Exam AWS Certified AI Practitioner AIF-C01 topic 1 question 50 discussion

Initially I was going with D but after reading this article sticking with A https://docs.aws.amazon.com/bedrock/latest/userguide/model-customization-use.html?form=MG0AV3

A To customize model you must purchase Provisioned Throughput.

Key words in this question: "To Use" You can not use it with out permissions. If the key words were "to run" it would be Provision-A. So the answer is D. We were all network admins at one point in time, permissions are always first. Its customer. IE not already in bedrock, so you need access first.

A is the correct answer

question is about using the custom model not training. so it has to be D

A melhor opção seria D. Conceda acesso ao modelo personalizado no Amazon Bedrock. O Amazon Bedrock permite que empresas utilizem modelos básicos e personalizem modelos para casos de uso específicos. Para que a empresa possa usar o modelo treinado, ela deve garantir que o modelo personalizado esteja acessível dentro do Bedrock. Dessa forma, ele pode ser utilizado para inferências e integração com aplicações.

Correct Answer: A. Purchase Provisioned Throughput for the custom model To utilize a custom model in Amazon Bedrock, you must first purchase Provisioned Throughput for it. This step is essential to enable inference capabilities with your customized model. According to the AWS documentation: "Before you can use a customized model, you need to purchase Provisioned Throughput for it." AWS Documentation +1 AWS Documentation +1 Once Provisioned Throughput is purchased, you can perform inference by invoking the provisioned model using its ARN.

When you train a custom model using Amazon Bedrock, especially via fine-tuning a base foundation model (e.g., Anthropic Claude, AI21, etc.), the custom model is managed within Bedrock itself. To use it in your applications via Bedrock APIs, you must: Grant access to the fine-tuned (custom) model within Bedrock. This allows your applications to invoke it using Bedrock's InvokeModel API.

Amazon Bedrock allows companies to import and use their customized models alongside base models through its Custom Model Import feature. By registering the custom model within Amazon Bedrock, it can be accessed seamlessly via Bedrock’s unified API without requiring deployment in SageMaker or other infrastructure management.

Yes, D for sure.

Correct Answer: ✅ D. Grant access to the custom model in Amazon Bedrock. Explanation: Since the company has trained a custom model to enhance summarization and wants to use it through Amazon Bedrock, they must grant access to the custom model within Bedrock so it can be used for inference. • Amazon Bedrock allows fine-tuning of base models → After fine-tuning, the custom model must be registered and access must be granted. • Ensures secure and controlled model usage → This step enables API access for the custom summarization model. • Bedrock manages model deployment internally → The model does not need an Amazon SageMaker endpoint for use within Bedrock.

D. Grant access to the custom model in Amazon Bedrock: When using Amazon Bedrock, you can fine-tune models or create custom versions of base models. To use your custom model, you would need to grant access to it within the Amazon Bedrock environment, enabling the model to be accessed and invoked by your application for summarization tasks.

Provisioned Throughput helps to improve the quality.

Accordingly to https://docs.aws.amazon.com/bedrock/latest/userguide/model-customization-use.html?form=MG0AV3

A particularly insightful comment from user "may2021_r" clarifies this: "Bottom Line: Required to use a custom model? Give Bedrock permissions and register your model so it can retrieve your artifacts. Optional but recommended at scale? Purchase Provisioned Throughput to guarantee a certain level of concurrency and avoid throttling." The key distinction is: Granting access is the fundamental requirement to use the model at all Provisioned Throughput is about performance and scaling, not basic access

D: Grant access to the custom model in Amazon Bedrock. Explanation: When a company trains a custom model to improve the performance of a base model provided by Amazon Bedrock, they need to ensure the custom model is accessible through the Amazon Bedrock service. Granting access to the custom model ensures it can be integrated and used through Bedrock's APIs and workflows for inference tasks like document summarization.

The correct answer is D. Access must be granted in Bedrock to use custom models.