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Your enterprise runs mission-critical hourly data transformation pipelines and executive dashboards in a dedicated Production folder, while data scientists run unpredictable, heavy ad-hoc queries in an Analytics folder. During peak hours, ad-hoc queries consume excessive compute resources, causing slot contention and delayed execution for production pipelines.
You need to implement a capacity-based workload management architecture in BigQuery that:
Which configuration should you implement?
Create two separate regional reservations in the administration project: prod and analytics. Configure both reservations with job_type = 'PIPELINE', enable idle slot sharing on both, and assign individual BigQuery dataset IDs to their respective reservations.
Create a dedicated administration project. In this project, configure two regional Enterprise edition reservations: a prod reservation configured with baseline slots, an autoscaling maximum slot size, and the ignore_idle_slots setting set to true, and an analytics reservation configured with autoscaling slots. Assign the Production folder to the prod reservation and the Analytics folder to the analytics reservation with job_type = 'QUERY'.
Create a single shared-prod reservation in an administration project with baseline slots and autoscaling enabled. Assign the entire Google Cloud organization to this reservation with job_type = 'QUERY', and set up project-level custom daily bytes scanned quotas on every project inside the Analytics folder.
Leave the Production folder unassigned so that it defaults to the shared on-demand slot pool with a 2,000-slot cap. Create an Enterprise edition reservation for the Analytics folder with baseline slots and configure ignore_idle_slots = true to restrict analytics to its own dedicated pool.
Create two separate regional reservations in the administration project: prod and analytics. Configure both reservations with job_type = 'PIPELINE', enable idle slot sharing on both, and assign individual BigQuery dataset IDs to their respective reservations.
Create a dedicated administration project. In this project, configure two regional Enterprise edition reservations: a prod reservation configured with baseline slots, an autoscaling maximum slot size, and the ignore_idle_slots setting set to true, and an analytics reservation configured with autoscaling slots. Assign the Production folder to the prod reservation and the Analytics folder to the analytics reservation with job_type = 'QUERY'.
BigQuery reservations provide dedicated compute capacity measured in slots (virtual CPUs) to execute SQL queries, data manipulation, and pipeline transformations. Within capacity-based billing (available in BigQuery editions such as Enterprise), administrators can structure compute capacity into isolated pools and allocate them across projects, folders, or organizations.
StudyCerts
StudyCertsignore_idle_slots: Setting ignore_idle_slots = true (or enabling the Ignore idle slots toggle) disables idle slot sharing. This ensures that unused baseline capacity in the prod reservation is never loaned out to other reservations, eliminating any risk of slot preemption or delayed reclaiming when critical production jobs start.analytics reservation with autoscaling slots and an upper bound max reservation size allows data science queries to scale dynamically without ever competing for or exhausting production resources.Production and Analytics) using job_type = 'QUERY' leverages resource hierarchy inheritance, ensuring all current and future projects within those folders automatically route their query jobs to the designated reservation pool without manual per-project administration.Using edition-based reservations with baseline slots, autoscaling limits, idle slot isolation, and folder-level assignments is the architectural best practice for enterprise multi-tenant BigQuery environments requiring strict SLA guarantees.
Create a single shared-prod reservation in an administration project with baseline slots and autoscaling enabled. Assign the entire Google Cloud organization to this reservation with job_type = 'QUERY', and set up project-level custom daily bytes scanned quotas on every project inside the Analytics folder.
Leave the Production folder unassigned so that it defaults to the shared on-demand slot pool with a 2,000-slot cap. Create an Enterprise edition reservation for the Analytics folder with baseline slots and configure ignore_idle_slots = true to restrict analytics to its own dedicated pool.