2025 TEST 1Z0-1127-25 DUMPS DEMO - LATEST ORACLE ORACLE CLOUD INFRASTRUCTURE 2025 GENERATIVE AI PROFESSIONAL - 1Z0-1127-25 STUDY GUIDE PDF

2025 Test 1Z0-1127-25 Dumps Demo - Latest Oracle Oracle Cloud Infrastructure 2025 Generative AI Professional - 1Z0-1127-25 Study Guide Pdf

2025 Test 1Z0-1127-25 Dumps Demo - Latest Oracle Oracle Cloud Infrastructure 2025 Generative AI Professional - 1Z0-1127-25 Study Guide Pdf

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Oracle Cloud Infrastructure 2025 Generative AI Professional Sample Questions (Q76-Q81):

NEW QUESTION # 76
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 language model that operates on a token-by-token output basis
  • B. A diffusion model that specializes in producing complex outputs.
  • C. A Retrieval Augmented Generation (RAG) model that uses text as input and output
  • D. A Large Language Model-based agent that focuses on generating textual responses

Answer: B

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 # 77
What does the RAG Sequence model do in the context of generating a response?

  • A. It retrieves a single relevant document for the entire input query and generates a response based on that alone.
  • B. It retrieves relevant documents only for the initial part of the query and ignores the rest.
  • C. For each input query, it retrieves a set of relevant documents and considers them together to generate a cohesive response.
  • D. It modifies the input query before retrieving relevant documents to ensure a diverse response.

Answer: C

Explanation:
Comprehensive and Detailed In-Depth Explanation=
The RAG (Retrieval-Augmented Generation) Sequence model retrieves a set of relevant documents for a query from an external knowledge base (e.g., via a vector database) and uses them collectively with the LLM to generate a cohesive, informed response. This leverages multiple sources for better context, making Option B correct. Option A describes a simpler approach (e.g., RAG Token), not Sequence. Option C is incorrect-RAG considers the full query. Option D is false-query modification isn't standard in RAG Sequence. This method enhances response quality with diverse inputs.
OCI 2025 Generative AI documentation likely details RAG Sequence under retrieval-augmented techniques.


NEW QUESTION # 78
How are fine-tuned customer models stored to enable strong data privacy and security in the OCI Generative AI service?

  • A. Stored in Object Storage encrypted by default
  • B. Shared among multiple customers for efficiency
  • C. Stored in an unencrypted form in Object Storage
  • D. Stored in Key Management service

Answer: A

Explanation:
Comprehensive and Detailed In-Depth Explanation=
In OCI, fine-tuned models are stored in Object Storage, encrypted by default, ensuring privacy and security per cloud best practices-Option B is correct. Option A (shared) violates privacy. Option C (unencrypted) contradicts security standards. Option D (Key Management) stores keys, not models. Encryption protects customer data.
OCI 2025 Generative AI documentation likely details storage security under fine-tuning workflows.


NEW QUESTION # 79
Why is it challenging to apply diffusion models to text generation?

  • A. Because text is not categorical
  • B. Because diffusion models can only produce images
  • C. Because text generation does not require complex models
  • D. Because text representation is categorical unlike images

Answer: D

Explanation:
Comprehensive and Detailed In-Depth Explanation=
Diffusion models, widely used for image generation, iteratively denoise data from noise to a structured output. Images are continuous (pixel values), while text is categorical (discrete tokens), making it challenging to apply diffusion directly to text, as the denoising process struggles with discrete spaces. This makes Option C correct. Option A is false-text generation can benefit from complex models. Option B is incorrect-text is categorical. Option D is wrong, as diffusion models aren't inherently image-only but are better suited to continuous data. Research adapts diffusion for text, but it's less straightforward.
OCI 2025 Generative AI documentation likely discusses diffusion models under generative techniques, noting their image focus.


NEW QUESTION # 80
What do embeddings in Large Language Models (LLMs) represent?

  • A. The color and size of the font in textual data
  • B. The semantic content of data in high-dimensional vectors
  • C. The grammatical structure of sentences in the data
  • D. The frequency of each word or pixel in the data

Answer: B

Explanation:
Comprehensive and Detailed In-Depth Explanation=
Embeddings in LLMs are high-dimensional vectors that encode the semantic meaning of words, phrases, or sentences, capturing relationships like similarity or context (e.g., "cat" and "kitten" being close in vector space). This allows the model to process and understand text numerically, making Option C correct. Option A is irrelevant, as embeddings don't deal with visual attributes. Option B is incorrect, as frequency is a statistical measure, not the purpose of embeddings. Option D is partially related but too narrow-embeddings capture semantics beyond just grammar.
OCI 2025 Generative AI documentation likely discusses embeddings under data representation or vectorization topics.


NEW QUESTION # 81
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