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?

Deploy Workload 1 to Cloud Run and deploy Workload 2 to Google Kubernetes Engine (GKE) Standard.: What the Recommended Architecture Is This architecture pairs Cloud Run—a fully managed serverless container execution environment—with Google Kubernetes Engine (GKE) Standard, a managed Kubernetes platform providing deep infrastructure control. How It Meets Workload Requirements Workload 1 (Serverless & Zero Maintenance): Cloud Run abstracts all underlying server, node, and cluster operations. It automatically manages scaling up under load and scaling down to zero when idle, minimizing both operational overhead and idle compute costs for lightweight HTTP services. Workload 2 (Infrastructure Control & Custom Configuration): GKE Standard gives administrators granular control over node pool configurations, allowing custom OS images (such as Ubuntu or Container-Optimized OS), specialized machine families, and custom VM sizing. Inside the cluster, administrators can enforce multi-tenant isolation using Kubernetes Network Policies to restrict pod-to-pod communication and Kubernetes RBAC (Roles and RoleBindings) to enforce fine-grained permissions across namespaces. Key Advantages and Capabilities Operational Efficiency: Offloads infrastructure provisioning and management to Google Cloud for stateless, spiky services via Cloud Run. Granular Customization: Allows custom node pool configuration, daemonsets, and underlying VM parameter tuning in GKE Standard. Advanced Multi-Tenancy: Provides native Kubernetes primitives such as namespaces, resource quotas, and network policies to securely isolate tenant workloads. Optimized Total Cost of Ownership (TCO): Avoids paying for idle compute on spiky workloads while reserving dedicated compute capacity where deep control is required. Why This Is Optimal Using Cloud Run for simple HTTP services removes cluster maintenance overhead (such as control plane version management and node pool sizing), while deploying GKE Standard for the complex pipeline provides the required host-level flexibility and internal Kubernetes access control mechanisms that serverless abstractions do not support.

Evaluating Gke Standard Vs Cloud Run Operational Control

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 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?

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

IntermediateProfessional Cloud Architect

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?

Serverless computing

IntermediateProfessional Cloud Architect

GCVE Route Summarization and Filtering

Cloud-native network configuration for compute resources (e.g., Compute Engine, GKE, serverless networking, and Google Cloud VMware Engine)

IntermediateProfessional Cloud Architect

An e-commerce organization hosts a latency-sensitive public web service on Google Cloud using App Engine standard environment with automatic scaling. The service currently scales down to zero instances during inactive overnight periods to save costs. However, during sudden morning traffic spikes, users experience latency degradation caused by instance cold starts and request queuing before new instances can spin up. You need to optimize the autoscaling behavior to satisfy the following requirements: Eliminate cold-start latency for initial incoming morning requests Proactively scale out new instances based on concurrent connections before current instances reach full capacity Retain automatic scaling capabilities during high-traffic periods Which configuration should you specify in the application's automatic scaling configuration?

Serverless computing

AdvancedProfessional Cloud Data Engineer

A data engineering team operates a large Apache Spark batch workload on a Google Cloud Dataproc cluster, with input and output datasets stored directly in Cloud Storage buckets. The workload experiences unpredictable processing spikes throughout the day, interspersed with low-utilization periods. During review of recent cluster operations and billing metrics, the team identifies several performance and cost issues: Minor, short-lived fluctuations in resource demand trigger frequent scaling thrashing (unnecessary scale-up and scale-down cycles). Scaling down primary worker instances causes HDFS block movement overhead and delays worker removal. In-progress Spark tasks are abruptly cancelled during node deprovisioning, causing job retries and increased execution costs. Which Dataproc autoscaling policy configuration resolves these issues while minimizing infrastructure costs?

Minimizing costs per required business need for data

Professional Cloud Architect

Evaluating Gke Standard Vs Cloud Run Operational Control

Related questions

GCVE Route Summarization and Filtering

Professional Cloud Architect

Evaluating Gke Standard Vs Cloud Run Operational Control

What the Recommended Architecture Is

How It Meets Workload Requirements

Key Advantages and Capabilities

Why This Is Optimal

Related questions

GCVE Route Summarization and Filtering

What the Recommended Architecture Is

How It Meets Workload Requirements

Key Advantages and Capabilities

Why This Is Optimal