An enterprise is migrating multiple backend workloads to Google Cloud and must map each workload to the optimal compute service based on operational overhead, scaling characteristics, and architectural dependencies: Workload 1: A containerized stateless web API that receives fluctuating HTTP(S) request traffic, requires rapid automatic scaling (including scaling to zero when idle), and must minimize cluster management overhead. Workload 2: A lightweight, single-purpose event handler that executes short-lived business logic asynchronously whenever new files are uploaded to a Cloud Storage bucket. Workload 3: A complex distributed application that requires deployment via Helm, interacts directly with Kubernetes Custom Resource Definitions (CRDs), and enforces fine-grained multi-tenant isolation using Kubernetes Role-Based Access Control (RBAC). Which compute mapping should you recommend?

Workload 1: Cloud Run; Workload 2: Cloud Run functions; Workload 3: Google Kubernetes Engine (GKE): What the Recommended Services Are This architecture correctly maps each application archetype to its optimal Google Cloud runtime: Cloud Run for managed containerized request serving, Cloud Run functions for lightweight event-driven executions, and Google Kubernetes Engine (GKE) for complex container orchestration. How It Meets Each Requirement Workload 1 (Stateless Web API): Cloud Run provides a fully managed, serverless platform that automatically scales container instances up or down to zero based on incoming HTTP(S) request traffic. It abstracts cluster management and infrastructure provisioning, satisfying the requirement for minimal operational overhead. Workload 2 (File Processing Trigger): Cloud Run functions (formerly Cloud Functions) offer an event-driven serverless compute model ideal for executing lightweight code in response to asynchronous Cloud Storage events (such as object creation) without running a persistent web service. Workload 3 (Complex Multi-Tenant Application): Google Kubernetes Engine (GKE) natively supports Kubernetes APIs, Helm packaging, Custom Resource Definitions (CRDs), and native Kubernetes Role-Based Access Control (RBAC) for fine-grained multi-tenancy. Key Advantages and Capabilities Serverless Request Autoscaling: Cloud Run manages concurrency, load balancing, and rapid scale-to-zero without manual node provisioning. Seamless Event-Driven Invocations: Cloud Run functions automatically handle event consumption from Cloud Storage and Eventarc without dedicated polling infrastructure. Extensible Container Orchestration: GKE delivers comprehensive support for open-source Kubernetes tooling, custom controllers, and advanced scheduling primitives. Optimized Operational Balance: Balances fully managed operational simplicity for standard web and event workloads with deep infrastructure configurability where Kubernetes APIs are strictly required. Why This Is Optimal Choosing Cloud Run and Cloud Run functions eliminates cluster maintenance and operational overhead for standard HTTP endpoints and event handlers, while reserving GKE for workloads that have explicit dependencies on Kubernetes ecosystem APIs and RBAC multi-tenancy.

Compute Services Workload Archetype Mapping | Professional Cloud Architect

IntermediateProfessional Cloud Architect

A company has developed a custom containerized API packaged as a standard OCI container image. The workload exhibits highly sporadic and unpredictable traffic patterns throughout the day, often experiencing several consecutive hours of zero incoming traffic. The architecture team has established the following operational and economic requirements: Scale-to-zero: Eliminate all compute infrastructure costs during periods of inactivity. Cold start mitigation: Reduce latency during initial container startup when scaling up from zero. Resource cost optimization: Pay for CPU resources only during active request processing rather than continuously. Container portability: Run the unmodified container image without rewriting application code into vendor-specific function wrappers. Which architecture should the cloud architect recommend?

Mapping compute needs to platform products (e.g., Google Kubernetes Engine [GKE], Cloud Run, and Cloud Run functions)

IntermediateProfessional Cloud Architect

An organization is evaluating Google Cloud compute options for two distinct application workloads: Workload 1: A lightweight, event-driven HTTP service with spiky traffic patterns that requires zero server maintenance, automated scaling to zero, and minimal operational complexity. Workload 2: An enterprise multi-tenant processing engine that requires fine-grained pod-level network policy isolation, custom node OS configurations, and in-cluster role-based access control (RBAC). Which compute platform design meets these requirements while optimizing administrative overhead and infrastructure control?

Mapping compute needs to platform products (e.g., Google Kubernetes Engine [GKE], Cloud Run, and Cloud Run functions)

IntermediateProfessional Cloud Architect

Google Cloud Emulators

Cloud Emulators (e.g., Bigtable, Spanner, Pub/Sub, and Firestore)

IntermediateProfessional Cloud Architect

A site reliability engineering (SRE) team is investigating an intermittent outage impacting a business-critical application hosted on Google Cloud. While reviewing system errors in Cloud Logging Logs Explorer, the team needs to quickly identify the underlying root cause across complex log entries, configurations, and metric anomalies, and obtain remediation recommendations to restore high availability. Which action should the team take using Gemini Cloud Assist?

Gemini Cloud Assist

IntermediateProfessional Cloud Architect

OKR-Driven Phased Cloud Modernization

Stakeholder management (e.g., influencing and facilitation)

AdvancedProfessional Cloud Data Engineer

A financial data enterprise runs an OLTP database on Cloud SQL Enterprise Plus with high availability (HA) enabled in its primary region. To meet regulatory compliance, the architecture requires cross-region disaster recovery (DR) capabilities with minimal impact on production write latency during normal operations, routine zero-data-loss DR drill testing, and automated routing via a DNS write endpoint. Which replication and failover topology should the data engineering team configure?

Data replication and failover (e.g., Cloud SQL, Redis clusters)

Professional Cloud Architect

Compute Services Workload Archetype Mapping

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