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Oracle Cloud Infrastructure 2024 Generative AI Professional Sample Questions (Q42-Q47):

NEW QUESTION # 42
Which LangChain component is responsible for generating the linguistic output in a chatbot system?

A. Vector Stores
B. LLMs
C. Document Loaders
D. LangChain Application

Answer: B

Explanation:
LangChain is an open-source framework that helps integrate Large Language Models (LLMs) into applications. In a chatbot system, the LLM (Large Language Model) component is responsible for generating linguistic output, as it processes user inputs and generates human-like responses.
Key components of LangChain include:
Document Loaders - Responsible for extracting and processing external data sources before passing them to the LLM.
Vector Stores - Used for storing and retrieving vector embeddings of documents for semantic search and similarity retrieval.
LLMs (Large Language Models) - This is the core component responsible for understanding prompts and generating text-based outputs in a chatbot.
LangChain Applications - The overall framework that connects all components but does not directly generate text.
🔹 Oracle Generative AI Reference:
Oracle supports LLM-driven chatbots and enterprise AI solutions, utilizing frameworks like LangChain to enhance AI capabilities.

NEW QUESTION # 43
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: D

NEW QUESTION # 44
Which is a distinguishing feature of "Parameter-Efficient Fine-tuning (PEFT)" as opposed to classic Tine- tuning" in Large Language Model training?

A. PEFT does not modify any parameters but uses soft prompting with unlabeled data. PEFT modifies
B. PEFT modifies all parameters and uses unlabeled, task-agnostic data.
C. PEFT involves only a few or new parameters and uses labeled, task-specific data.
D. PEFT parameters and b typically used when no training data exists.

Answer: C

NEW QUESTION # 45
How are fine-tuned customer models stored to enable strong data privacy and security in the OCI Generative AI service?

A. Stored in Key Management service
B. Shared among multiple customers for efficiency
C. Stored in Object Storage encrypted by default
D. Stored in an unencrypted form in Object Storage

Answer: C

NEW QUESTION # 46
Which statement is true about the "Top p" parameter of the OCI Generative AI Generation models?

A. Top p determines the maximum number of tokens per response.
B. Top p limits token selection based on the sum of their probabilities.
C. Top p selects tokens from the "Top k' tokens sorted by probability.
D. Top p assigns penalties to frequently occurring tokens.

Answer: B

Explanation:
The "Top p" parameter, also known as nucleus sampling, in generative AI models limits token selection based on the sum of their probabilities. It ensures that the cumulative probability of the selected tokens meets or exceeds a specified threshold p. This approach dynamically includes as many tokens as necessary to reach the desired probability sum, allowing for more diverse and contextually appropriate outputs compared to a fixed top-k selection.
Reference
Research papers on nucleus sampling and token selection methods
OCI Generative AI model documentation

NEW QUESTION # 47
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