Compare compute models in public clouds (VMs, containers, serverless) and explain when you would choose each on GCP versus AWS. Describe strategies for testing cloud services (unit, integration, load, chaos) in these environments. Deep-dive into Kubernetes: explain the control plane versus worker nodes, list core components on each, and walk through kubectl commands to deploy, scale, roll back, and debug a service. Discuss data security for a multi-tenant cluster (RBAC, network policies, secrets management, encryption in transit/at rest). Finally, design a CI/CD pipeline that builds, tests, scans, and deploys containerized services to GKE with rollbacks and canary releases.

This question evaluates a candidate's competency in cloud compute model selection, testing strategies, Kubernetes architecture and operational commands, multi-tenant data security, and CI/CD pipeline design including rollbacks and canary releases.

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Compare cloud compute; design Kubernetes security and CI/CD

Cloud Compute Models, Testing Strategies, Kubernetes Deep Dive, Security, and CI/CD to GKE

Context

You are designing and operating services across public clouds. Compare compute models (VMs, containers, serverless) and explain when to choose each on GCP vs AWS. Then discuss testing strategies in cloud environments, deep-dive into Kubernetes (architecture and kubectl workflow), data security for a multi-tenant cluster, and design a CI/CD pipeline that deploys to GKE with rollbacks and canary releases.

Tasks

Compare compute models in public clouds:
- Virtual machines vs containers vs serverless.
- When to choose each on GCP vs AWS. Map to key services.
Testing strategies for cloud services:
- Unit, integration, load/performance, and chaos testing.
- How approaches differ for VMs, containers/Kubernetes, and serverless.
Kubernetes deep dive:
- Control plane vs worker nodes; core components on each.
- Walk through kubectl commands to deploy, scale, roll back, and debug a service.
Data security in a multi-tenant cluster:
- RBAC, network policies, secrets management, encryption in transit and at rest.
CI/CD pipeline for GKE:
- Build, test, scan, and deploy containerized services to GKE.
- Include rollbacks and canary releases.

Context

Tasks

Compare compute models in public clouds:

Virtual machines vs containers vs serverless.
When to choose each on GCP vs AWS. Map to key services.

Testing strategies for cloud services:

Unit, integration, load/performance, and chaos testing.
How approaches differ for VMs, containers/Kubernetes, and serverless.

Kubernetes deep dive:

Control plane vs worker nodes; core components on each.
Walk through kubectl commands to deploy, scale, roll back, and debug a service.

Data security in a multi-tenant cluster:

RBAC, network policies, secrets management, encryption in transit and at rest.

CI/CD pipeline for GKE:

Build, test, scan, and deploy containerized services to GKE.
Include rollbacks and canary releases.

Compare cloud compute; design Kubernetes security and CI/CD

Quick Overview

Cloud Compute Models, Testing Strategies, Kubernetes Deep Dive, Security, and CI/CD to GKE

Context

Tasks

Solution

Comments (0)

Compare cloud compute; design Kubernetes security and CI/CD

Quick Overview

Cloud Compute Models, Testing Strategies, Kubernetes Deep Dive, Security, and CI/CD to GKE

Context

Tasks

Solution

Comments (0)