An enterprise is modernizing its application infrastructure and needs to select the appropriate serverless compute deployment models for two distinct backend services: Service A: A lightweight event processor that needs to trigger automatically upon new file uploads in Cloud Storage, process metadata in Python, and finish in under 30 seconds. The development team prefers to deploy source code directly without creating or managing container build configurations. Service B: A document-processing API written in C++ that relies on custom operating system packages and system-level shared libraries. The application is already built and packaged as an Open Container Initiative (OCI)-compliant container image and handles HTTP requests from internal client applications. Which serverless deployment architecture should the cloud architect recommend?

Deploy Service A as an event-driven Cloud Function with a Cloud Storage trigger, and deploy Service B as a containerized Cloud Run service.: What the Recommended Architecture Is This architecture pairs Cloud Functions (Cloud Run functions) for source-deployed, event-driven compute with Cloud Run for running arbitrary containerized web applications and microservices. How It Meets the Requirements Service A (Cloud Functions): Cloud Functions provides a source-centric deployment experience where developers deploy raw Python code directly without writing Dockerfiles or managing container registries. It integrates natively with Cloud Storage through Eventarc triggers, reacting instantly to object creation events with executions completing well within the platform's time limits. Service B (Cloud Run): Cloud Run is purpose-built to execute arbitrary container images listening on an HTTP port. It supports any programming language, including C++, and allows custom operating system libraries bundled within an OCI-compliant container image, making it ideal for existing containerized workflows. Key Advantages and Capabilities Source-to-Cloud deployment: Cloud Functions automatically builds and deploys code using underlying buildpacks, minimizing maintenance overhead for lightweight event handlers. Universal language and runtime portability: Cloud Run supports any binary, language, or system dependency packaged in a container container image. Automatic horizontal scaling: Both services scale automatically down to zero when idle and scale up rapidly to handle concurrent spikes. Granular triggers: Eventarc natively filters and delivers Cloud Storage mutation events directly to function instances. Why This Is Optimal This architecture leverages the specific strengths of both serverless products: source-based event simplicity for Service A and arbitrary runtime container execution for Service B.

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Serverless Compute Selection Cloud Run Cloud Functions

Section: Serverless computing

An enterprise is modernizing its application infrastructure and needs to select the appropriate serverless compute deployment models for two distinct backend services:

Service A: A lightweight event processor that needs to trigger automatically upon new file uploads in Cloud Storage, process metadata in Python, and finish in under 30 seconds. The development team prefers to deploy source code directly without creating or managing container build configurations.
Service B: A document-processing API written in C++ that relies on custom operating system packages and system-level shared libraries. The application is already built and packaged as an Open Container Initiative (OCI)-compliant container image and handles HTTP requests from internal client applications.

Which serverless deployment architecture should the cloud architect recommend?

Deploy both Service A and Service B to Cloud Functions using standard managed runtimes.

Deploy Service A as an event-driven Cloud Function with a Cloud Storage trigger, and deploy Service B as a containerized Cloud Run service.

Deploy Service A to Compute Engine with managed instance groups, and deploy Service B to App Engine standard environment.

Deploy Service A as a scheduled Cloud Run Job, and deploy Service B as an HTTP Cloud Function using a Python wrapper.

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Serverless Compute Selection Cloud Run Cloud Functions

Section: Serverless computing

An enterprise is modernizing its application infrastructure and needs to select the appropriate serverless compute deployment models for two distinct backend services:

Service A: A lightweight event processor that needs to trigger automatically upon new file uploads in Cloud Storage, process metadata in Python, and finish in under 30 seconds. The development team prefers to deploy source code directly without creating or managing container build configurations.
Service B: A document-processing API written in C++ that relies on custom operating system packages and system-level shared libraries. The application is already built and packaged as an Open Container Initiative (OCI)-compliant container image and handles HTTP requests from internal client applications.

Which serverless deployment architecture should the cloud architect recommend?

Deploy both Service A and Service B to Cloud Functions using standard managed runtimes.

Deploy Service A as an event-driven Cloud Function with a Cloud Storage trigger, and deploy Service B as a containerized Cloud Run service.

Deploy Service A to Compute Engine with managed instance groups, and deploy Service B to App Engine standard environment.

Deploy Service A as a scheduled Cloud Run Job, and deploy Service B as an HTTP Cloud Function using a Python wrapper.

Explanations

What the Recommended Architecture Is

This architecture pairs Cloud Functions (Cloud Run functions) for source-deployed, event-driven compute with Cloud Run for running arbitrary containerized web applications and microservices.

How It Meets the Requirements

Service A (Cloud Functions): Cloud Functions provides a source-centric deployment experience where developers deploy raw Python code directly without writing Dockerfiles or managing container registries. It integrates natively with Cloud Storage through Eventarc triggers, reacting instantly to object creation events with executions completing well within the platform's time limits.
Service B (Cloud Run): Cloud Run is purpose-built to execute arbitrary container images listening on an HTTP port. It supports any programming language, including C++, and allows custom operating system libraries bundled within an OCI-compliant container image, making it ideal for existing containerized workflows.

Key Advantages and Capabilities

Source-to-Cloud deployment: Cloud Functions automatically builds and deploys code using underlying buildpacks, minimizing maintenance overhead for lightweight event handlers.
Universal language and runtime portability: Cloud Run supports any binary, language, or system dependency packaged in a container container image.
Automatic horizontal scaling: Both services scale automatically down to zero when idle and scale up rapidly to handle concurrent spikes.
Granular triggers: Eventarc natively filters and delivers Cloud Storage mutation events directly to function instances.

Why This Is Optimal

This architecture leverages the specific strengths of both serverless products: source-based event simplicity for Service A and arbitrary runtime container execution for Service B.

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Professional Cloud Architect

Serverless Compute Selection Cloud Run Cloud Functions

Professional Cloud Architect

Serverless Compute Selection Cloud Run Cloud Functions

What the Recommended Architecture Is

How It Meets the Requirements

Key Advantages and Capabilities

Why This Is Optimal

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