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Oracle 1Z0-1127-25 Exam Syllabus Topics:

Topic
Details

Topic 1

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 2

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 3

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 4

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.

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Oracle Cloud Infrastructure 2025 Generative AI Professional Sample Questions (Q22-Q27):

NEW QUESTION # 22
Given the following code:
chain = prompt | llm
Which statement is true about LangChain Expression Language (LCEL)?

A. LCEL is an older Python library for building Large Language Models.
B. LCEL is a legacy method for creating chains in LangChain.
C. LCEL is a declarative and preferred way to compose chains together.
D. LCEL is a programming language used to write documentation for LangChain.

Answer: C

Explanation:
Comprehensive and Detailed In-Depth Explanation=
LangChain Expression Language (LCEL) is a declarative syntax (e.g., using | to pipe components) for composing chains in LangChain, combining prompts, LLMs, and other elements efficiently-Option C is correct. Option A is false-LCEL isn't for documentation. Option B is incorrect-it's current, not legacy; traditional Python classes are older. Option D is wrong-LCEL is part of LangChain, not a standalone LLM library. LCEL simplifies chain design.
OCI 2025 Generative AI documentation likely highlights LCEL under LangChain chaincomposition.

NEW QUESTION # 23
Which statement best describes the role of encoder and decoder models in natural language processing?

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

Answer: B

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 # 24
What is the purpose of memory in the LangChain framework?

A. To store various types of data and provide algorithms for summarizing past interactions
B. To retrieve user input and provide real-time output only
C. To perform complex calculations unrelated to user interaction
D. To act as a static database for storing permanent records

Answer: A

Explanation:
Comprehensive and Detailed In-Depth Explanation=
In LangChain, memory stores contextual data (e.g., chat history) and provides mechanisms to summarize or recall past interactions, enabling coherent, context-aware conversations. This makes Option B correct. Option A is too limited, as memory does more than just input/output handling. Option C is unrelated, as memory focuses on interaction context, not abstract calculations. Option D is inaccurate, as memory is dynamic, not a static database. Memory is crucial for stateful applications.
OCI 2025 Generative AI documentation likely discusses memory under LangChain's context management features.

NEW QUESTION # 25
An AI development company is working on an AI-assisted chatbot for a customer, which happens to be an online retail company. The goal is to create an assistant that can best answer queries regarding the company policies as well as retain the chat history throughout a session. Considering the capabilities, which type of model would be the best?

A. An LLM enhanced with Retrieval-Augmented Generation (RAG) for dynamic information retrieval and response generation.
B. A keyword search-based AI that responds based on specific keywords identified in customer queries.
C. An LLM dedicated to generating text responses without external data integration.
D. A pre-trained LLM model from Cohere or OpenAI.

Answer: A

Explanation:
Comprehensive and Detailed In-Depth Explanation=
For a chatbot needing to answer policy queries (requiring up-to-date, specific data) and retain chat history (context awareness), an LLM with RAG is ideal. RAG integrates external data (e.g., policy documents) via retrieval and supports memory for session-long context, making Option B correct. Option A (keyword search) lacks reasoning and context retention. Option C (standalone LLM) can't dynamically fetch policy data. Option D (pre-trained LLM) is too vague and lacks RAG's capabilities. RAG meets both requirements effectively.
OCI 2025 Generative AI documentation likely highlights RAG for dynamic, context-aware applications.

NEW QUESTION # 26
What does the Ranker do in a text generation system?

A. It generates the final text based on the user's query.
B. It sources information from databases to use in text generation.
C. It interacts with the user to understand the query better.
D. It evaluates and prioritizes the information retrieved by the Retriever.

Answer: D

Explanation:
Comprehensive and Detailed In-Depth Explanation=
In systems like RAG, the Ranker evaluates and sorts the information retrieved by the Retriever (e.g., documents or snippets) based on relevance to the query, ensuring the most pertinent data is passed to the Generator. This makes Option C correct. Option A is the Generator's role. Option B describes the Retriever. Option D is unrelated, as the Ranker doesn't interact with users but processes retrieved data. The Ranker enhances output quality by prioritizing relevant content.
OCI 2025 Generative AI documentation likely details the Ranker under RAG pipeline components.

NEW QUESTION # 27
......

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