Google Professional Data Engineer

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You are managing two BigQuery projects with distinct requirements: one for production jobs with strict SLAs needing high priority and consistent access to 300 slots (spiking to 800), and another for ad-hoc analytical queries preferring billing based on data scanned, typically using up to 200 slots. How should you allocate compute resources to meet both projects' needs?

Real Exam

Explanation:

The correct approach is to create two separate reservations tailored to each project's needs. For the production jobs with strict SLAs, an Enterprise Edition reservation with a baseline of 300 slots and autoscaling up to 500 slots ensures consistent resource availability. For the ad-hoc queries, on-demand billing aligns with the preference to pay based on data scanned, not slot capacity. This setup optimally addresses the distinct requirements of each project.

Option A is incorrect because setting a baseline of 0 slots for the ad-hoc project may lead to resource contention and delays.
Option B fails to address the ad-hoc project's billing preference and lacks resource isolation.
Option D misallocates resources by setting an unnecessarily high baseline for the SLA project and limiting the ad-hoc project's scalability.