Exam-style
A media company has settled on a specific open-weight summarisation model. The published weights are good enough as they are, so nothing needs training or fine-tuning. What the team does need is the model running inside its own VPC, on GPU instance types it chooses and can resize as demand becomes clearer. Which of these gets them there?
Reveal the answer
B. Deploy the model from SageMaker JumpStart onto an Amazon SageMaker AI endpoint in the company's own account
Read the requirements before the service names. There are three of them: a particular set of published weights, a VPC those weights have to run inside, and an instance type the team names. Nothing asks for training. SageMaker JumpStart is a catalogue of pre-trained models that deploys the one you choose onto an Amazon SageMaker AI endpoint in your account, where you pick the instance type, the count and the network placement, so it satisfies all three at once. Invoking the same model through Amazon Bedrock gets the model right and the infrastructure wrong: Bedrock is an API, there is no instance to choose and no endpoint of yours for the traffic to reach, which is what makes it the quick route when those things do not matter and the wrong route here. Building the model in SageMaker AI answers a requirement nobody has, spending a corpus and a training bill to arrive at weights the company already has. Amazon Quick is a finished assistant for people asking questions of company content, not a place to host a model you have chosen. Kiro is a developer assistant that helps write the service around the model; it does not run the model either.
Q. A named open-weight model, no training needed, and it has to run in your VPC on instances you choose. Which of the five?
A. SageMaker JumpStart, deploying the pre-trained model onto an Amazon SageMaker AI endpoint in your own account, where the instance type and the network placement are yours.
Why? Three names, three jobs. Amazon Bedrock when you want an API and no infrastructure. SageMaker JumpStart onto Amazon SageMaker AI when you want the weights on infrastructure you choose. Amazon SageMaker AI on its own when you are training or building the model yourself. Amazon Quick and Kiro sit outside that line entirely: one is a finished assistant over company content, the other helps developers write code, and neither hosts a model you have picked.