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[General] 1Z0-1127-25 Valid Test Tips, 1Z0-1127-25 Free Test Questions

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【General】 1Z0-1127-25 Valid Test Tips, 1Z0-1127-25 Free Test Questions

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Oracle 1Z0-1127-25 Exam Syllabus Topics:
TopicDetails
Topic 1
  • 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 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 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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Oracle Cloud Infrastructure 2025 Generative AI Professional Sample Questions (Q69-Q74):NEW QUESTION # 69
Given the following code:
PromptTemplate(input_variables=["human_input", "city"], template=template) Which statement is true about PromptTemplate in relation to input_variables?
  • A. PromptTemplate supports any number of variables, including the possibility of having none.
  • B. PromptTemplate can support only a single variable at a time.
  • C. PromptTemplate requires a minimum of two variables to function properly.
  • D. PromptTemplate is unable to use any variables.
Answer: A
Explanation:
Comprehensive and Detailed In-Depth Explanation=
In LangChain, PromptTemplate supports any number of input_variables (zero, one, or more), allowing flexible prompt design-Option C is correct. The example shows two, but it's not a requirement. Option A (minimum two) is false-no such limit exists. Option B (single variable) is too restrictive. Option D (no variables) contradicts its purpose-variables are optional but supported. This adaptability aids prompt engineering.
OCI 2025 Generative AI documentation likely covers PromptTemplate under LangChain prompt design.

NEW QUESTION # 70
Which is a characteristic of T-Few fine-tuning for Large Language Models (LLMs)?
  • A. It selectively updates only a fraction of the model's weights.
  • B. It does not update any weights but restructures the model architecture.
  • C. It updates all the weights of the model uniformly.
  • D. It increases the training time as compared to Vanilla fine-tuning.
Answer: A
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 # 71
What do prompt templates use for templating in language model applications?
  • A. Python's str.format syntax
  • B. Python's list comprehension syntax
  • C. Python's class and object structures
  • D. Python's lambda functions
Answer: A
Explanation:
Comprehensive and Detailed In-Depth Explanation=
Prompt templates in LLM applications (e.g., LangChain) typically use Python's str.format() syntax to insert variables into predefined string patterns (e.g., "Hello, {name}!"). This makes Option B correct. Option A (list comprehension) is for list operations, not templating. Option C (lambda functions) defines functions, not templates. Option D (classes/objects) is overkill-templates are simpler constructs. str.format() ensures flexibility and readability.
OCI 2025 Generative AI documentation likely mentions str.format() under prompt template design.

NEW QUESTION # 72
Which is NOT a category of pretrained foundational models available in the OCI Generative AI service?
  • A. Embedding models
  • B. Translation models
  • C. Summarization models
  • D. Generation models
Answer: B
Explanation:
Comprehensive and Detailed In-Depth Explanation=
OCI Generative AI typically offers pretrained models for summarization (A), generation (B), and embeddings (D), aligning with common generative tasks. Translation models (C) are less emphasized in generative AI services, often handled by specialized NLP platforms, making C the NOT category. While possible, translation isn't a core OCI generative focus based on standard offerings.
OCI 2025 Generative AI documentation likely lists model categories under pretrained options.

NEW QUESTION # 73
An AI development company is working on an advanced AI assistant capable of handling queries in a seamless manner. Their goal is to create an assistant that can analyze images provided by users and generate descriptive text, as well as take text descriptions and produce accurate visual representations. Considering the capabilities, which type of model would the company likely focus on integrating into their AI assistant?
  • A. A Large Language Model-based agent that focuses on generating textual responses
  • B. A Retrieval Augmented Generation (RAG) model that uses text as input and output
  • C. A language model that operates on a token-by-token output basis
  • D. A diffusion model that specializes in producing complex outputs.
Answer: D
Explanation:
Comprehensive and Detailed In-Depth Explanation=
The task requires bidirectional text-image capabilities: analyzing images to generate text and generating images from text. Diffusion models (e.g., Stable Diffusion) excel at complex generative tasks, including text-to-image and image-to-text with appropriate extensions, making Option A correct. Option B (LLM) is text-only. Option C (token-based LLM) lacks image handling. Option D (RAG) focuses on text retrieval, not image generation. Diffusion models meet both needs.
OCI 2025 Generative AI documentation likely discusses diffusion models under multimodal applications.

NEW QUESTION # 74
......
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