An enterprise is optimizing compute costs for three distinct workloads hosted on Google Cloud: Workload 1: A core relational database running 24/7 on dedicated N2 Compute Engine virtual machines in us-central1 with predictable, steady-state CPU and memory utilization. Workload 2: A dynamic microservices architecture deployed across Compute Engine, Google Kubernetes Engine (GKE), and Cloud Run across multiple regions, with a predictable aggregate baseline hourly spend. Workload 3: A nightly high-throughput batch processing job that is stateless, fault-tolerant, and capable of resuming after sudden node terminations. Which combination of Google Cloud pricing models and discount strategies should you recommend to minimize costs?

Apply resource-based Committed Use Discounts (CUDs) for Workload 1, purchase Compute flexible spend-based CUDs for Workload 2, and provision Spot VMs for Workload 3.: What the Recommended Pricing Strategy Is This multi-tiered pricing architecture aligns specific Google Cloud commitment models and execution tiers to the distinct predictability, geographical distribution, and fault-tolerance characteristics of each workload. How It Meets Each Requirement Workload 1 (Predictable Regional DB): Resource-based Committed Use Discounts (CUDs) provide deep discounts (typically up to 55% for general-purpose series and up to 70% for memory-optimized machines) in exchange for committing to a specific quantity of vCPUs and memory within a specific region (us-central1) and machine series (N2). Workload 2 (Multi-Service Microservices Fleet): Compute flexible spend-based CUDs are purchased at the Cloud Billing account level and automatically apply across Compute Engine, Google Kubernetes Engine (GKE), and Cloud Run regardless of region or machine family, making them ideal for predictable aggregate spend across heterogeneous runtimes. Workload 3 (Fault-Tolerant Batch Jobs): Spot VMs offer steep discounts (60–91% compared to standard on-demand rates) for flexible, fault-tolerant batch workloads that can handle VM preemption without impacting business SLAs. Key Advantages and Capabilities Deepest Hardware Discounts: Maximizes savings on steady 24/7 database instances by fixing commitments to specific regional hardware resources. Cross-Service Flexibility: Avoids lock-in to specific machine types or single compute services for containerized microservices spanning Compute Engine, GKE, and Cloud Run. Aggressive Cost Reductions for Batch: Reclaims spare Google Cloud capacity for stateless batch jobs at minimal cost without long-term commitment overhead. Automated Credit Application: Google Cloud applies resource-based CUDs first to eligible usage before utilizing flexible spend-based CUDs. Why This Is Optimal This architecture pairs the highest discount tier available for fixed, long-running regional workloads (resource-based CUDs) with broad elasticity for distributed architectures (flexible CUDs) and hyper-discounted ephemeral capacity (Spot VMs).

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Matching Google Cloud Discounts to Workload Patterns

Section: Cost optimization

...

Explanations

...

View source documentation

Keep the momentum going with these hand-picked practice scenarios

IntermediateProfessional Cloud Architect

A global financial technology enterprise is modernizing its core payment processing system on Google Cloud. The system requires an operational database architecture that satisfies the following technical requirements: Provide strict ACID transactional consistency across distributed tables globally Support standard relational schemas and ANSI SQL query dialect Offer horizontal scaling for both reads and writes across multiple geographic regions Deliver a high-availability Service Level Agreement (SLA) of up to 99.999% (five nines) Which database service should the enterprise select?

Choosing appropriate storage types (e.g., object, file, and databases)

AdvancedProfessional Cloud Security Engineer

An enterprise uses Microsoft Entra ID as its authoritative identity provider (IdP) and federates authentication to Google Cloud using SAML Single Sign-On (SSO). The organization is designing an automated user lifecycle management architecture to synchronize employee provisioning, security group assignments for IAM access, and deprovisioning workflows upon employee departure. The security team establishes the following design requirements: The dedicated account used by Microsoft Entra ID for automated provisioning in Cloud Identity must adhere to the principle of least privilege and avoid full super-admin permissions. When an employee departs, terminating their identity in Microsoft Entra ID must automatically and reliably revoke all Google Cloud access, including SSH access to Compute Engine virtual machines utilizing OS Login. Which solution should the enterprise implement?

Automating the user lifecycle management process.

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Last updated August 28, 2026

Professional Cloud Architect

Intrigued by the art of cloud architecture? Discover how to design, develop, and manage robust, secure, scalable, and dynamic solutions on Google Cloud as you prepare for the Professional Cloud Architect exam!

