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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

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 (Q54-Q59):

NEW QUESTION # 54
In which scenario is soft prompting appropriate compared to other training styles?

A. When the model needs to be adapted to perform well in a domain on which it was not originally trained
B. When there is a significant amount of labeled, task-specific data available
C. When there is a need to add learnable parameters to a Large Language Model (LLM) without task-specific training
D. When the model requires continued pretraining on unlabeled data

Answer: C

Explanation:
Comprehensive and Detailed In-Depth Explanation=
Soft prompting adds trainable parameters (soft prompts) to adapt an LLM without retraining its core weights, ideal for low-resource customization without task-specific data. This makes Option C correct. Option A suits fine-tuning. Option B may require more than soft prompting (e.g., domain fine-tuning). Option D describes pretraining, not soft prompting. Soft prompting is efficient for specific adaptations.
OCI 2025 Generative AI documentation likely discusses soft prompting under PEFT methods.

NEW QUESTION # 55
Which statement is true about string prompt templates and their capability regarding variables?

A. They are unable to use any variables.
B. They require a minimum of two variables to function properly.
C. They can only support a single variable at a time.
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 # 56
What does the RAG Sequence model do in the context of generating a response?

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

Answer: B

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 # 57
Which statement best describes the role of encoder and decoder models in natural language processing?

A. Encoder models and decoder models both convert sequences of words into vector representations without generating new text.
B. Encoder models take a sequence of words and predict the next word in the sequence, whereas decoder models convert a sequence of words into a numerical representation.
C. Encoder models convert a sequence of words into a vector representation, and decoder models take this vector representation to generate a sequence of words.
D. Encoder models are used only for numerical calculations, whereas decoder models are used to interpret the calculated numerical values back into text.

Answer: C

Explanation:
Comprehensive and Detailed In-Depth Explanation=
In NLP (e.g., transformers), encoders convert input text into a vector representation (encoding meaning), while decoders generate text from such vectors (e.g., in translation or generation). This makes Option C correct. Option A is false-decoders generate text. Option B reverses roles-encoders don't predict next words, and decoders don't encode. Option D oversimplifies-encoders handle text, not just numbers. This is the foundation of seq2seq models.
OCI 2025 Generative AI documentation likely explains encoder-decoder roles under model architecture.

NEW QUESTION # 58
How does the structure of vector databases differ from traditional relational databases?

A. It is based on distances and similarities in a vector space.
B. It is not optimized for high-dimensional spaces.
C. A vector database stores data in a linear or tabular format.
D. It uses simple row-based data storage.

Answer: A

Explanation:
Comprehensive and Detailed In-Depth Explanation=
Vector databases store data as high-dimensional vectors, optimized for similarity searches (e.g., cosine distance), unlike relational databases' tabular, row-column structure. This makes Option C correct. Option A and D describe relational databases. Option B is false-vector databases excel in high-dimensional spaces. Vector databases support semantic queries critical for LLMs.
OCI 2025 Generative AI documentation likely contrasts these under data storage options.

NEW QUESTION # 59
......

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