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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.

Oracle Cloud Infrastructure 2024 Generative AI Professional Sample Questions (Q63-Q68):

NEW QUESTION # 63
Which is a cost-related benefit of using vector databases with Large Language Models (LLMs)?

A. They offer real-time updated knowledge bases and are cheaper than fine-tuned LLMs.
B. They increase the cost due to the need for real- time updates.
C. They require frequent manual updates, which increase operational costs.
D. They are more expensive but provide higher quality data.

Answer: A

NEW QUESTION # 64
How are fine-tuned customer models stored to enable strong data privacy and security in the OCI Generative AI service?

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

Answer: C

NEW QUESTION # 65
What distinguishes the Cohere Embed v3 model from its predecessor in the OCI Generative AI service?

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

Answer: C

NEW QUESTION # 66
What do prompt templates use for templating in language model applications?

A. Python's str.format syntax
B. Python's list comprehension syntax
C. Python's lambda functions
D. Python's class and object structures

Answer: A

Explanation:
Prompt templates are structured text-based input patterns that include placeholders for dynamic variable substitution. These templates help generate prompts for LLMs (Large Language Models) in a systematic and reusable way.
Prompt Template Example using str.format():
template = "What is the capital of {country}?"
formatted_prompt = template.format(country="France")
print(formatted_prompt) # Output: "What is the capital of France?"
Why str.format() is Used:
It allows dynamic insertion of variables.
It is flexible and widely supported in Python-based AI frameworks.
Used in LangChain, OpenAI API, and Oracle AI applications.
Why Other Options Are Incorrect:
(A) Lambda functions are used for anonymous function execution, not string templating.
(C) List comprehensions are used for iterating over lists, not text formatting.
(D) Class and object structures define OOP models, not LLM prompt templates.
🔹 Oracle Generative AI Reference:
Oracle AI frameworks use Python's str.format() and f-strings for LLM prompt engineering and AI-driven workflow automation.

NEW QUESTION # 67
Which statement is true about string prompt templates and their capability regarding variables?

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

Answer: A

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 # 68
......

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1z0-1127-24 Reliable Test Sample, 1z0-1127-24 Reliable Exam Vce

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Oracle Cloud Infrastructure 2024 Generative AI Professional Sample Questions (Q63-Q68):

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