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Professional Cloud DevOps Engineer
Adding capacity
An e-commerce platform hosted on Google Cloud experiences pe…

Professional Cloud DevOps Engineer

Professional Cloud DevOps Engineer

Advanced

Prepare and test your skills

Description
Sample Question
Free Trial
Practice Test

Advanced

Prepare and test your skills

Description
Sample Question
Free Trial
Practice Test

Multi Region Capacity Overflow Failover Architecture

Section: Adding capacity

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Related questions

Keep the momentum going with these hand-picked practice scenarios

AdvancedProfessional Cloud DevOps Engineer

An e-commerce platform hosted on Google Kubernetes Engine (GKE) experiences an unprecedented traffic spike during a flash sale. The primary regional cluster reaches its maximum physical node quota and compute limits, causing critical user checkout requests to degrade alongside non-critical recommendation and browsing requests. You need to implement a traffic shedding and load balancing strategy using Google Cloud native networking to shed non-essential traffic, prioritize critical transaction journeys, and automatically overflow excess global traffic to healthy secondary clusters once service capacity limits are exceeded. What should you do?

Adding capacity

AdvancedProfessional Cloud DevOps Engineer

Your company runs an asynchronous batch-processing workload on a Compute Engine Managed Instance Group (MIG). The worker instances consume compute-heavy image rendering tasks from a Cloud Pub/Sub subscription. During unexpected marketing campaigns, massive influxes of messages arrive in the subscription. The current CPU-based autoscaler reacts too slowly to these sudden surges, resulting in high message processing delays. Furthermore, you need to ensure that the autoscaler does not prematurely scale in instances while tasks are still in progress. How should you configure the autoscaling policy for the Managed Instance Group to rapidly absorb these traffic surges?

Adding capacity

AdvancedProfessional Cloud DevOps Engineer

A production microservices application running on Google Kubernetes Engine (GKE) is experiencing severe tail latency during peak checkout periods. A DevOps engineer investigates the issue across Google Cloud Observability tools and observes the following telemetry: Cloud Monitoring: The GKE nodes and pods report low average CPU utilization (under 25%) and normal memory usage, but p99 latency alerts are firing consistently. Cloud Trace: Distributed request traces reveal that the checkout service spends over 3.2 seconds waiting on a specific internal span inside the payment-auth service, while downstream database calls within that span complete in less than 15 milliseconds. Cloud Profiler: The payment-auth service exhibits very low CPU time, but shows extremely high Wall time and Contention concentrated on a shared synchronization mutex routine in the application runtime. Which root cause and diagnostic conclusion do these correlated telemetry signals indicate?

Application performance monitoring

AdvancedProfessional Cloud DevOps Engineer

An enterprise runs a suite of steady-state microservices on Google Compute Engine virtual machines (n2-standard-8 machine types with 8 vCPUs and 32 GB memory each) across a single region. The workloads run continuously 24/7 with predictable demand. Cloud Monitoring telemetry shows that across all instances, actual average CPU utilization remains steady at 25% to 30%, while memory utilization remains consistently high at approximately 24 GB. The infrastructure team wants to eliminate resource over-provisioning and secure the highest possible continuous cost reductions over a 3-year commitment period. Which combination of actions should the DevOps engineer implement to optimize workload efficiency and ongoing costs?

Optimizing individual workload costs (e.g., GKE, Cloud Run, Compute Engine)

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An enterprise running high-throughput microservices on Google Kubernetes Engine (GKE) and Cloud Bigtable is experiencing high observability costs due to massive Cloud Logging volumes. The DevOps team must optimize logging costs according to FinOps best practices while satisfying the following architectural requirements: Prevent high-volume DATA_READ and DATA_WRITE audit logs generated by trusted backend service accounts from creating cost overhead. Eliminate storage costs for routine GKE health check (/healthz) and informational debug logs while preserving critical error and security logs in Cloud Logging for 30-day operational triage. Retain long-term compliance audit logs for 7 years at minimum cost without paying extended Cloud Logging bucket retention fees. Which logging architecture strategy should the DevOps team implement?

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Security, data masking, Identity and Access Management (IAM), and Cloud Data Loss Prevention (Cloud DLP)

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Last updated September 11, 2026

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50 questions in the full practice test

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Professional Cloud DevOps Engineer

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

Other sections in this exam

Bootstrapping and maintaining a Google Cloud organization
Building and implementing CI/CD pipelines, including continuous testing, for application, infrastructure, and machine learning workloads
Applying site reliability engineering practices
Implementing observability practices and troubleshooting issues
Optimizing performance and cost

Ask Scout

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

An e-commerce platform hosted on Google Cloud experiences periodic flash-sale traffic surges that can exhaust Compute Engine regional vCPU quotas and cause capacity constraints in the primary region (us-central1).

The Site Reliability Engineering (SRE) team needs to implement an automated multi-region capacity scaling and rapid failover strategy. The solution must:

Ensure client requests automatically overflow to a secondary region (us-east4) as soon as the primary region reaches backend capacity or quota limits.
Avoid client-side DNS caching delays during localized saturation incidents.
Maintain high availability and automated traffic distribution based on real-time backend capacity.

Which architectural strategy should the SRE team implement?

Consolidate workloads into a single large regional GKE cluster spanning all zones in us-central1 and configure Horizontal Pod Autoscaling (HPA).

Deploy backends across multiple regions using Network Service Tiers Premium Tier with a global external load balancer and configured backend capacity limits.

Deploy backends in each region using Network Service Tiers Standard Tier with Cloud DNS geolocation routing policies and health checks.

Configure Cloud Monitoring to trigger a Cloud Function that executes manual capacity draining on us-central1 and updates Cloud DNS records.

Consolidate workloads into a single large regional GKE cluster spanning all zones in us-central1 and configure Horizontal Pod Autoscaling (HPA).

Deploy backends across multiple regions using Network Service Tiers Premium Tier with a global external load balancer and configured backend capacity limits.

What Network Service Tiers Premium Tier with Global Load Balancing is

Network Service Tiers Premium Tier routes global traffic over Google's high-performance, low-latency private backbone network using a single global anycast IP address. When combined with an external Application Load Balancer and multi-region backend services, Google Cloud monitors backend utilization in real time.

How it meets the requirements

Automatic overflow on capacity exhaustion: SREs configure maximum backend capacities (such as max CPU utilization or max request rate per instance/endpoint). When backends in us-central1 hit capacity limits or cannot autoscale further due to regional quota exhaustion, the global load balancer automatically spills over excess requests to the nearest healthy region (us-east4) with available capacity.
Eliminating DNS caching delays: Because a single global anycast VIP serves all traffic, failover and capacity overflow occur at the network layer without modifying DNS records or waiting for client Time to Live (TTL) caches to expire.
High availability and real-time distribution: Health checks continuously monitor backend health. If a zone or region suffers severe degradation, traffic shifts dynamically to remaining operational backends.

Key advantages

Automated overflow handling: Prevents dropped requests when localized quotas are reached.
Sub-second failover routing: Eliminates DNS propagation latency during critical incidents.
Capacity-aware load balancing: Distributes load based on actual backend capability.
Optimized user latency: Delivers requests via the closest Google Cloud edge Point of Presence (PoP).

Why this is optimal

This approach provides true active-active multi-region resilience. It seamlessly absorbs regional capacity constraints without administrative intervention or complex client-side failover logic.

Deploy backends in each region using Network Service Tiers Standard Tier with Cloud DNS geolocation routing policies and health checks.

Configure Cloud Monitoring to trigger a Cloud Function that executes manual capacity draining on us-central1 and updates Cloud DNS records.