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Oracle 1z0-1127-24 Exam Syllabus Topics:

Topic	Details
Topic 1	Fundamentals of Large Language Models (LLMs): For AI developers and Cloud Architects, this topic discusses LLM architectures and LLM fine-tuning. Additionally, it focuses on prompts for LLMs and fundamentals of code models.
Topic 2	Using OCI Generative AI Service: For AI Specialists, this section covers dedicated AI clusters for fine-tuning and inference. The topic also focuses on the fundamentals of OCI Generative AI service, foundational models for Generation, Summarization, and Embedding.
Topic 3	Building an LLM Application with OCI Generative AI Service: For AI Engineers, this section covers Retrieval Augmented Generation (RAG) concepts, vector database concepts, and semantic search concepts. It also focuses on deploying an LLM, tracing and evaluating an LLM, and building an LLM application with RAG and LangChain.

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Oracle Cloud Infrastructure 2024 Generative AI Professional Sample Questions (Q56-Q61):

NEW QUESTION # 56
What does a higher number assigned to a token signify in the "Show Likelihoods" feature of the language model token generation?

A. The token is more likely to follow the current token.
B. The token is unrelated to the current token and will not be used.
C. The token is less likely to follow the current token.
D. The token will be the only one considered in the next generation step.

Answer: A

NEW QUESTION # 57
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 refers to contextual alignment, whereas Answer Relevance deals with syntactic accuracy.
D. Groundedness focuses on data integrity, whereas Answer Relevance emphasizes lexical diversity.

Answer: A

Explanation:
In Retrieval-Augmented Generation (RAG), two key evaluation metrics are Groundedness and Answer Relevance, each addressing different aspects of AI-generated responses.
Groundedness (Factual Correctness):
Determines whether the AI-generated answer is factually based on the retrieved data.
Ensures that AI responses are verifiable from the reference documents.
Prevents hallucination, where the model generates plausible but incorrect answers.
Answer Relevance (Query Alignment):
Evaluates how well the response aligns with the user's original query.
A response may be factually correct but irrelevant to the actual question.
Ensures that AI responses remain contextually appropriate.
Key Differences:
Groundedness ensures that responses are based on correct data.
Answer Relevance checks whether the response actually answers the user's query.
🔹 Oracle Generative AI Reference:
Oracle AI ensures fact-checking and contextual accuracy in its retrieval-augmented models, reducing AI hallucination risks.

NEW QUESTION # 58
How does the integration of a vector database into Retrieval-Augmented Generation (RAG)-based Large Language Models(LLMS) fundamentally alter their responses?

A. It shifts the basis of their responses from pretrained internal knowledge to real-time data retrieval.
B. It enables them to bypass the need for pretraining on large text corpora.
C. It transforms their architecture from a neural network to a traditional database system.
D. It limits their ability to understand and generate natural language.

Answer: A

Explanation:
The integration of a vector database into Retrieval-Augmented Generation (RAG)-based Large Language Models (LLMs) fundamentally alters their responses by shifting the basis from pretrained internal knowledge to real-time data retrieval. This means that instead of relying solely on the knowledge encoded in the model during training, the LLM can retrieve and incorporate up-to-date and relevant information from an external database in real time. This enhances the model's ability to generate accurate and contextually relevant responses.
Reference
Research papers on Retrieval-Augmented Generation (RAG) techniques
Technical documentation on integrating vector databases with LLMs

NEW QUESTION # 59
What issue might arise from using small data sets with the Vanilla fine-tuning method in the OCI Generative AI service?

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

Answer: B

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 Large Language Model based agent that focuses on generating textual responses
B. A Retrieval Augmented Generation (RAG) model that uses text as input and output
C. A language model that operates on a token-by-token output basis
D. A diffusion model that specializes in producing complex outputs.

Answer: B

NEW QUESTION # 61
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Rob Ford Rob Ford

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