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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	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	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.

Oracle Cloud Infrastructure 2024 Generative AI Professional Sample Questions (Q31-Q36):

NEW QUESTION # 31
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 language model that operates on a token-by-token output basis
C. A Retrieval Augmented Generation (RAG) model that uses text as input and output
D. A diffusion model that specializes in producing complex outputs.

Answer: C

NEW QUESTION # 32
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. Continuously throughout the entire chain execution process
B. Before user input and after chain execution
C. Only after the output has been generated
D. After user input but before chain execution, and again after core logic but before output

Answer: C

NEW QUESTION # 33
How are fine-tuned customer models stored to enable strong data privacy and security in the OCI Generative AI service?

A. Stored in an unencrypted form in Object Storage
B. Stored in Object Storage encrypted by default
C. Stored in Key Management service
D. Shared among multiple customers for efficiency

Answer: B

Explanation:
Fine-tuned customer models in the OCI Generative AI service are stored in Object Storage, and they are encrypted by default. This encryption ensures strong data privacy and security by protecting the model data from unauthorized access. Using encrypted storage is a key measure in safeguarding sensitive information and maintaining compliance with security standards.
Reference
OCI documentation on data storage and security practices
Technical details on encryption and data privacy in OCI services

NEW QUESTION # 34
Which is a key characteristic of the annotation process used in T-Few fine-tuning?

A. T- Few fine-tuning involves updating the weights of all layers in the model.
B. T-Few fine-tuning uses annotated data to adjust a fraction of model weights.
C. T-Few fine-tuning relies on unsupervised learning techniques for annotation.
D. T-Few fine-tuning requires manual annotation of input-output pain.

Answer: C

NEW QUESTION # 35
Which is a distinguishing feature of "Parameter-Efficient Fine-tuning (PEFT)" as opposed to classic Tine- tuning" in Large Language Model training?

A. PEFT involves only a few or new parameters and uses labeled, task-specific data.
B. PEFT does not modify any parameters but uses soft prompting with unlabeled data. PEFT modifies
C. PEFT modifies all parameters and uses unlabeled, task-agnostic data.
D. PEFT parameters and b typically used when no training data exists.

Answer: A

Explanation:
Parameter-Efficient Fine-Tuning (PEFT) is a technique used in large language model training that focuses on adjusting only a subset of the model's parameters rather than all of them. This approach involves using labeled, task-specific data to fine-tune new or a limited number of parameters. PEFT is designed to be more efficient than classic fine-tuning, which typically adjusts all the parameters of the model. By only updating a small fraction of the model's parameters, PEFT reduces the computational resources and time required for fine-tuning while still achieving significant performance improvements on specific tasks.
Reference
Research papers on Parameter-Efficient Fine-Tuning (PEFT)
Technical documentation on fine-tuning techniques for large language models

NEW QUESTION # 36
......

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Unparalleled Oracle Reliable 1z0-1127-24 Exam Cost Pass Guaranteed Quiz

1z0-1127-24 Reliable Braindumps Free & 1z0-1127-24 Exam Assessment

Oracle 1z0-1127-24 Exam Syllabus Topics:

Oracle Cloud Infrastructure 2024 Generative AI Professional Sample Questions (Q31-Q36):

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