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Oracle 1Z0-1127-25 Exam Syllabus Topics:

Topic
Details

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

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

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

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.

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Oracle Cloud Infrastructure 2025 Generative AI Professional Sample Questions (Q56-Q61):

NEW QUESTION # 56
Accuracy in vector databases contributes to the effectiveness of Large Language Models (LLMs) by preserving a specific type of relationship. What is the nature of these relationships, and why arethey crucial for language models?

A. Hierarchical relationships; important for structuring database queries
B. Semantic relationships; crucial for understanding context and generating precise language
C. Temporal relationships; necessary for predicting future linguistic trends
D. Linear relationships; they simplify the modeling process

Answer: B

Explanation:
Comprehensive and Detailed In-Depth Explanation=
Vector databases store embeddings that preserve semantic relationships (e.g., similarity between "dog" and "puppy") via their positions in high-dimensional space. This accuracy enables LLMs to retrieve contextually relevant data, improving understanding and generation, making Option B correct. Option A (linear) is too vague and unrelated. Option C (hierarchical) applies more to relational databases. Option D (temporal) isn't the focus-semantics drives LLM performance. Semantic accuracy is vital for meaningful outputs.
OCI 2025 Generative AI documentation likely discusses vector database accuracy under embeddings and RAG.

NEW QUESTION # 57
What issue might arise from using small datasets with the Vanilla fine-tuning method in the OCI Generative AI service?

A. Overfitting
B. Model Drift
C. Underfitting
D. Data Leakage

Answer: A

Explanation:
Comprehensive and Detailed In-Depth Explanation=
Vanilla fine-tuning updates all model parameters, and with small datasets, it can overfit-memorizing the data rather than generalizing-leading to poor performance on unseen data. Option A is correct. Option B (underfitting) is unlikely with full updates-overfitting is the risk. Option C (data leakage) depends on data handling, not size. Option D (model drift) relates to deployment shifts, not training. Small datasets exacerbate overfitting in Vanilla fine-tuning.
OCI 2025 Generative AI documentation likely warns of overfitting under Vanilla fine-tuning limitations.

NEW QUESTION # 58
What is the main advantage of using few-shot model prompting to customize a Large Language Model (LLM)?

A. It provides examples in the prompt to guide the LLM to better performance with no training cost.
B. It eliminates the need for any training or computational resources.
C. It significantly reduces the latency for each model request.
D. It allows the LLM to access a larger dataset.

Answer: A

Explanation:
Comprehensive and Detailed In-Depth Explanation=
Few-shot prompting involves providing a few examples in the prompt to guide the LLM's behavior, leveraging its in-context learning ability without requiring retraining or additional computational resources. This makes Option C correct. Option A is false, as few-shot prompting doesn't expand the dataset. Option B overstates the case, as inference still requires resources. Option D is incorrect, as latency isn't significantly affected by few-shot prompting.
OCI 2025 Generative AI documentation likely highlights few-shot prompting in sections on efficient customization.

NEW QUESTION # 59
Which statement accurately reflects the differences between these approaches in terms of the number of parameters modified and the type of data used?

A. Fine-tuning and continuous pretraining both modify all parameters and use labeled, task-specific data.
B. Parameter Efficient Fine-Tuning and Soft Prompting modify all parameters of the model using unlabeled data.
C. Soft Prompting and continuous pretraining are both methods that require no modification to the original parameters of the model.
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 # 60
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 # 61
......

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Matt White Matt White

Biography

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