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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	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 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.

Oracle Cloud Infrastructure 2024 Generative AI Professional Sample Questions (Q60-Q65):

NEW QUESTION # 60
Which is a key advantage of usingT-Few over Vanilla fine-tuning in the OCI Generative AI service?

A. Reduced model complexity
B. Enhanced generalization to unseen data
C. Increased model interpretability
D. Foster training time and lower cost

Answer: D

Explanation:
The key advantage of using T-Few over Vanilla fine-tuning in the OCI Generative AI service is faster training time and lower cost. T-Few fine-tuning is designed to be more efficient by updating only a fraction of the model's parameters, which significantly reduces the computational resources and time required for fine-tuning. This efficiency translates to lower costs, making it a more economical choice for model fine-tuning.
Reference
Technical documentation on T-Few fine-tuning
Research articles comparing fine-tuning methods in machine learning

NEW QUESTION # 61
In which scenario is soft prompting appropriate compared to other training styles?

A. When the model requires continued pretraining on unlabeled data
B. When there is a need to add learnable parameters to a Large Language Model (LLM) without task-specific training
C. When there is a significant amount of labeled, task-specific data available
D. When the model needs to be adapted to perform well in a domain on which it was not originally trained

Answer: B

Explanation:
Soft prompting is an efficient method for modifying LLM behavior without full retraining. Unlike fine-tuning, soft prompting adds learnable embeddings (soft prompts) to guide the model.
When Soft Prompting is Useful:
Enhances model behavior without full retraining.
Uses small trainable prompt tokens, avoiding large parameter updates.
Works well when labeled, task-specific data is unavailable.
Why Other Options Are Incorrect:
(A) is incorrect because continued pretraining involves modifying core model weights.
(C) is incorrect because adapting a model to a new domain is better suited to fine-tuning or full retraining.
(D) is incorrect because soft prompting is designed for low-data scenarios, while full fine-tuning requires labeled datasets.
🔹 Oracle Generative AI Reference:
Oracle AI supports efficient adaptation methods, including soft prompting and LoRA, to improve LLM flexibility.

NEW QUESTION # 62
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 support any number of variables, including the possibility of having none.
D. They require a minimum of two variables to function properly.

Answer: C

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 # 63
What do prompt templates use for templating in language model applications?

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

Answer: B

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 # 64
How does the integration of a vector database into Retrieval-Augmented Generation (RAG)-based Large Language Models(LLMS) fundamentally alter their responses?

A. It limits their ability to understand and generate natural language.
B. It enables them to bypass the need for pretraining on large text corpora.
C. It transforms their architecture from a neural network to a traditional database system.
D. It shifts the basis of their responses from pretrained internal knowledge to real-time data retrieval.

Answer: D

NEW QUESTION # 65
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Pass the Oracle 1z0-1127-24 certification exam with flying colors

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Oracle Cloud Infrastructure 2024 Generative AI Professional Sample Questions (Q60-Q65):

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