An e-commerce company runs an order processing application on Google Kubernetes Engine (GKE). The application experiences sudden, sharp traffic surges during flash sales and low activity overnight. The engineering team has identified the following requirements: The application must automatically scale the number of Pod replicas out and in based on incoming traffic volume to preserve responsiveness. Container CPU and memory resource requests must be continually right-sized over time to prevent over-allocation and optimize costs. The underlying VM node capacity must scale dynamically without manual intervention whenever new Pods cannot be scheduled due to insufficient compute resources. Autoscaling components must not compete or create scaling race conditions with one another. Which autoscaling architecture should you implement to satisfy these requirements?

Configure the Horizontal Pod Autoscaler (HPA) targeting custom traffic metrics, deploy the Vertical Pod Autoscaler (VPA) in recommendation mode to right-size Pod requests, and enable Cluster Autoscaler on the GKE node pools.: What this architecture is This architecture combines Horizontal Pod Autoscaler (HPA) for rapid workload horizontal scaling, Vertical Pod Autoscaler (VPA) in advisory mode for baseline resource right-sizing, and Cluster Autoscaler (CA) for dynamic node infrastructure provisioning. How it meets the requirements Responsive Workload Scaling: HPA monitors traffic-based metrics (such as request rate) and automatically scales the number of Pod replicas up during spikes and down during quiet hours. Continuous Resource Right-Sizing: Running VPA in Off (recommendation) mode analyzes historical CPU and memory utilization to provide optimal resource requests without restarting Pods dynamically or conflicting with HPA. Dynamic Node Provisioning: Cluster Autoscaler monitors pending Pods that cannot be scheduled due to resource constraints and provisions new GKE worker nodes on demand, while deprovisioning underutilized nodes. Conflict Prevention: Because HPA scales horizontally on traffic metrics while VPA operates in recommendation mode rather than actively mutating running Pod CPU/memory, race conditions and autoscaling loops between HPA and VPA are completely avoided. Key advantages and capabilities Multi-tiered autoscaling: Seamless coordination between application layer elasticity and infrastructure layer scaling. Cost efficiency: Prevents paying for idle VM instances overnight and eliminates over-provisioned container resource allocations. Stability: Prevents autoscaler thrashing and ensures predictable scale-up behavior during flash sales. Why this is optimal This approach aligns with Google Cloud and Kubernetes best practices for multi-dimensional autoscaling. It ensures rapid horizontal elasticity under load while keeping infrastructure right-sized and preventing autoscaler metric collisions.

Gke Automated Scaling Hpa Vpa Cluster Autoscaler

IntermediateProfessional Cloud Architect

An enterprise is planning to migrate a fleet of standard web services and background processing microservices to Google Kubernetes Engine (GKE). The platform architecture must satisfy the following technical and operational criteria: Provide high availability for the Kubernetes API control plane during maintenance and single-zone infrastructure disruptions Eliminate the operational overhead of configuring node pools, OS patching, scaling parameters, and bin-packing Optimize cloud spend by billing compute based on container resource requests rather than paying for unused worker node VM capacity Which GKE cluster configuration should you recommend?

Container orchestration

IntermediateProfessional Cloud Architect

An enterprise runs a multi-tier analytics application on Google Kubernetes Engine (GKE). A backend workload running in the data-processing namespace requires access to Cloud Storage and Cloud Bigtable. The security team mandates the following requirements: The backend application must authenticate to Google Cloud APIs following the principle of least privilege, without managing or storing long-lived service account keys in the cluster. Pods running on the same node pool from other namespaces must not inherit the backend workload's Google Cloud API permissions. Network traffic must be restricted at Layer 4 so that backend Pods only allow authorized cluster communication and can reach the required Google Cloud endpoints. Which architectural approach should you implement to satisfy these requirements?

Container orchestration

IntermediateProfessional Cloud Architect

An enterprise machine learning team is implementing automated training pipelines on Google Cloud using managed Vertex AI Custom Training (TrainingPipeline). To protect intellectual property, prevent model weight exfiltration, and enforce the principle of least privilege, the security architect establishes the following requirements: The training job must pull proprietary training container images from Artifact Registry. The training job must stream execution and runtime metrics to Cloud Logging. The training job must write exported model weights and artifacts to a designated Cloud Storage bucket (gs://company-model-registry/output/) without having permission to read, overwrite, or delete existing model files. ML engineers submitting the pipeline must not have direct permissions to read or modify model output files in Cloud Storage, but must be able to launch the job under the approved execution identity. Which identity and access management (IAM) configuration fulfills these requirements?

Determining security requirements for IaaS-hosted and PaaS-hosted training models.

Professional Cloud Architect

Gke Automated Scaling Hpa Vpa Cluster Autoscaler

Cloud Monitoring Metric Threshold Alerting Condition

BigQuery Table Clones for Test Data Isolation

SLIs, SLOs, and Error Budgets

Professional Cloud Architect

Gke Automated Scaling Hpa Vpa Cluster Autoscaler

What this architecture is

How it meets the requirements

Key advantages and capabilities

Why this is optimal

Cloud Monitoring Metric Threshold Alerting Condition

BigQuery Table Clones for Test Data Isolation

SLIs, SLOs, and Error Budgets

What this architecture is

How it meets the requirements

Key advantages and capabilities

Why this is optimal

Professional Cloud Architect

Gke Automated Scaling Hpa Vpa Cluster Autoscaler

Related questions

Cloud Monitoring Metric Threshold Alerting Condition

BigQuery Table Clones for Test Data Isolation

SLIs, SLOs, and Error Budgets

Professional Cloud Architect

Gke Automated Scaling Hpa Vpa Cluster Autoscaler

What this architecture is

How it meets the requirements

Key advantages and capabilities

Why this is optimal

Related questions

Cloud Monitoring Metric Threshold Alerting Condition

BigQuery Table Clones for Test Data Isolation

SLIs, SLOs, and Error Budgets

What this architecture is

How it meets the requirements

Key advantages and capabilities

Why this is optimal