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Latest Oracle 1z0-1127-24 Dumps - Eliminate Your Risk of Failing [2025]
Oracle dumps are designed according to the Oracle 1z0-1127-24 certification exam standard and have hundreds of questions similar to the actual 1z0-1127-24 exam. 2Pass4sure Oracle Cloud Infrastructure 2024 Generative AI Professional (1z0-1127-24) web-based practice exam software also works without installation. It is browser-based; therefore no need to install it, and you can start practicing for the Oracle Cloud Infrastructure 2024 Generative AI Professional (1z0-1127-24) exam by creating the Oracle 1z0-1127-24 practice test.
Oracle 1z0-1127-24 Exam Syllabus Topics:
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Oracle Cloud Infrastructure 2024 Generative AI Professional Sample Questions (Q53-Q58):
NEW QUESTION # 53
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 # 54
How does the Retrieval-Augmented Generation (RAG) Token technique differ from RAG Sequence when generating a model's response?
- A. RAG Token retrieves relevant documents for each part of the response and constructs the answer incrementally.
- B. Unlike RAG Sequence, RAG Token generates the entire response at once without considering individual parts.
- C. RAG Token retrieves documents oar/at the beginning of the response generation and uses those for the entire content
- D. RAG Token does not use document retrieval but generates responses based on pre-existing knowledge only.
Answer: A
Explanation:
The Retrieval-Augmented Generation (RAG) technique enhances the response generation process of language models by incorporating relevant external documents. RAG Token and RAG Sequence are two variations of this technique.
RAG Token retrieves relevant documents for each part of the response and constructs the answer incrementally. This means that during the response generation process, the model continuously retrieves and incorporates information from external documents as it generates each token (or part) of the response. This allows for more dynamic and contextually relevant answers, as the model can adjust its retrieval based on the evolving context of the response.
In contrast, RAG Sequence typically retrieves documents once at the beginning of the response generation and uses those documents to generate the entire response. This approach is less dynamic compared to RAG Token, as it does not adjust the retrieval process during the generation of the response.
Reference
Research articles on Retrieval-Augmented Generation (RAG) techniques
Documentation on advanced language model inference methods
NEW QUESTION # 55
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 list comprehension syntax
- D. Python's lambda functions
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 # 56
What issue might arise from using small data sets with the Vanilla fine-tuning method in the OCI Generative AI service?
- A. Model Drift
- B. Data Leakage
- C. Overfilling
- D. Underfitting
Answer: C
NEW QUESTION # 57
What does a cosine distance of 0 indicate about the relationship between two embeddings?
- A. They are unrelated
- B. They have the same magnitude
- C. They are completely dissimilar
- D. They are similar in direction
Answer: D
NEW QUESTION # 58
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