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PCD
PCD Google Cloud

Professional Cloud Developer

The course teaches developers how to design, build, test, deploy, and manage highly scalable, reliable, and cloud‑native applications on Google Cloud, covering all five exam domains.

120
Minutes
50
Questions
70/100
Passing Score
$200
Exam Cost
2
Languages

Who Should Take This

It is intended for software engineers, solution architects, or DevOps professionals with at least three years of industry experience and a minimum of one year building and operating solutions on Google Cloud. These candidates aim to validate their expertise and advance to senior cloud‑development roles.

What's Covered

1 Designing microservices architectures, API-driven services, and application patterns that meet scalability, availability, and reliability requirements on GCP.
2 Setting up local development environments, building applications using Cloud Build and Artifact Registry, and implementing testing strategies including unit, integration, and load testing.
3 Implementing deployment strategies such as blue-green, canary, and rolling deployments using Cloud Deploy, GKE, and Cloud Run.
4 Integrating applications with data services, messaging services like Pub/Sub, and compute services; implementing service-to-service authentication and authorization.
5 Monitoring application performance with Cloud Monitoring and Cloud Trace; debugging issues with Cloud Debugger and Error Reporting; optimizing resource utilization.

Exam Structure

Question Types

  • Multiple Choice
  • Multiple Select

Scoring Method

Pass/fail. Google does not publish a scaled score or passing percentage.

Delivery Method

Kryterion testing center or online proctored

Prerequisites

None required. Associate Cloud Engineer recommended.

Recertification

3 years

What's Included in AccelaStudy® AI

Adaptive Knowledge Graph
Practice Questions
Lesson Modules
Console Simulator Labs
Exam Tips & Strategy
20 Activity Formats

Course Outline

71 learning goals
1 Domain 1: Designing Highly Scalable, Available, and Reliable Cloud-Native Applications
4 topics

Design high-performance applications

  • Implement microservices architecture patterns including service decomposition, bounded contexts, inter-service communication via synchronous REST/gRPC and asynchronous Pub/Sub messaging, and API versioning strategies on Google Cloud.
  • Implement API design and management using Cloud Endpoints for OpenAPI-based API hosting and Apigee for enterprise API gateway capabilities including rate limiting, quota enforcement, and developer portal provisioning.
  • Implement twelve-factor application principles including configuration externalization, stateless process design, port binding, disposability, and dev/prod parity for cloud-native workloads on Google Cloud compute services.
  • Analyze application architecture tradeoffs between monolithic, microservices, and serverless patterns and select the optimal decomposition strategy based on team structure, latency requirements, and operational complexity.
  • Design enterprise API governance strategies that standardize API lifecycle management, versioning policies, deprecation workflows, and consumer onboarding across multiple teams using Apigee and Cloud Endpoints.

Design secure applications

  • Implement IAM policies with least-privilege access using predefined roles, custom roles, service accounts, and workload identity federation for application authentication and authorization on Google Cloud.
  • Implement service account security patterns including key rotation, short-lived credentials via workload identity, impersonation chains, and scope restriction for inter-service authentication.
  • Implement Secret Manager for application credential lifecycle management including automatic rotation, versioned secrets, IAM-controlled access, and integration with Cloud Run and GKE workloads.
  • Implement Identity-Aware Proxy for context-aware access control including BeyondCorp zero-trust patterns, OAuth/OIDC-based authentication flows, and programmatic access with signed headers.
  • Analyze application security posture and evaluate tradeoffs between authentication mechanisms, secret management approaches, and identity federation patterns to select optimal security architecture for given compliance requirements.

Manage application data

  • Implement relational data storage using Cloud SQL with high availability configurations, read replicas, automated backups, and connection pooling for transactional application workloads.
  • Implement globally distributed relational storage using Cloud Spanner with schema design for horizontal scaling, interleaved tables, secondary indexes, and multi-region instance configurations.
  • Implement NoSQL data storage using Firestore with document model design, collection group queries, composite indexes, real-time listeners, and offline persistence for mobile and web applications.
  • Implement wide-column data storage using Bigtable with row key design, column family optimization, performance tuning, and replication for high-throughput time-series and analytical workloads.
  • Implement in-memory caching using Memorystore for Redis and Memcached with session management, cache invalidation strategies, and connection configuration for latency-sensitive application tiers.
  • Analyze data storage tradeoffs across Cloud SQL, Spanner, Firestore, Bigtable, and Memorystore and select the optimal data service based on consistency requirements, query patterns, scalability needs, and cost constraints.

