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Professional Cloud Data Engineer
Minimizing costs per required business need for data
Your organization runs three distinct BigQuery workloads: Da…

Professional Cloud Data Engineer

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Bigquery Slot Commitments Reservations Workload Management

Section: Minimizing costs per required business need for data

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Minimizing costs per required business need for data

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Last updated August 3, 2026

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Professional Cloud Data Engineer

Provider: Google Cloud
Passing score: 70%
Questions: 50
Duration: 90 minutes

Other sections in this exam

Designing data processing systems
Ingesting and processing the data
Storing the data
Preparing and using data for analysis
Maintaining and automating data workloads

Ask Scout

Still curious? Scout, our AI tutor, can explain this concept further and answer your follow-up questions.

Your organization runs three distinct BigQuery workloads: Data Science, ELT, and Business Intelligence (BI). Currently, all queries use the default on-demand pricing model, resulting in highly variable and unpredictable monthly costs. You need to implement a cost-optimized processing architecture that provides predictable pricing while guaranteeing dedicated compute capacity for each workload. Additionally, to maximize resource utilization, any idle compute capacity from one workload must be automatically available to the others.

What should you do?

Configure all workloads to use batch queries instead of interactive queries to utilize the BigQuery shared resource pool at a lower cost.

Purchase BigQuery slot commitments and assign all projects to a single, unified reservation to ensure all workloads share the same compute capacity.

Configure BigQuery custom quotas for each project to limit the maximum bytes billed per day. Continue using the on-demand pricing model.

Purchase BigQuery slot commitments. Create separate reservations for the Data Science, ELT, and BI workloads, and assign the respective projects to these reservations.

Configure all workloads to use batch queries instead of interactive queries to utilize the BigQuery shared resource pool at a lower cost.

Purchase BigQuery slot commitments and assign all projects to a single, unified reservation to ensure all workloads share the same compute capacity.

Configure BigQuery custom quotas for each project to limit the maximum bytes billed per day. Continue using the on-demand pricing model.

Purchase BigQuery slot commitments. Create separate reservations for the Data Science, ELT, and BI workloads, and assign the respective projects to these reservations.

What BigQuery slot commitments and reservations are

BigQuery slot commitments allow organizations to transition from on-demand pricing to capacity-based pricing by purchasing dedicated compute resources (slots). Reservations are allocations of these purchased slots that can be assigned to specific projects, folders, or workloads.

How it meets the requirements

Predictable pricing: Purchasing slot commitments (flex, monthly, or yearly) provides a fixed, predictable cost compared to the variable on-demand model.
Dedicated compute capacity: Creating separate reservations for Data Science, ELT, and BI ensures that each workload has a guaranteed baseline of compute resources.
Maximize resource utilization: By default, BigQuery is designed so that queries running in one reservation automatically utilize idle slots from other reservations, ensuring no compute capacity is wasted.

Key advantages

Eliminates the risk of sudden cost spikes caused by unexpected query volumes.
Prevents resource starvation by isolating workloads into their own reservations.
Provides flexibility to adjust slot allocations as workload demands change over time.
Offers significant cost savings for consistent, high-volume workloads through long-term commitments.

Why this is optimal

This architecture perfectly balances performance and cost. It provides the financial predictability of flat-rate pricing while leveraging BigQuery's intelligent workload management to dynamically share idle resources, ensuring optimal performance for all three distinct workloads.