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Oracle Cloud Infrastructure 2024 Generative AI Professional Sample Questions (Q48-Q53):

NEW QUESTION # 48
Given the following code:
Prompt Template
(input_variable[''rhuman_input",'city''], template-template)
Which statement is true about Promt Template in relation to input_variables?

A. PromptTemplate is unable to use any variables.
B. PromptTemplate requires a minimum of two variables to function property.
C. PromptTemplate supports Any number of variable*, including the possibility of having none.
D. PromptTemplate can support only a single variable M a time.

Answer: C

NEW QUESTION # 49
Which is a key characteristic of the annotation process used in T-Few fine-tuning?

A. T-Few fine-tuning uses annotated data to adjust a fraction of model weights.
B. T-Few fine-tuning relies on unsupervised learning techniques for annotation.
C. T-Few fine-tuning requires manual annotation of input-output pain.
D. T- Few fine-tuning involves updating the weights of all layers in the model.

Answer: B

NEW QUESTION # 50
Given the following code: chain = prompt |11m

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

Answer: B

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 # 51
What does the RAG Sequence model do in the context of generating a response?

A. It modifies the input query before retrieving relevant documents to ensure a diverse response.
B. It retrieves a single relevant document for the entire input query and generates a response based on that alone.
C. It retrieves relevant documents only for the initial part of the query and ignores the rest.
D. For each input query, it retrieves a set of relevant documents and considers them together to generate a cohesive response.

Answer: D

Explanation:
RAG (Retrieval-Augmented Generation) Sequence models combine retrieval-based search with LLM-generated responses, ensuring factually grounded and contextually relevant outputs.
How the RAG Sequence Model Works:
Retrieves multiple documents for an input query.
Uses all retrieved documents collectively to generate a well-informed response.
Ensures the answer is contextually aware and factually accurate.
Why Other Options Are Incorrect:
(A) is incorrect because RAG does not ignore part of the query.
(B) is incorrect because it does not rely on a single document.
(C) is incorrect because RAG does not modify the input query but focuses on retrieval and generation.
🔹 Oracle Generative AI Reference:
Oracle AI implements RAG-based architectures to enhance LLM-generated responses by retrieving and grounding responses in factual data.

NEW QUESTION # 52
Given a block of code:
qa = Conversational Retrieval Chain, from 11m (11m, retriever-retv, memory-memory) when does a chain typically interact with memory during execution?

A. Before user input and after chain execution
B. After user input but before chain execution, and again after core logic but before output
C. Continuously throughout the entire chain execution process
D. Only after the output has been generated

Answer: B

Explanation:
In a Conversational Retrieval Chain using LangChain, the chain typically interacts with memory at two key points: after the user input but before the chain execution, and again after the core logic but before the output is generated. This approach allows the system to update the memory with relevant context before executing the chain's main logic and then update the memory again with any new information or context gained during the execution before producing the final output.
Reference
LangChain documentation on Conversational Retrieval Chains
Technical guides on managing memory in conversational AI models

NEW QUESTION # 53
......

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Oracle 1z0-1127-24 Test Review & Latest 1z0-1127-24 Practice Questions

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Oracle Cloud Infrastructure 2024 Generative AI Professional Sample Questions (Q48-Q53):

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