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Oracle 1Z0-1127-25 exam practice questions and answers

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Oracle 1Z0-1127-25 Exam Syllabus Topics:

Topic
Details

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 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 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 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 (Q67-Q72):

NEW QUESTION # 67
What does "Loss" measure in the evaluation of OCI Generative AI fine-tuned models?

A. The difference between the accuracy of the model at the beginning of training and the accuracy of the deployed model
B. The improvement in accuracy achieved by the model during training on the user-uploaded dataset
C. The level of incorrectness in the model's predictions, with lower values indicating better performance
D. The percentage of incorrect predictions made by the model compared with the total number of predictions in the evaluation

Answer: C

Explanation:
Comprehensive and Detailed In-Depth Explanation=
Loss measures the discrepancy between a model's predictions and true values, with lower values indicating better fit-Option D is correct. Option A (accuracy difference) isn't loss-it's a derived metric. Option B (error percentage) is closer to error rate, not loss. Option C (accuracy improvement) is a training outcome, not loss's definition. Loss is a fundamental training signal.
OCI 2025 Generative AI documentation likely defines loss under fine-tuning metrics.

NEW QUESTION # 68
Why is it challenging to apply diffusion models to text generation?

A. Because text generation does not require complex models
B. Because diffusion models can only produce images
C. Because text is not categorical
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 # 69
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 diffusion model that specializes in producing complex outputs.
B. A language model that operates on a token-by-token output basis
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: A

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 # 70
Which statement accurately reflects the differences between these approaches in terms of the number of parameters modified and the type of data used?

A. Soft Prompting and continuous pretraining are both methods that require no modification to the original parameters of the model.
B. Parameter Efficient Fine-Tuning and Soft Prompting modify all parameters of the model using unlabeled data.
C. Fine-tuning and continuous pretraining both modify all parameters and use labeled, task-specific data.
D. Fine-tuning modifies all parameters using labeled, task-specific data, whereas Parameter Efficient Fine-Tuning updates a few, new parameters also with labeled, task-specific data.

Answer: D

Explanation:
Comprehensive and Detailed In-Depth Explanation=
Fine-tuning typically involves updating all parameters of an LLM using labeled, task-specific data to adapt it to a specific task, which is computationally expensive. Parameter Efficient Fine-Tuning (PEFT), such as methods like LoRA (Low-Rank Adaptation), updates only a small subset of parameters (often newly added ones) while still using labeled, task-specific data, making it more efficient. Option C correctly captures this distinction. Option A is wrong because continuous pretraining uses unlabeled data and isn't task-specific. Option B is incorrect as PEFT and Soft Prompting don't modify all parameters, and Soft Prompting typically uses labeled examples indirectly. Option D is inaccurate because continuous pretraining modifies parameters, while SoftPrompting doesn't.
OCI 2025 Generative AI documentation likely discusses Fine-tuning and PEFT under model customization techniques.

NEW QUESTION # 71
What is the role of temperature in the decoding process of a Large Language Model (LLM)?

A. To increase the accuracy of the most likely word in the vocabulary
B. To adjust the sharpness of probability distribution over vocabulary when selecting the next word
C. To decide to which part of speech the next word should belong
D. To determine the number of words to generate in a single decoding step

Answer: B

Explanation:
Comprehensive and Detailed In-Depth Explanation=
Temperature is a hyperparameter in the decoding process of LLMs that controls the randomness of word selection by modifying the probability distribution over the vocabulary. A lower temperature (e.g., 0.1) sharpens the distribution, making the model more likely to select the highest-probability words, resulting in more deterministic and focused outputs. A higher temperature (e.g., 2.0) flattens the distribution, increasing the likelihood of selecting less probable words, thus introducing more randomness and creativity. Option D accurately describes this role. Option A is incorrect because temperature doesn't directly increase accuracy but influences output diversity. Option B is unrelated, as temperature doesn't dictate the number of words generated. Option C is also incorrect, as part-of-speech decisions are not directly tied to temperature but to the model's learned patterns.
General LLM decoding principles, likely covered in OCI 2025 Generative AI documentation under decoding parameters like temperature.

NEW QUESTION # 72
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