An enterprise organization operates multiple microservice workloads across Google Kubernetes Engine (GKE) and Compute Engine. The cloud operations team needs to centralize operational observability while optimizing logging ingestion costs. They want to filter out non-essential debug logs from storage while ensuring high-severity application errors and security audit logs are reliably preserved for incident investigation. Which log routing design best meets these observability and reliability requirements?
- Configure an aggregated Log Router sink at the organization level with explicit inclusion filters targeting severity levels of ERROR and above, while applying fine-grained exclusion filters for low-severity debug logs before exporting to a centralized BigQuery dataset.Cevap
- BConfigure an organization-level log exclusion filter that matches severity level ERROR and above to prevent high-volume error logs from consuming storage quotas during operational outages.
- CApply a broad organization-level log exclusion filter across all projects and grant primitive Owner roles to operation engineers so they can manually disable filters during active incidents.
- DSet up centralized log export sinks and rely exclusively on standard IAM roles to control log access, omitting VPC Service Controls around the central log storage destination.
Cevap
Configure an aggregated Log Router sink at the organization level with explicit inclusion filters targeting severity levels of ERROR and above, while applying fine-grained exclusion filters for low-severity debug logs before exporting to a centralized BigQuery dataset.
Configuring an aggregated Log Router sink with targeted inclusion filters for ERROR severity logs and explicit exclusions for debug logs ensures that critical telemetry is centrally preserved for analysis while optimizing overall storage costs.
Adım Adım Çözüm
Anahtar Kavram
Monitoring, Logging, and Observability Integration