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Oracle 1z0-1127-24 Exam Syllabus Topics:

Topic
Details

Topic 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.

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

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.

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Oracle Cloud Infrastructure 2024 Generative AI Professional Sample Questions (Q54-Q59):

NEW QUESTION # 54
Why is normalization of vectors important before indexing in a hybrid search system?

A. It standardizes vector lengths for meaningful comparison using metrics such as Cosine Similarity.
B. It significantly reduces the size of the database.
C. It converts all sparse vectors to dense vectors.
D. It ensures that all vectors represent keywords only.

Answer: A

NEW QUESTION # 55
Given the following code: chain = prompt |11m

A. LCEL is a declarative and preferred way to compose chains together.
B. LCEL is a programming language used to write documentation for LangChain.
C. Which statement is true about LangChain Expression language (ICED?
D. LCEL is a legacy method for creating chains in LangChain

Answer: A

Explanation:
LangChain Expression Language (LCEL) is a declarative language used to compose chains together in LangChain. It allows users to define the flow and interaction of different components in a clear and concise manner. By using LCEL, developers can easily specify how prompts, models, and other elements should interact, making the process of creating and managing chains more straightforward and efficient. This method is preferred due to its readability and ease of use, compared to more imperative or programmatic approaches.
Reference
LangChain documentation on LCEL
Examples and tutorials on using LangChain Expression Language

NEW QUESTION # 56
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 Retrieval Augmented Generation (RAG) model that uses text as input and output
B. A diffusion model that specializes in producing complex outputs.
C. A language model that operates on a token-by-token output basis
D. A Large Language Model based agent that focuses on generating textual responses

Answer: A

NEW QUESTION # 57
Which is the main characteristic of greedy decoding in the context of language model word prediction?

A. It picks the most likely word email at each step of decoding.
B. It chooses words randomly from the set of less probable candidates.
C. It requires a large temperature setting to ensure diverse word selection.
D. It selects words bated on a flattened distribution over the vocabulary.

Answer: A

Explanation:
Greedy decoding in the context of language model word prediction refers to a decoding strategy where, at each step, the model selects the word with the highest probability (the most likely word). This approach is simple and straightforward but can sometimes lead to less diverse or creative outputs because it always opts for the most likely option without considering alternative sequences that might result in better overall sentences.
Reference
Research papers on decoding strategies in language models
Technical documentation on language model inference methods

NEW QUESTION # 58
Which statement is true about string prompt templates and their capability regarding variables?

A. They are unable to use any variables.
B. They require a minimum of two variables to function properly.
C. They can only support a single variable at a time.
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 # 59
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