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Oracle Cloud Infrastructure 2024 Generative AI Professional Sample Questions (Q31-Q36):

NEW QUESTION # 31
How does the integration of a vector database into Retrieval-Augmented Generation (RAG)-based Large Language Models(LLMS) fundamentally alter their responses?

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

Answer: A

Explanation:
The integration of a vector database into Retrieval-Augmented Generation (RAG)-based Large Language Models (LLMs) fundamentally alters their responses by shifting the basis from pretrained internal knowledge to real-time data retrieval. This means that instead of relying solely on the knowledge encoded in the model during training, the LLM can retrieve and incorporate up-to-date and relevant information from an external database in real time. This enhances the model's ability to generate accurate and contextually relevant responses.
Reference
Research papers on Retrieval-Augmented Generation (RAG) techniques
Technical documentation on integrating vector databases with LLMs

NEW QUESTION # 32
What is the primary purpose of LangSmith Tracing?

A. To generate test cases for language models
B. To monitor the performance of language models
C. To debug issues in language model outputs
D. To analyze the reasoning process of language

Answer: C

Explanation:
The primary purpose of LangSmith Tracing is to debug issues in language model outputs. LangSmith Tracing allows developers to trace and analyze the sequence of operations and decisions made by the model during the generation process. This helps identify and resolve problems, ensuring the model's outputs are accurate and reliable.
Reference
LangSmith documentation on tracing and debugging
Tutorials on using tracing tools for language model development

NEW QUESTION # 33
What does "k-shot prompting* refer to when using Large Language Models for task-specific applications?

A. Providing the exact k words in the prompt to guide the model's response
B. Explicitly providing k examples of the intended task in the prompt to guide the models output
C. Limiting the model to only k possible outcomes or answers for a given task
D. The process of training the model on k different tasks simultaneously to improve its versatility

Answer: B

Explanation:
K-shot prompting refers to providing the language model with k examples of the task at hand within the prompt. These examples help guide the model's understanding and output by demonstrating the desired format and approach. This technique is used to improve the model's performance on specific tasks by showing it how to handle similar situations.
Reference
Research papers on few-shot learning and prompting techniques
Technical documentation on using examples in prompts for large language models

NEW QUESTION # 34
What does a higher number assigned to a token signify in the "Show Likelihoods" feature of the language model token generation?

A. The token is more likely to follow the current token.
B. The token is less likely to follow the current token.
C. The token will be the only one considered in the next generation step.
D. The token is unrelated to the current token and will not be used.

Answer: A

NEW QUESTION # 35
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 language model that operates on a token-by-token output basis
B. A Large Language Model based agent that focuses on generating textual responses
C. A diffusion model that specializes in producing complex outputs.
D. A Retrieval Augmented Generation (RAG) model that uses text as input and output

Answer: C

Explanation:
An AI development company aiming to create an assistant capable of analyzing images and generating descriptive text, as well as converting text descriptions into accurate visual representations, would likely focus on integrating a diffusion model. Diffusion models are advanced generative models that specialize in producing complex outputs, including high-quality images from textual descriptions and vice versa.
Reference
Research papers on diffusion models and their applications
Technical documentation on generative models for image and text synthesis

NEW QUESTION # 36
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