Intermediate

Prepare and test your skills

Intermediate

Prepare and test your skills

Matching Google Cloud Discounts to Workload Patterns

Section: Cost optimization

An enterprise is optimizing compute costs for three distinct workloads hosted on Google Cloud:

Workload 1: A core relational database running 24/7 on dedicated N2 Compute Engine virtual machines in us-central1 with predictable, steady-state CPU and memory utilization.
Workload 2: A dynamic microservices architecture deployed across Compute Engine, Google Kubernetes Engine (GKE), and Cloud Run across multiple regions, with a predictable aggregate baseline hourly spend.
Workload 3: A nightly high-throughput batch processing job that is stateless, fault-tolerant, and capable of resuming after sudden node terminations.

Which combination of Google Cloud pricing models and discount strategies should you recommend to minimize costs?

...

Rely entirely on automated Sustained Use Discounts (SUDs) across all workloads and attach regional reservations to Workload 3.

...

Apply resource-based Committed Use Discounts (CUDs) for Workload 1, purchase Compute flexible spend-based CUDs for Workload 2, and provision Spot VMs for Workload 3.

...

Apply spend-based CUDs for Workload 1, configure resource-based CUDs across multiple regions for Workload 2, and rely on standard on-demand VMs with Sustained Use Discounts for Workload 3.

...

Purchase Compute flexible spend-based CUDs for Workload 3, apply resource-based CUDs for Workload 1, and run all instances in Workload 2 as Spot VMs across GKE and Cloud Run.

...

Explanations

What the Recommended Pricing Strategy Is

This multi-tiered pricing architecture aligns specific Google Cloud commitment models and execution tiers to the distinct predictability, geographical distribution, and fault-tolerance characteristics of each workload.

How It Meets Each Requirement

Workload 1 (Predictable Regional DB): Resource-based Committed Use Discounts (CUDs) provide deep discounts (typically up to 55% for general-purpose series and up to 70% for memory-optimized machines) in exchange for committing to a specific quantity of vCPUs and memory within a specific region (us-central1) and machine series (N2).
Workload 2 (Multi-Service Microservices Fleet): Compute flexible spend-based CUDs are purchased at the Cloud Billing account level and automatically apply across Compute Engine, Google Kubernetes Engine (GKE), and Cloud Run regardless of region or machine family, making them ideal for predictable aggregate spend across heterogeneous runtimes.
Workload 3 (Fault-Tolerant Batch Jobs): Spot VMs offer steep discounts (60–91% compared to standard on-demand rates) for flexible, fault-tolerant batch workloads that can handle VM preemption without impacting business SLAs.

Key Advantages and Capabilities

Deepest Hardware Discounts: Maximizes savings on steady 24/7 database instances by fixing commitments to specific regional hardware resources.
Cross-Service Flexibility: Avoids lock-in to specific machine types or single compute services for containerized microservices spanning Compute Engine, GKE, and Cloud Run.
Aggressive Cost Reductions for Batch: Reclaims spare Google Cloud capacity for stateless batch jobs at minimal cost without long-term commitment overhead.
Automated Credit Application: Google Cloud applies resource-based CUDs first to eligible usage before utilizing flexible spend-based CUDs.

Why This Is Optimal

This architecture pairs the highest discount tier available for fixed, long-running regional workloads (resource-based CUDs) with broad elasticity for distributed architectures (flexible CUDs) and hyper-discounted ephemeral capacity (Spot VMs).

...

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IntermediateProfessional Cloud Architect

A global financial technology enterprise is modernizing its core payment processing system on Google Cloud. The system requires an operational database architecture that satisfies the following technical requirements: Provide strict ACID transactional consistency across distributed tables globally Support standard relational schemas and ANSI SQL query dialect Offer horizontal scaling for both reads and writes across multiple geographic regions Deliver a high-availability Service Level Agreement (SLA) of up to 99.999% (five nines) Which database service should the enterprise select?

Choosing appropriate storage types (e.g., object, file, and databases)

AdvancedProfessional Cloud Security Engineer

An enterprise uses Microsoft Entra ID as its authoritative identity provider (IdP) and federates authentication to Google Cloud using SAML Single Sign-On (SSO). The organization is designing an automated user lifecycle management architecture to synchronize employee provisioning, security group assignments for IAM access, and deprovisioning workflows upon employee departure. The security team establishes the following design requirements: The dedicated account used by Microsoft Entra ID for automated provisioning in Cloud Identity must adhere to the principle of least privilege and avoid full super-admin permissions. When an employee departs, terminating their identity in Microsoft Entra ID must automatically and reliably revoke all Google Cloud access, including SSH access to Compute Engine virtual machines utilizing OS Login. Which solution should the enterprise implement?

