Question
A team performs interactive experimentation during development. The team also runs scalable jobs for model training.
The team must minimize costs while ensuring compute resources scale when needed. Different workloads require different compute behaviors within the same workspace.
You need to configure compute targets that support each workload.
Which compute targets should you use? To answer, move the appropriate compute targets to the correct workload types. You may use each compute target once, more than once, or not at all.
NOTE: Each correct selection is worth one point.
Explanation
Interactive experimentation: an Azure Machine Learning compute instance is a managed single-node development workstation for notebooks and the terminal, and idle shutdown keeps its cost down. Scalable training jobs: an Azure Machine Learning compute cluster is a managed single- or multinode cluster that autoscales each time a job is submitted, and with a minimum of 0 nodes it costs nothing while idle. Azure Batch, Databricks, AKS and a standalone VM are unmanaged or attached targets that add management overhead and are not the native fit for these workloads.
