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Oracle 1z0-1127-24 Exam Syllabus Topics:
Topic
Details
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Topic 3
Oracle Cloud Infrastructure 2024 Generative AI Professional Sample Questions (Q33-Q38):
NEW QUESTION # 33
Which is a key advantage of usingT-Few over Vanilla fine-tuning in the OCI Generative AI service?
Answer: D
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 # 34
How does the integration of a vector database into Retrieval-Augmented Generation (RAG)-based Large Language Models(LLMS) fundamentally alter their responses?
Answer: B
NEW QUESTION # 35
What is the primary purpose of LangSmith Tracing?
Answer: C
NEW QUESTION # 36
Which is a key characteristic of the annotation process used in T-Few fine-tuning?
Answer: B
NEW QUESTION # 37
In LangChain, which retriever search type is used to balance between relevancy and diversity?
Answer: B
Explanation:
In LangChain, the "mmr" (Maximal Marginal Relevance) search type is used to balance between relevancy and diversity when retrieving documents. This technique aims to select documents that are not only relevant to the query but also diverse from each other. This helps in avoiding redundancy and ensures that the retrieved set of documents covers a broader aspect of the topic.
Maximal Marginal Relevance (MMR) works by iteratively selecting documents that have high relevance to the query but low similarity to the documents already selected. This ensures that each new document adds new information and perspectives, rather than repeating what is already included.
Reference
LangChain documentation on retrievers and search types
Research papers and articles on Maximal Marginal Relevance (MMR)
NEW QUESTION # 38
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