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MLA-C02 practice question: Deploying foundation models (1 of 2)

Deployment and Orchestration of ML and AI Workflows A free question from our original MLA-C02 practice exam. Try it, then open the answer to see why each option is right or wrong.

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A ski resort group fine-tuned an open-weight language model on its own server in a data center, using its guest-feedback data. It now wants the model available through Amazon Bedrock so that its applications can call it with the same APIs and access controls as its other Bedrock models.

The weights are in a private S3 bucket in the account, and the team does not want to host or patch inference servers. The model must not be retrained on AWS.

Which approach meets these requirements?

Choose one answer.

  1. A. Create a Bedrock fine-tuning job that points at the uploaded weights, so that Bedrock registers the result as a custom model it can serve.
  2. B. Use Bedrock Custom Model Import, uploading the model weights from the S3 bucket in a supported format, and invoke the imported model through Bedrock.
  3. C. Deploy the weights on an Amazon EC2 instance with a container and call that instance from the applications through a load balancer.
  4. D. Store the model in Amazon SageMaker JumpStart and invoke it through the JumpStart catalog, since JumpStart offers all open-weight models through Bedrock.
Show the answer and explanation

Answer: B

  • A. Wrong: Bedrock fine-tuning starts from a Bedrock base model and runs training on AWS, which the stem does not want. It does not import a model that was trained elsewhere.
  • B. Right: Correct. Custom Model Import brings a model whose weights were built outside AWS into Bedrock from S3, so applications use the same Bedrock APIs and access controls. The team does not run inference servers. Learn more: Use Custom Model Import to import a customized open-source model (opens in a new tab)
  • C. Wrong: This keeps the model outside Bedrock and makes the team operate the inference servers, which the stem rules out. The applications would not use Bedrock APIs.
  • D. Wrong: JumpStart is a catalog of models for SageMaker AI, and it does not put a custom model into Bedrock. The stem asks for Bedrock APIs and access controls.

Explanation

Bedrock Custom Model Import loads a model whose weights were created outside Bedrock from S3, so the model is invoked through the Bedrock APIs with the same access controls. Fine-tuning in Bedrock trains from a Bedrock base model, and hosting on EC2 or JumpStart does not put the model in Bedrock.

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