[Reclamação] The correct answer is Azure Batch, which is not listed among your options.

For processing large amounts of data in parallel across many machines, Azure Batch is the most fitting choice. It's a service specifically built for this purpose, designed to run large-scale parallel and high-performance computing (HPC) applications efficiently in the cloud .

Azure Batch excels by managing a pool of compute nodes (virtual machines) for you. It handles the entire lifecycle, from creating and managing the nodes and installing applications to scheduling jobs and tasks on them . This makes it an ideal platform for "embarrassingly parallel" workloads, where tasks run independently and can be scaled out massively to process data faster . Examples include financial risk modeling, image analysis, media transcoding, and genetic sequence analysis .

Here’s a breakdown of why the other options are less suitable:

Azure Data Lake Analytics was a strong candidate, but it was retired on February 29, 2024 and is no longer an available service . Microsoft recommends migrating to Azure Synapse Analytics or Microsoft Fabric as alternatives .

Azure DevOps Pipelines is a tool for CI/CD (Continuous Integration and Continuous Delivery) and is not designed to manage or execute large-scale data processing tasks on many virtual machines.

Azure Functions can handle parallel execution, but it is better suited for event-driven, short-lived, and granular tasks. It is not designed for orchestrating large, complex batch jobs that run on many machines for extended periods.

Azure Scale Sets provides the infrastructure (VMs) but not the job scheduling, application deployment, and lifecycle management that Batch offers. You would still need to build the management logic yourself. Azure Batch does this for you automatically .