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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 Updated Torrent - 1Z0-1127-25 Study Questions & 1Z0-1127-25 Updated Material

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Oracle Cloud Infrastructure 2025 Generative AI Professional Sample Questions (Q16-Q21):

NEW QUESTION # 16
How can the concept of "Groundedness" differ from "Answer Relevance" in the context of Retrieval Augmented Generation (RAG)?

A. Groundedness pertains to factual correctness, whereas Answer Relevance concerns query relevance.
B. Groundedness measures relevance to the user query, whereas Answer Relevance evaluates data integrity.
C. Groundedness focuses on data integrity, whereas Answer Relevance emphasizes lexical diversity.
D. Groundedness refers to contextual alignment, whereas Answer Relevance deals with syntactic accuracy.

Answer: A

Explanation:
Comprehensive and Detailed In-Depth Explanation=
In RAG, "Groundedness" assesses whether the response is factually correct and supported by retrieved data, while "Answer Relevance" evaluates how well the response addresses the user's query. Option A captures this distinction accurately. Option B is off-groundedness isn't just contextual alignment, and relevance isn't about syntax. Option C swaps the definitions. Option D misaligns-groundedness isn't solely data integrity, and relevance isn't lexical diversity. This distinction ensures RAG outputs are both true and pertinent.
OCI 2025 Generative AI documentation likely defines these under RAG evaluation metrics.

NEW QUESTION # 17
What does accuracy measure in the context of fine-tuning results for a generative model?

A. How many predictions the model made correctly out of all the predictions in an evaluation
B. The depth of the neural network layers used in the model
C. The proportion of incorrect predictions made by the model during an evaluation
D. The number of predictions a model makes, regardless of whether they are correct or incorrect

Answer: A

Explanation:
Comprehensive and Detailed In-Depth Explanation=
Accuracy in fine-tuning measures the proportion of correct predictions (e.g., matching expected outputs) out of all predictions made during evaluation, reflecting model performance-Option C is correct. Option A (total predictions) ignores correctness. Option B (incorrect proportion) is the inverse-error rate. Option D (layer depth) is unrelated to accuracy. Accuracy is a standard metric for generative tasks.OCI 2025 Generative AI documentation likely defines accuracy under fine-tuning evaluation metrics.

NEW QUESTION # 18
When does a chain typically interact with memory in a run within the LangChain framework?

A. Continuously throughout the entire chain execution process.
B. Before user input and after chain execution.
C. After user input but before chain execution, and again after core logic but before output.
D. Only after the output has been generated.

Answer: C

Explanation:
Comprehensive and Detailed In-Depth Explanation=
In LangChain, a chain interacts with memory after receiving user input (to load prior context) but before execution (to inform the process), and again after the core logic (to update memory with new context) but before the final output. This ensures context continuity, making Option C correct. Option A is too late, missing pre-execution context. Option B is misordered. Option D overstates interaction, as it's not continuous but at specific points. Memory integration is key for stateful chains.
OCI 2025 Generative AI documentation likely details memory interaction under LangChain workflows.

NEW QUESTION # 19
How does a presence penalty function in language model generation?

A. It penalizes a token each time it appears after the first occurrence.
B. It penalizes only tokens that have never appeared in the text before.
C. It penalizes all tokens equally, regardless of how often they have appeared.
D. It applies a penalty only if the token has appeared more than twice.

Answer: A

Explanation:
Comprehensive and Detailed In-Depth Explanation=
A presence penalty reduces the probability of tokens that have already appeared in the output, applying the penalty each time they reoccur after their first use, to discourage repetition. This makes Option D correct. Option A (equal penalties) ignores prior appearance. Option B is the opposite-penalizing unused tokens isn't the intent. Option C (more than twice) adds an arbitrary threshold not typically used. Presence penalty enhances output variety.OCI 2025 Generative AI documentation likely details presence penalty under generation control parameters.

NEW QUESTION # 20
Given the following code block:
history = StreamlitChatMessageHistory(key="chat_messages")
memory = ConversationBufferMemory(chat_memory=history)
Which statement is NOT true about StreamlitChatMessageHistory?

A. StreamlitChatMessageHistory can be used in any type of LLM application.
B. A given StreamlitChatMessageHistory will NOT be persisted.
C. StreamlitChatMessageHistory will store messages in Streamlit session state at the specified key.
D. A given StreamlitChatMessageHistory will not be shared across user sessions.

Answer: A

Explanation:
Comprehensive and Detailed In-Depth Explanation=
StreamlitChatMessageHistory integrates with Streamlit's session state to store chat history, tied to a specific key (Option A, true). It's not persisted beyond the session (Option B, true) and isn't shared across users (Option C, true), as Streamlit sessions are user-specific. However, it's designed specifically for Streamlit apps, not universally for any LLM application (e.g., non-Streamlit contexts), making Option D NOT true.
OCI 2025 Generative AI documentation likely references Streamlit integration under LangChain memory options.

NEW QUESTION # 21
......

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