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Oracle 1z0-1127-24 Prüfungsplan:

Thema	Einzelheiten
Thema 1	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.
Thema 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.
Thema 3	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.

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1z0-1127-24 Dumps & 1z0-1127-24 Testantworten

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Oracle Cloud Infrastructure 2024 Generative AI Professional 1z0-1127-24 Prüfungsfragen mit Lösungen (Q26-Q31):

26. Frage
Which component of Retrieval-Augmented Generation (RAG) evaluates and prioritizes the information retrieved by the retrieval system?

A. Generator
B. Retriever
C. Ranker
D. Encoder-decoder

Antwort: C

27. Frage
Which technique involves prompting the Large Language Model (LLM) to emit intermediate reasoning steps as part of its response?

A. Chain-of-Through
B. Least to most Prompting
C. In context Learning
D. Step-Bock Prompting

Antwort: A

28. Frage
Which is NOT a category of pertained foundational models available in the OCI Generative AI service?

A. Embedding models
B. Summarization models
C. Translation models
D. Generation models

Antwort: C

29. Frage
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 are they crucial for language models?

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

Antwort: A

Begründung:
Vector databases store word, sentence, or document embeddings that preserve semantic meaning. These embeddings capture relationships between concepts in a multi-dimensional space, improving LLM performance.
Why Semantic Relationships Are Crucial:
Enhance NLP Models: Ensure that words with similar meanings are closely placed in vector space.
Improve Search and Retrieval: Allow LLMs to retrieve conceptually relevant documents even if exact keywords do not match.
Enable Context-Aware Responses: Helps LLMs generate cohesive and meaningful text.
Why Other Options Are Incorrect:
(A) Hierarchical relationships help in database indexing, but they do not drive semantic understanding.
(B) Linear relationships are too simplistic for complex semantic modeling.
(D) Temporal relationships matter for time-based predictions, not semantic retrieval.
🔹 Oracle Generative AI Reference:
Oracle AI integrates vector databases to enhance LLM retrieval accuracy and semantic search capabilities.

30. Frage
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 Large Language Model based agent that focuses on generating textual responses
C. A Retrieval Augmented Generation (RAG) model that uses text as input and output
D. A language model that operates on a token-by-token output basis

Antwort: C

31. Frage
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