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Oracle 1z0-1127-24 Exam Syllabus Topics:

Topic	Details
Topic 1	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 2	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.
Topic 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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Oracle Cloud Infrastructure 2024 Generative AI Professional Sample Questions (Q60-Q65):

NEW QUESTION # 60
Which Oracle Accelerated Data Science (ADS) class can be used to deploy a Large Language Model (LLM) application to OCI Data Science model deployment?

A. Text Leader
B. Chain Deployment
C. GenerativeAI
D. RetrievalQA

Answer: C

Explanation:
The Oracle Accelerated Data Science (ADS) class that can be used to deploy a Large Language Model (LLM) application to OCI Data Science model deployment is GenerativeAI. This class provides the necessary tools and functions to work with generative AI models, including deployment, fine-tuning, and inference capabilities. It integrates with OCI Data Science to streamline the process of deploying and managing LLM applications.
Reference
Oracle ADS documentation
Guides on deploying AI models using Oracle Data Science services

NEW QUESTION # 61
How are documents usually evaluated in the simplest form of keyword-based search?

A. Based on the presence and frequency of the user-provided keywords
B. By the complexity of language used in the documents
C. Based on the number of images and videos contained in the documents
D. According to the length of the documents

Answer: A

Explanation:
In the simplest form of keyword-based search, documents are evaluated based on keyword matching and term frequency. This approach does not account for context, semantics, or the meaning behind the words, but rather focuses on:
Presence of Keywords - If a document contains the search term, it is considered relevant.
Term Frequency (TF) - The more a keyword appears in a document, the higher the ranking in basic search algorithms.
Inverse Document Frequency (IDF) - Words that are common across many documents (e.g., "the," "is") are given less weight, while rare words are prioritized.
Boolean Matching - Some basic search engines support logical operators like AND, OR, and NOT to refine keyword searches.
Exact Match vs. Partial Match - Some systems prioritize exact keyword matches, while others allow partial or fuzzy matches.
🔹 Oracle Generative AI Reference:
Oracle has implemented semantic search and advanced AI-driven document search techniques in its cloud solutions, but traditional keyword-based search still forms the foundation of many enterprise search mechanisms.

NEW QUESTION # 62
Given the following code:
Prompt Template
(input_variable[''rhuman_input",'city''], template-template)
Which statement is true about Promt Template in relation to input_variables?

A. PromptTemplate requires a minimum of two variables to function property.
B. PromptTemplate is unable to use any variables.
C. PromptTemplate can support only a single variable M a time.
D. PromptTemplate supports Any number of variable*, including the possibility of having none.

Answer: D

NEW QUESTION # 63
When should you use the T-Few fine-tuning method for training a model?

A. For complicated semantical undemanding improvement
B. For models that require their own hosting dedicated Al duster
C. For data sets with a few thousand samples or less
D. For data sets with hundreds of thousands to millions of samples

Answer: C

Explanation:
The T-Few fine-tuning method is particularly suitable for data sets with a few thousand samples or less. This method is designed to be efficient and effective with limited data, making it ideal for scenarios where collecting large amounts of training data is impractical. T-Few fine-tuning allows for meaningful adjustments to the model even with smaller data sets, providing good performance improvements without requiring extensive data.
Reference
Articles on fine-tuning techniques for small data sets
Technical documentation on T-Few fine-tuning in machine learning models

NEW QUESTION # 64
Given a block of code:
qa = Conversational Retrieval Chain, from 11m (11m, retriever-retv, memory-memory) when does a chain typically interact with memory during execution?

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

Answer: C

NEW QUESTION # 65
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