NEW 1Z0-1127-25 STUDY PLAN PROFESSIONAL QUESTIONS POOL ONLY AT REALEXAMFREE

New 1Z0-1127-25 Study Plan Professional Questions Pool Only at RealExamFree

New 1Z0-1127-25 Study Plan Professional Questions Pool Only at RealExamFree

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Oracle 1Z0-1127-25 Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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 2
  • 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 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.

Oracle Cloud Infrastructure 2025 Generative AI Professional Sample Questions (Q36-Q41):

NEW QUESTION # 36
How are documents usually evaluated in the simplest form of keyword-based search?

  • A. By the complexity of language used in the documents
  • B. Based on the number of images and videos contained in the documents
  • C. According to the length of the documents
  • D. Based on the presence and frequency of the user-provided keywords

Answer: D

Explanation:
Comprehensive and Detailed In-Depth Explanation=
In basic keyword-based search, documents are evaluated by matching user-provided keywords, with relevance often determined by their presence and frequency (e.g., term frequency in TF-IDF). This makes Option C correct. Option A (language complexity) is unrelated to simple keyword search. Option B (multimedia) isn't considered in text-based keyword methods. Option D (length) may influence scoring indirectly but isn't the primary metric. Keyword search prioritizes exact matches.
OCI 2025 Generative AI documentation likely contrasts keyword search with semantic search under retrieval methods.


NEW QUESTION # 37
Given the following prompts used with a Large Language Model, classify each as employing the Chain-of-Thought, Least-to-Most, or Step-Back prompting technique:

  • A. "Solve a complex math problem by first identifying the formula needed, and then solve a simpler version of the problem before tackling the full question."
  • B. "Calculate the total number of wheels needed for 3 cars. Cars have 4 wheels each. Then, use the total number of wheels to determine how many sets of wheels we can buy with $200 if one set (4 wheels) costs $50."
  • C. "To understand the impact of greenhouse gases on climate change, let's start by defining what greenhouse gases are. Next, we'll explore how they trap heat in the Earth's atmosphere."A. 1: Step-Back, 2: Chain-of-Thought, 3: Least-to-MostB. 1: Least-to-Most, 2: Chain-of-Thought, 3: Step-BackC. 1: Chain-of-Thought, 2: Step-Back, 3: Least-to-MostD. 1: Chain-of-Thought, 2: Least-to-Most, 3: Step-Back

Answer: C

Explanation:
Comprehensive and Detailed In-Depth Explanation=
Prompt 1: Shows intermediate steps (3 × 4 = 12, then 12 ÷ 4 = 3 sets, $200 ÷ $50 = 4)-Chain-of-Thought.
Prompt 2: Steps back to a simpler problem before the full one-Step-Back.
Prompt 3: OCI 2025 Generative AI documentation likely defines these under prompting strategies.


NEW QUESTION # 38
What is LCEL in the context of LangChain Chains?

  • A. A declarative way to compose chains together using LangChain Expression Language
  • B. A programming language used to write documentation for LangChain
  • C. An older Python library for building Large Language Models
  • D. A legacy method for creating chains in LangChain

Answer: A

Explanation:
Comprehensive and Detailed In-Depth Explanation=
LCEL (LangChain Expression Language) is a declarative syntax in LangChain for composing chains-sequences of operations involving LLMs, tools, and memory. It simplifies chain creation with a readable, modular approach, making Option C correct. Option A is false, as LCEL isn't fordocumentation. Option B is incorrect, as LCEL is current, not legacy. Option D is wrong, as LCEL is part of LangChain, not a standalone LLM library. LCEL enhances flexibility in application design.
OCI 2025 Generative AI documentation likely mentions LCEL under LangChain integration or chain composition.


NEW QUESTION # 39
When should you use the T-Few fine-tuning method for training a model?

  • A. For complicated semantic understanding improvement
  • B. For models that require their own hosting dedicated AI cluster
  • C. For datasets with hundreds of thousands to millions of samples
  • D. For datasets with a few thousand samples or less

Answer: D

Explanation:
Comprehensive and Detailed In-Depth Explanation=
T-Few is ideal for smaller datasets (e.g., a few thousand samples) where full fine-tuning risks overfitting and is computationally wasteful-Option C is correct. Option A (semantic understanding) is too vague-dataset size matters more. Option B (dedicated cluster) isn't a condition for T-Few. Option D (large datasets) favors Vanilla fine-tuning. T-Few excels in low-data scenarios.
OCI 2025 Generative AI documentation likely specifies T-Few use cases under fine-tuning guidelines.


NEW QUESTION # 40
Which role does a "model endpoint" serve in the inference workflow of the OCI Generative AI service?

  • A. Updates the weights of the base model during the fine-tuning process
  • B. Evaluates the performance metrics of the custom models
  • C. Hosts the training data for fine-tuning custom models
  • D. Serves as a designated point for user requests and model responses

Answer: D

Explanation:
Comprehensive and Detailed In-Depth Explanation=
A "model endpoint" in OCI's inference workflow is an API or interface where users send requests and receive responses from a deployed model-Option B is correct. Option A (weight updates) occurs during fine-tuning, not inference. Option C (metrics) is for evaluation, not endpoints. Option D (training data) relates to storage, not inference. Endpoints enable real-time interaction.
OCI 2025 Generative AI documentation likely describes endpoints under inference deployment.


NEW QUESTION # 41
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