Azure Data Scientists Associate Practice Exam

The command that retrieves logs during deployment failure in Azure Machine Learning is associated with the Azure Container Instance (ACI) service, which is why it is the correct choice. The function aci_service.get_logs() allows users to access the logs generated by the Azure Container Instances, providing insights into any issues that occurred during the deployment process.

When there is a failure in deployment, having access to logs is crucial for debugging and diagnosing the underlying problem. The logs can provide valuable information such as error messages, stack traces, and other diagnostic data that can help pinpoint the exact cause of the failure, enabling a more efficient resolution.

In contrast, other options might refer to different functionalities within Azure Machine Learning, but they do not specifically focus on retrieving logs from a deployment context tied to ACI. For instance, ml_service.get_logs() typically relates to getting logs from the machine learning service itself and may not pertain directly to deployment failures. The other choices, logs.get_service() and deployment.get_logs(), do not accurately target the ACI service for deployment-related logging, making them less suitable for this scenario.