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Question No 1

In LangChain, which retriever search type is used to balance between relevancy and diversity?

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Question No 2

What does a dedicated RDMA cluster network do during model fine - tuning and inference?

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It leads to higher latency in model inference.

It enables the deployment of multiple fine - tuned models.

It limits the number of fine - tuned model deployable on the same GPU cluster.

It increases G PU memory requirements for model deployment.

Question No 3

Which role docs a "model end point" serve in the inference workflow of the OCI Generative AI service?

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Hosts the training data for fine - tuning custom model

Evaluates the performance metrics of the custom model

Serves as a designated point for user requests and model responses

Updates the weights of the base model during the fine - tuning process

Question No 4

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

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PEFT involves only a few or new parameters and uses labeled, task - specific data.

PEFT modifies all parameters and uses unlabeled, task - agnostic data.

PEFT does not modify any parameters but uses soft prompting with unlabeled data. PEFT modifies

PEFT parameters and b typically used when no training data exists.

Question No 5

How does the Retrieval - Augmented Generation (RAG) Token technique differ from RAG Sequence when generating a model's response?

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Unlike RAG Sequence, RAG Token generates the entire response at once without considering individual parts.

RAG Token does not use document retrieval but generates responses based on pre - existing knowledge only.

RAG Token retrieves documents oar/at the beginning of the response generation and uses those for the entire content

RAG Token retrieves relevant documents for each part of the response and constructs the answer incrementally.

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