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.

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Compute Services Workload Archetype Mapping

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

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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)

Workload 1: Compute Engine Managed Instance Groups (MIGs); Workload 2: Cloud Run functions; Workload 3: Cloud Run

Workload 1: Cloud Run functions; Workload 2: Google Kubernetes Engine (GKE); Workload 3: Cloud Run

Workload 1: Google Kubernetes Engine (GKE); Workload 2: Cloud Run; Workload 3: Compute Engine Managed Instance Groups (MIGs)

Professional Cloud Architect

Compute Services Workload Archetype Mapping

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