AIF-C01 · Question #49
A company is using an Amazon Bedrock base model to summarize documents for an internal use case. The company trained a custom model to improve the summarization quality. Which action must the…
The correct answer is B. Deploy the custom model in an Amazon SageMaker endpoint for real-time inference. To use a custom model that has been trained to improve summarization quality, the company must deploy the model on an Amazon SageMaker endpoint. This allows the model to be used for real-time inference through Amazon Bedrock or other AWS services. By deploying the model in…
Question
A company is using an Amazon Bedrock base model to summarize documents for an internal use case. The company trained a custom model to improve the summarization quality. Which action must the company take to use the custom model through Amazon Bedrock?
Options
- APurchase Provisioned Throughput for the custom model.
- BDeploy the custom model in an Amazon SageMaker endpoint for real-time inference.
- CRegister the model with the Amazon SageMaker Model Registry.
- DGrant access to the custom model in Amazon Bedrock.
How the community answered
(35 responses)- A17% (6)
- B71% (25)
- C3% (1)
- D9% (3)
Explanation
To use a custom model that has been trained to improve summarization quality, the company must deploy the model on an Amazon SageMaker endpoint. This allows the model to be used for real-time inference through Amazon Bedrock or other AWS services. By deploying the model in SageMaker, the custom model can be accessed programmatically via API calls, enabling integration with Amazon Bedrock.
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