Automating the user lifecycle management process.

Want more questions like this?

Get a free certification question every week.

Last updated August 28, 2026

An enterprise is optimizing compute costs for three distinct workloads hosted on Google Cloud:

Workload 1: A core relational database running 24/7 on dedicated N2 Compute Engine virtual machines in us-central1 with predictable, steady-state CPU and memory utilization.
Workload 2: A dynamic microservices architecture deployed across Compute Engine, Google Kubernetes Engine (GKE), and Cloud Run across multiple regions, with a predictable aggregate baseline hourly spend.
Workload 3: A nightly high-throughput batch processing job that is stateless, fault-tolerant, and capable of resuming after sudden node terminations.

Which combination of Google Cloud pricing models and discount strategies should you recommend to minimize costs?

Rely entirely on automated Sustained Use Discounts (SUDs) across all workloads and attach regional reservations to Workload 3.

Apply resource-based Committed Use Discounts (CUDs) for Workload 1, purchase Compute flexible spend-based CUDs for Workload 2, and provision Spot VMs for Workload 3.

Apply spend-based CUDs for Workload 1, configure resource-based CUDs across multiple regions for Workload 2, and rely on standard on-demand VMs with Sustained Use Discounts for Workload 3.

Purchase Compute flexible spend-based CUDs for Workload 3, apply resource-based CUDs for Workload 1, and run all instances in Workload 2 as Spot VMs across GKE and Cloud Run.

Rely entirely on automated Sustained Use Discounts (SUDs) across all workloads and attach regional reservations to Workload 3.

Apply resource-based Committed Use Discounts (CUDs) for Workload 1, purchase Compute flexible spend-based CUDs for Workload 2, and provision Spot VMs for Workload 3.

What the Recommended Pricing Strategy Is

This multi-tiered pricing architecture aligns specific Google Cloud commitment models and execution tiers to the distinct predictability, geographical distribution, and fault-tolerance characteristics of each workload.

How It Meets Each Requirement

Workload 1 (Predictable Regional DB): Resource-based Committed Use Discounts (CUDs) provide deep discounts (typically up to 55% for general-purpose series and up to 70% for memory-optimized machines) in exchange for committing to a specific quantity of vCPUs and memory within a specific region (us-central1) and machine series (N2).
Workload 2 (Multi-Service Microservices Fleet): Compute flexible spend-based CUDs are purchased at the Cloud Billing account level and automatically apply across Compute Engine, Google Kubernetes Engine (GKE), and Cloud Run regardless of region or machine family, making them ideal for predictable aggregate spend across heterogeneous runtimes.
Workload 3 (Fault-Tolerant Batch Jobs): Spot VMs offer steep discounts (60–91% compared to standard on-demand rates) for flexible, fault-tolerant batch workloads that can handle VM preemption without impacting business SLAs.

Key Advantages and Capabilities

Deepest Hardware Discounts: Maximizes savings on steady 24/7 database instances by fixing commitments to specific regional hardware resources.
Cross-Service Flexibility: Avoids lock-in to specific machine types or single compute services for containerized microservices spanning Compute Engine, GKE, and Cloud Run.
Aggressive Cost Reductions for Batch: Reclaims spare Google Cloud capacity for stateless batch jobs at minimal cost without long-term commitment overhead.
Automated Credit Application: Google Cloud applies resource-based CUDs first to eligible usage before utilizing flexible spend-based CUDs.

Why This Is Optimal

This architecture pairs the highest discount tier available for fixed, long-running regional workloads (resource-based CUDs) with broad elasticity for distributed architectures (flexible CUDs) and hyper-discounted ephemeral capacity (Spot VMs).

Apply spend-based CUDs for Workload 1, configure resource-based CUDs across multiple regions for Workload 2, and rely on standard on-demand VMs with Sustained Use Discounts for Workload 3.

Purchase Compute flexible spend-based CUDs for Workload 3, apply resource-based CUDs for Workload 1, and run all instances in Workload 2 as Spot VMs across GKE and Cloud Run.

Professional Cloud Architect

Matching Google Cloud Discounts to Workload Patterns

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Why This Is Optimal

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