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Oracle Cloud Infrastructure 2024 Generative AI Professional Sample Questions (Q57-Q62):

NEW QUESTION # 57
How can the concept of "Groundedness" differ from "Answer Relevance" in the context of Retrieval Augmented Generation (RAG)?

  • A. Groundedness pertains to factual correctness, whereas Answer Relevance concerns query relevance.
  • B. Groundedness measures relevance to the user query, whereas Answer Relevance evaluates data integrity.
  • C. Groundedness focuses on data integrity, whereas Answer Relevance emphasizes lexical diversity.
  • D. Groundedness refers to contextual alignment, whereas Answer Relevance deals with syntactic accuracy.

Answer: A

Explanation:
In Retrieval-Augmented Generation (RAG), two key evaluation metrics are Groundedness and Answer Relevance, each addressing different aspects of AI-generated responses.
Groundedness (Factual Correctness):
Determines whether the AI-generated answer is factually based on the retrieved data.
Ensures that AI responses are verifiable from the reference documents.
Prevents hallucination, where the model generates plausible but incorrect answers.
Answer Relevance (Query Alignment):
Evaluates how well the response aligns with the user's original query.
A response may be factually correct but irrelevant to the actual question.
Ensures that AI responses remain contextually appropriate.
Key Differences:
Groundedness ensures that responses are based on correct data.
Answer Relevance checks whether the response actually answers the user's query.
???? Oracle Generative AI Reference:
Oracle AI ensures fact-checking and contextual accuracy in its retrieval-augmented models, reducing AI hallucination risks.


NEW QUESTION # 58
Which is a key advantage of usingT-Few over Vanilla fine-tuning in the OCI Generative AI service?

  • A. Increased model interpretability
  • B. Foster training time and lower cost
  • C. Enhanced generalization to unseen data
  • D. Reduced model complexity

Answer: B

Explanation:
The key advantage of using T-Few over Vanilla fine-tuning in the OCI Generative AI service is faster training time and lower cost. T-Few fine-tuning is designed to be more efficient by updating only a fraction of the model's parameters, which significantly reduces the computational resources and time required for fine-tuning. This efficiency translates to lower costs, making it a more economical choice for model fine-tuning.
Reference
Technical documentation on T-Few fine-tuning
Research articles comparing fine-tuning methods in machine learning


NEW QUESTION # 59
Which is NOT a built-in memory type in LangChain?

  • A. Conversation Summary Memory
  • B. Conversation Token Buffer Memory
  • C. Conversation ImgeMemory
  • D. Conversation Buffer Memory

Answer: C

Explanation:
In LangChain, "Conversation Image Memory" is not a built-in memory type. The built-in memory types in LangChain include:
Conversation Token Buffer Memory: This memory type stores a buffer of tokens from the conversation history.
Conversation Buffer Memory: This memory type retains a buffer of conversation history, typically in the form of text.
Conversation Summary Memory: This memory type summarizes the conversation history to keep track of key points and information.
These memory types help manage and utilize conversation history in various ways to enhance the performance of conversational models.
Reference
LangChain documentation on memory types
Technical guides on implementing memory in conversational AI systems


NEW QUESTION # 60
Which is a key advantage of usingT-Few over Vanilla fine-tuning in the OCI Generative AI service?

  • A. Increased model interpretability
  • B. Foster training time and lower cost
  • C. Enhanced generalization to unseen data
  • D. Reduced model complexity

Answer: B


NEW QUESTION # 61
How does the utilization of T-Few transformer layers contribute to the efficiency of the fine-tuning process?

  • A. By allowing updates across all layers of the model
  • B. By restricting updates to only a specific croup of transformer Layers
  • C. By incorporating additional layers to the base model
  • D. By excluding transformer layers from the fine-tuning process entirely

Answer: B

Explanation:
The utilization of T-Few transformer layers contributes to the efficiency of the fine-tuning process by restricting updates to only a specific group of transformer layers. This selective updating approach allows the model to adapt to new data without the need to retrain all layers, thus saving computational resources and time. By focusing on the most relevant parts of the model, T-Few fine-tuning achieves efficient and effective performance improvements.
Reference
Research papers on T-Few fine-tuning techniques
Technical guides on optimizing transformer models


NEW QUESTION # 62
......

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