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Oracle Cloud Infrastructure 2024 Generative AI Professional Sample Questions (Q56-Q61):

NEW QUESTION # 56
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 diffusion model that specializes in producing complex outputs.
B. A Retrieval Augmented Generation (RAG) model that uses text as input and output
C. A language model that operates on a token-by-token output basis
D. A Large Language Model based agent that focuses on generating textual responses

Answer: A

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 # 57
What does "k-shot prompting* refer to when using Large Language Models for task-specific applications?

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

Answer: C

NEW QUESTION # 58
Which statement is NOT true about StreamlitChatMessageHistory?

A. A given StreamlitChatMessageHistory will not be shared across user sessions.
B. StreamlitChatMessageHistory will store messages in Streamlit session state at the specified key.
C. A given StreamlitChatMessageHistory will NOT be persisted.
D. StreamlitChatMessageHistory can be used in any type of LLM application.

Answer: D

Explanation:
StreamlitChatMessageHistory is a chat message storage tool in Streamlit, used to manage message history within LLM-powered applications.
Key Features of StreamlitChatMessageHistory:
Stores chat messages within Streamlit's session state.
Not persistent across sessions; resets when the session is closed.
Specific to Streamlit applications, not designed for all LLM applications.
Why Option (D) is Incorrect:
StreamlitChatMessageHistory is designed for Streamlit-based apps.
It is not suitable for all LLM applications, particularly those requiring persistent storage.
Why Other Options Are Correct:
(A) True: Each session has its own instance of StreamlitChatMessageHistory.
(B) True: It is not persisted across sessions.
(C) True: It stores messages in the Streamlit session state.
🔹 Oracle Generative AI Reference:
While Oracle AI supports various LLM applications, StreamlitChatMessageHistory is limited to Streamlit-based chat interfaces.

NEW QUESTION # 59
How does the architecture of dedicated Al clusters contribute to minimizing GPU memory overhead forT- Few fine-tuned model inference?

A. By optimizing GPIJ memory utilization for each model's unique para
B. By sharing base model weights across multiple fine-tuned model's on the same group of GPUs
C. By loading the entire model into G PU memory for efficient processing
D. By allocating separate GPUS for each model instance

Answer: B

Explanation:
The architecture of dedicated AI clusters contributes to minimizing GPU memory overhead for fine-tuned model inference by sharing base model weights across multiple fine-tuned models on the same group of GPUs. This approach allows different fine-tuned models to leverage the shared base model weights, reducing the memory requirements and enabling efficient use of GPU resources. By not duplicating the base model weights for each fine-tuned model, the system can handle more models simultaneously with lower memory overhead.
Reference
Technical documentation on AI cluster architectures
Research articles on optimizing GPU memory utilization in model inference

NEW QUESTION # 60
Which is NOT a typical use case for LangSmith Evaluators?

A. Evaluating factual accuracy of outputs
B. Aliening code readability
C. Detecting bias or toxicity
D. Measuring coherence of generated text

Answer: B

Explanation:
LangSmith Evaluators are not typically used for aligning code readability. Instead, they are used for tasks such as measuring the coherence of generated text, evaluating the factual accuracy of outputs, and detecting bias or toxicity. Evaluators help ensure the quality and reliability of the outputs generated by language models.
Reference
LangSmith documentation on evaluators
Research articles on evaluation metrics for language models

NEW QUESTION # 61
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