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實用的1z0-1127-24最新考證以及資格考試的領先材料供應商和一流的1z0-1127-24考題免費下載

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最新的 Oracle Cloud Infrastructure 1z0-1127-24 免費考試真題 (Q20-Q25):

問題 #20
You create a fine-tuning dedicated AI cluster to customize a foundational model with your custom training dat a. How many unit hours arc required for fine-tuning if the cluster is active for 10 hours?

A. 30 unit hours
B. 40 unit hours
C. 15 unit hours
D. 10 unit hours

答案：D

問題 #21
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 parameters and b typically used when no training data exists.
D. PEFT involves only a few or new parameters and uses labeled, task-specific data.

答案：D

解題說明：
Parameter-Efficient Fine-Tuning (PEFT) is a technique used in large language model training that focuses on adjusting only a subset of the model's parameters rather than all of them. This approach involves using labeled, task-specific data to fine-tune new or a limited number of parameters. PEFT is designed to be more efficient than classic fine-tuning, which typically adjusts all the parameters of the model. By only updating a small fraction of the model's parameters, PEFT reduces the computational resources and time required for fine-tuning while still achieving significant performance improvements on specific tasks.
Reference
Research papers on Parameter-Efficient Fine-Tuning (PEFT)
Technical documentation on fine-tuning techniques for large language models

問題 #22
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 incorporating additional layers to the base model
C. By excluding transformer layers from the fine-tuning process entirely
D. By restricting updates to only a specific croup of transformer Layers

答案：D

問題 #23
Given the following code: chain = prompt |11m

A. Which statement is true about LangChain Expression language (ICED?
B. LCEL is a declarative and preferred way to compose chains together.
C. LCEL is a legacy method for creating chains in LangChain
D. LCEL is a programming language used to write documentation for LangChain.

答案：B

解題說明：
LangChain Expression Language (LCEL) is a declarative language used to compose chains together in LangChain. It allows users to define the flow and interaction of different components in a clear and concise manner. By using LCEL, developers can easily specify how prompts, models, and other elements should interact, making the process of creating and managing chains more straightforward and efficient. This method is preferred due to its readability and ease of use, compared to more imperative or programmatic approaches.
Reference
LangChain documentation on LCEL
Examples and tutorials on using LangChain Expression Language

問題 #24
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 is unrelated to the current token and will not be used.
D. The token will be the only one considered in the next generation step.

答案：A

問題 #25
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