Designing and Planning a Cloud Solution Architecture

Business Requirements

Every architecture decision on the PCA exam traces back to business requirements. The exam presents case studies where you must identify constraints before jumping to technical solutions. Key dimensions include:

Technical Requirements

Technical requirements define how the system must behave. On the exam, you will translate these into specific GCP service selections and configurations.

The Five Pillars

The Google Cloud Well-Architected Framework provides design principles for building cloud-native systems. The exam heavily references these pillars when evaluating architecture decisions.

Architecture Reviews

Google recommends periodic architecture reviews against the Well-Architected Framework. On the exam, you should recognize when a proposed architecture violates a pillar and suggest the correct remediation.

GKE vs Cloud Run vs App Engine vs Compute Engine

Compute selection is one of the most-tested topics on the PCA exam. Each service has a sweet spot determined by workload type, team expertise, and operational requirements.

Compute Engine Patterns

When VMs are the right choice, key architecture patterns include:

• Managed Instance Groups (MIGs) — autoscaling, autohealing, rolling updates. Use regional MIGs for high availability across zones.
• Sole-Tenant Nodes — physical server isolation for compliance (HIPAA, PCI). Dedicated hardware, no sharing with other tenants.
• Spot/Preemptible VMs — up to 91% cheaper. Use for fault-tolerant batch, data processing, CI/CD runners. Not for production serving.
• Custom Machine Types — right-size vCPU and memory independently. Cost optimization when standard types over-provision.

# Create a regional MIG with autoscaling
gcloud compute instance-templates create web-template \
    --machine-type=e2-medium \
    --image-family=debian-12 \
    --image-project=debian-cloud \
    --tags=http-server \
    --metadata=startup-script='#!/bin/bash
apt-get update && apt-get install -y nginx
systemctl start nginx'

gcloud compute instance-groups managed create web-mig \
    --template=web-template \
    --size=2 \
    --region=us-central1 \
    --target-distribution-shape=EVEN

gcloud compute instance-groups managed set-autoscaling web-mig \
    --region=us-central1 \
    --min-num-replicas=2 \
    --max-num-replicas=10 \
    --target-cpu-utilization=0.6 \
    --cool-down-period=90

Storage Decision Framework

Storage selection depends on data model (structured/unstructured), access pattern (OLTP/OLAP/streaming), consistency requirements, and scale.

Database Selection by Access Pattern

# Cloud Storage lifecycle policy (cost optimization)
gsutil lifecycle set lifecycle.json gs://my-bucket

# lifecycle.json — transition to Nearline after 30 days, Coldline after 90
{
  "rule": [
    {
      "action": {"type": "SetStorageClass", "storageClass": "NEARLINE"},
      "condition": {"age": 30, "matchesStorageClass": ["STANDARD"]}
    },
    {
      "action": {"type": "SetStorageClass", "storageClass": "COLDLINE"},
      "condition": {"age": 90, "matchesStorageClass": ["NEARLINE"]}
    },
    {
      "action": {"type": "Delete"},
      "condition": {"age": 365}
    }
  ]
}

VPC Architecture Patterns

Network design on GCP centers around VPC networks, which are global resources containing regional subnets. The exam tests your understanding of connectivity models, especially for multi-project and hybrid environments.

Hybrid Connectivity Decision

# Create HA VPN gateway
gcloud compute vpn-gateways create my-ha-vpn \
    --network=my-vpc \
    --region=us-central1

# Create external VPN gateway (on-prem peer)
gcloud compute external-vpn-gateways create on-prem-gw \
    --interfaces 0=203.0.113.1,1=203.0.113.2

# Create Cloud Router for dynamic routing
gcloud compute routers create my-router \
    --network=my-vpc \
    --region=us-central1 \
    --asn=65001

Google Cloud Migration Phases

Google recommends a four-phase migration framework. The exam tests your ability to identify which phase activities belong to and recommend appropriate tools.

1. Assess — Inventory existing workloads, evaluate TCO, identify dependencies, assess cloud readiness. Tools: Migration Center (formerly StratoZone), Application Discovery.
2. Plan — Define migration strategy per workload, establish landing zone (org hierarchy, networking, IAM), set up foundation infrastructure. Tools: Cloud Foundation Toolkit, Terraform.
3. Deploy — Execute migration, validate functionality, run parallel operations. Tools: Migrate to VMs, Migrate to Containers, Database Migration Service.
4. Optimize — Right-size resources, implement monitoring, automate operations, modernize applications. Tools: Recommender, Active Assist, Cloud Monitoring.

The 6 Rs of Migration

# Terraform landing zone — project factory pattern
module "project-factory" {
  source  = "terraform-google-modules/project-factory/google"
  version = "~> 15.0"

  name                 = "my-migration-project"
  org_id               = "123456789"
  folder_id            = "folders/456789012"
  billing_account      = "01ABCD-234567-EFGH89"

  activate_apis = [
    "compute.googleapis.com",
    "container.googleapis.com",
    "sqladmin.googleapis.com",
    "monitoring.googleapis.com",
  ]

  shared_vpc         = "host-project-id"
  shared_vpc_subnets = [
    "projects/host-project-id/regions/us-central1/subnetworks/shared-subnet",
  ]
}

Gemini and Agent Builder

The PCA exam now includes AI/ML solution design. As a Cloud Architect, you need to recommend the right AI approach based on the use case, not train models yourself.

Model Garden

Vertex AI Model Garden provides a catalog of Google models (Gemini, Imagen, Chirp), open-source models (Llama, Mistral, Gemma), and partner models (Claude, Cohere). As an architect, you choose based on task, latency, cost, and data governance requirements.

# Deploy a Gemini model endpoint via gcloud
gcloud ai endpoints create \
    --display-name=gemini-endpoint \
    --region=us-central1

# Create an Agent Builder data store
gcloud discovery-engine data-stores create my-datastore \
    --location=global \
    --collection=default_collection \
    --type=CONTENT

# Import documents to the data store
gcloud discovery-engine documents import \
    --data-store=my-datastore \
    --location=global \
    --collection=default_collection \
    --gcs-uri=gs://my-bucket/docs/