ORACLE 1Z0-1127-25 PDF FORMAT FOR EASY ACCESS

Oracle 1Z0-1127-25 PDF Format for Easy Access

Oracle 1Z0-1127-25 PDF Format for Easy Access

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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
  • 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 3
  • 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 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 (Q38-Q43):

NEW QUESTION # 38
When should you use the T-Few fine-tuning method for training a model?

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

Answer: A

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 # 39
Which statement is true about string prompt templates and their capability regarding variables?

  • A. They can only support a single variable at a time.
  • B. They require a minimum of two variables to function properly.
  • C. They are unable to use any variables.
  • D. They support any number of variables, including the possibility of having none.

Answer: D

Explanation:
Comprehensive and Detailed In-Depth Explanation=
String prompt templates (e.g., in LangChain) are flexible frameworks that can include zero, one, or multiple variables (placeholders) to customize prompts dynamically. They can be static (no variables) or complex (many variables), making Option C correct. Option A is too restrictive. Option B is false-variables are a core feature. Option D is incorrect, as no minimum is required. This flexibility aids prompt engineering.
OCI 2025 Generative AI documentation likely covers prompt templates under LangChain or prompt design.


NEW QUESTION # 40
What is the purpose of memory in the LangChain framework?

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

Answer: A

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 # 41
Which is a characteristic of T-Few fine-tuning for Large Language Models (LLMs)?

  • A. It increases the training time as compared to Vanilla fine-tuning.
  • B. It does not update any weights but restructures the model architecture.
  • C. It updates all the weights of the model uniformly.
  • D. It selectively updates only a fraction of the model's weights.

Answer: D

Explanation:
Comprehensive and Detailed In-Depth Explanation=
T-Few fine-tuning, a Parameter-Efficient Fine-Tuning (PEFT) method, updates only a small fraction of an LLM's weights, reducing computational cost and overfitting risk compared to Vanilla fine-tuning (all weights). This makes Option C correct. Option A describes Vanilla fine-tuning. Option B is false-T-Few updates weights, not architecture. Option D is incorrect-T-Few typically reduces training time. T-Few optimizes efficiency.
OCI 2025 Generative AI documentation likely highlights T-Few under fine-tuning options.


NEW QUESTION # 42
How does the utilization of T-Few transformer layers contribute to the efficiency of the fine-tuning process?

  • A. By restricting updates to only a specific group of transformer layers
  • B. By excluding transformer layers from the fine-tuning process entirely
  • C. By incorporating additional layers to the base model
  • D. By allowing updates across all layers of the model

Answer: A

Explanation:
Comprehensive and Detailed In-Depth Explanation=
T-Few fine-tuning enhances efficiency by updating only a small subset of transformer layers or parameters (e.g., via adapters), reducing computational load-Option D is correct. Option A (adding layers) increases complexity, not efficiency. Option B (all layers) describes Vanilla fine-tuning. Option C (excluding layers) is false-T-Few updates, not excludes. This selective approach optimizes resource use.
OCI 2025 Generative AI documentation likely details T-Few under PEFT methods.


NEW QUESTION # 43
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