Exam AWS Certified AI Practitioner AIF-C01 topic 1 question 62 discussion

Explicação: A transformação em lote (batch transform) do Amazon SageMaker é ideal quando: Você precisa realizar inferência em grandes volumes de dados (vários GBs), Não há necessidade de resposta em tempo real, Os dados estão arquivados (por exemplo, em Amazon S3), O processamento pode ocorrer de forma agendada ou sob demanda, com resultados também armazenados em lote. Essa abordagem é eficiente, escalável e econômica para casos em que a latência não é um fator crítico. D. Inferência assíncrona ➡️ Boa para requisições grandes com latência variável, mas ainda envolve chamadas via endpoint. Batch transform é mais otimizado quando os dados já estão arquivados e processados em massa.

Batch transform is ideal for scenarios where you need to perform inference on large datasets, but the predictions are not needed immediately.

A. Batch transform: This is the best option for performing inference on large datasets that are stored in bulk and do not require immediate access to predictions. Batch transform allows you to process large amounts of data (such as multiple GBs of archived data) in batches, without the need for real-time responses. You can submit data in large volumes, and SageMaker processes the data and returns the results once the job completes.

A. Batch transform ✅ Explanation: Batch Transform is ideal for processing large datasets in bulk when immediate responses are not needed. It supports multiple GB-sized datasets and can handle inference without requiring an endpoint to be always active. Since the company is working with archived data and does not need real-time predictions, batch processing is the most efficient and cost-effective choice.

asynchronous inference is the most appropriate choice for the company's specific needs, as it provides a balance between processing large datasets and not requiring immediate results.

Batch transform is specifically designed to handle large volumes of data, including datasets that are multiple GBs in size. This aligns perfectly with the company's requirement to perform inference on large datasets.

Info on Batch Transform matches up with the details of 'large datsets' and 'don't need projections immediately. https://docs.aws.amazon.com/sagemaker/latest/dg/batch-transform.html