither JavaScript, Java, nor Ruby-based, nor limited to summarization or generation alone-it's broader in scope. It's widely used for LLM-powered apps.
OCI 2025 Generative AI documentation likely introduces LangChain under supported frameworks.


NEW QUESTION # 86
How does a presence penalty function in language model generation when using OCI Generative AI service?

  • A. It only penalizes tokens that have never appeared in the text before.
  • B. It penalizes a token each time it appears after the first occurrence.
  • 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: B

Explanation:
Comprehensive and Detailed In-Depth Explanation=
A presence penalty in LLMs (including OCI's service) reduces the probability of tokens that have already appeared in the output, applying the penalty each time they reoccur after their first use. This discourages repetition, making Option D correct. Option A is false, as penalties depend on prior appearance, not uniform application. Option B is the opposite-penalizing unused tokens isn't the goal. Option C is incorrect, as the penalty isn't threshold-based (e.g., more than twice) but applied per reoccurrence. This enhances output diversity.
OCI 2025 Generative AI documentation likely details presence penalty under generation parameters.


NEW QUESTION # 87
You create a fine-tuning dedicated AI cluster to customize a foundational model with your custom training dat a. How many unit hours are required for fine-tuning if the cluster is active for 10 hours?

  • A. 40 unit hours
  • B. 25 unit hours
  • C. 20 unit hours
  • D. 30 unit hours

Answer: C

Explanation:
Comprehensive and Detailed In-Depth Explanation=
In OCI, unit hours typically equal actual hours of cluster activity unless specified otherwise (e.g., per GPU scaling). For 10 hours of activity, it's 10 hours × 1 unit/hour = 10 unit hours, but options suggest a multiplier (common in cloud pricing). Assuming a standard 2-unit/hour rate (e.g., for GPU clusters), it's 10 × 2 = 20 unit hours-Option C fits best. Options A, B, and D imply inconsistent rates (2.5, 4, 3).
OCI 2025 Generative AI documentation likely specifies unit hour rates under DedicatedAI Cluster pricing.


NEW QUESTION # 88
What is the purpose of Retrievers in LangChain?

  • A. To break down complex tasks into smaller steps
  • B. To train Large Language Models
  • C. To retrieve relevant information from knowledge bases
  • D. To combine multiple components into a single pipeline

Answer: C

Explanation:
Comprehensive and Detailed In-Depth Explanation=
Retrievers in LangChain fetch relevant information (e.g., documents, embeddings) from external knowledge bases (like vector stores) to provide context for LLM responses, especially in RAG setups. This makes Option B correct. Option A (training) is unrelated-Retrievers operate at inference. Option C (task breakdown) pertains to prompting techniques, not retrieval. Option D (pipeline combination) describes chains, not Retrievers specifically. Retrievers enhance context awareness.
OCI 2025 Generative AI documentation likely defines Retrievers under LangChain components.


NEW QUESTION # 89
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