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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	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 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
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 modifies all parameters and uses unlabeled, task-agnostic data.
C. PEFT does not modify any parameters but uses soft prompting with unlabeled data. PEFT modifies
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 # 57
Which statement is true about string prompt templates and their capability regarding variables?

A. They can only support a single variable at a time.
B. They are unable to use any variables.
C. They require a minimum of two variables to function properly.
D. They support any number of variables, including the possibility of having none.

Answer: D

Explanation:
A string prompt template is a mechanism used to structure prompts dynamically by inserting variables. These templates are commonly used in LLM-powered applications like chatbots, text generation, and automation tools.
How Prompt Templates Handle Variables:
They support an unlimited number of variables or can work without any variables.
Variables are typically denoted by placeholders such as {variable_name} or {{variable_name}} in frameworks like LangChain or Oracle AI.
Users can dynamically populate these placeholders to generate different prompts without rewriting the entire template.
Example of a Prompt Template:
Without variables: "What is the capital of France?"
With one variable: "What is the capital of {country}?"
With multiple variables: "What is the capital of {country}, and what language is spoken there?" Why Other Options Are Incorrect:
(B) is false because templates can work with one or no variables.
(C) is false because templates rely on variables for dynamic input.
(D) is false because templates can handle multiple placeholders.
???? Oracle Generative AI Reference:
Oracle integrates prompt engineering capabilities into its AI platforms, allowing developers to create scalable, reusable prompts for various AI applications.

NEW QUESTION # 58
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 language model that operates on a token-by-token output basis
C. A Large Language Model based agent that focuses on generating textual responses
D. A Retrieval Augmented Generation (RAG) model that uses text as input and output

Answer: D

NEW QUESTION # 59
Which statement best describes the role of encoder and decoder models in natural language processing?

A. Encoder models are used only for numerical calculations, whereas decoder models are used to interpret the calculated numerical values back into text.
B. Encoder models take a sequence of words and predict the next word in the sequence, whereas decoder models convert a sequence of words into a numerical representation.
C. Encoder models and decoder models both convert sequence* of words into vector representations without generating new text.
D. Encoder models convert a sequence of words into a vector representation, and decoder models take this vector representation to sequence of words.

Answer: D

Explanation:
In natural language processing (NLP), encoder and decoder models play distinct but complementary roles:
Encoder Models: These models convert a sequence of words into a vector representation. They capture the semantic meaning of the input text and encode it into a fixed-size vector.
Decoder Models: These models take the vector representation generated by the encoder and convert it back into a sequence of words. This process allows for generating new text based on the encoded information, such as in translation or text generation tasks.
Reference
Research articles on encoder-decoder architectures in NLP
Technical guides on the use of encoder and decoder models in machine translation and text generation

NEW QUESTION # 60
What distinguishes the Cohere Embed v3 model from its predecessor in the OCI Generative AI service?

A. Emphasis on syntactic clustering of word embedding's
B. Support for tokenizing longer sentences
C. Capacity to translate text in over u languages
D. Improved retrievals for Retrieval Augmented Generation (RAG) systems

Answer: D

NEW QUESTION # 61
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1Z0-1127-24 INTERACTIVE QUESTIONS & 1Z0-1127-24 RELIABLE BRAINDUMPS PDF

1z0-1127-24 Interactive Questions & 1z0-1127-24 Reliable Braindumps Pdf