Re-architect applications for the cloud

  • Implement the strangler fig pattern for incremental migration of monolithic applications to microservices using API facades, traffic routing, and gradual service extraction on Google Cloud.
  • Implement event-driven architectures using Pub/Sub, Eventarc, and Cloud Functions for asynchronous processing, event sourcing patterns, and decoupled service communication.
  • Implement CQRS and eventual consistency patterns for distributed data management including separate read/write models, event-driven synchronization, and saga orchestration using Workflows.
  • Design cloud migration strategies that evaluate lift-and-shift, re-platform, and re-architect approaches based on application complexity, team capabilities, timeline constraints, and long-term operational objectives.
2 Domain 2: Building and Testing Applications
3 topics

Set up the development environment

  • Implement development workflows using Cloud Shell and Cloud Code IDE extensions with project configuration, service account authentication, and local preview capabilities for rapid prototyping.
  • Implement local development environments using Google Cloud emulators for Pub/Sub, Firestore, Bigtable, and Spanner to enable offline testing and rapid iteration without cloud resource provisioning.
  • Analyze development environment tradeoffs between Cloud Shell, Cloud Code, local emulators, and direct cloud resource usage and select optimal configurations for team productivity and cost efficiency.

Build cloud-native applications

  • Implement containerized applications on Cloud Run with service configuration, concurrency settings, CPU allocation modes, VPC connectors, and revision-based traffic management for serverless container workloads.
  • Implement container orchestration on GKE with Deployment, Service, and Ingress resources, horizontal pod autoscaling, node pool management, and workload identity for Kubernetes-native applications.
  • Implement serverless applications using Cloud Functions with event triggers from Pub/Sub, Cloud Storage, Firestore, and HTTP endpoints including cold start optimization and function composition patterns.
  • Implement applications on App Engine with standard and flexible environments, service splitting, version management, and automatic scaling configurations for web application workloads.
  • Implement multi-step orchestration workflows using Workflows with YAML/JSON definitions, conditional branching, parallel execution, error handling, and integration with Cloud Run and Cloud Functions.
  • Analyze compute platform tradeoffs across Cloud Run, GKE, App Engine, and Cloud Functions and select the optimal platform based on workload characteristics, scaling requirements, operational overhead, and cost constraints.

Test applications

  • Implement unit testing strategies for cloud-native applications using mocking frameworks for Google Cloud client libraries, dependency injection patterns, and isolated test configurations.
  • Implement integration testing using Google Cloud emulators and Cloud Build test steps with service dependency simulation, test data management, and environment-specific configuration for end-to-end validation.
  • Implement load testing and performance benchmarking for Google Cloud applications using distributed load generation, latency percentile analysis, and capacity planning to validate scaling behavior under production traffic patterns.
  • Design comprehensive testing strategies that integrate unit, integration, and load testing into CI/CD pipelines with quality gates, test coverage analysis, and progressive confidence building across deployment stages.
3 Domain 3: Deploying Applications
2 topics

Adopt appropriate deployment strategies

  • Implement rolling deployment strategies on GKE using Deployment rollout configurations, maxSurge and maxUnavailable settings, readiness probes, and rollback procedures for progressive workload updates.
  • Implement blue-green and canary deployment strategies using Cloud Run traffic splitting, GKE service mesh traffic management, and Cloud Deploy delivery pipeline configurations for controlled release progression.
  • Implement CI/CD pipelines using Cloud Build with build triggers, cloudbuild.yaml definitions, multi-step builds, container image builds, and integration with Artifact Registry for automated build and delivery.
  • Analyze deployment strategy tradeoffs across rolling, blue-green, canary, and all-at-once models and select the optimal approach based on risk tolerance, rollback speed, resource overhead, and service continuity requirements.

Deploy applications and services to Google Cloud

  • Implement container image management using Artifact Registry with repository policies, vulnerability scanning, image lifecycle rules, and cross-project access for secure artifact storage and distribution.
  • Implement continuous delivery pipelines using Cloud Deploy with delivery pipeline definitions, target configurations, approval workflows, and automated promotion across staging and production environments.
  • Implement GKE deployment patterns including Deployment, StatefulSet, DaemonSet, and Job resources with health checks, resource limits, pod disruption budgets, and namespace-based environment isolation.
  • Implement Cloud Run service deployments with revision management, minimum instance configuration, startup and liveness probes, execution environment selection, and secrets integration for production workloads.
  • Design enterprise delivery pipeline strategies that standardize build, test, scan, and deploy stages across multiple teams with governance controls, environment promotion gates, and rollback procedures.
4 Domain 4: Integrating Google Cloud Services
4 topics

Integrate with data and storage services

  • Implement Cloud Storage integration with signed URLs, object lifecycle management, event notifications via Pub/Sub, storage class selection, and client library usage for application-level object storage operations.
  • Implement BigQuery integration for analytical workloads including streaming inserts, batch loading, federated queries, materialized views, and application-level query execution via client libraries.
  • Implement Firestore and Datastore integration with transaction management, batch operations, query optimization, consistency models, and real-time listener patterns for application-level document storage.
  • Analyze data service integration patterns and evaluate tradeoffs between synchronous and asynchronous data access, caching strategies, and consistency guarantees for multi-service application architectures.

Integrate with compute services

  • Implement service-to-service communication patterns using service discovery, internal load balancing, VPC networking, and Cloud Run service URLs for reliable inter-service connectivity.
  • Implement service mesh capabilities using Anthos Service Mesh and Istio with traffic management, mutual TLS, circuit breaking, retry policies, and observability features for microservice communication governance.
  • Analyze compute service integration architectures and evaluate service mesh overhead, direct connectivity tradeoffs, and hybrid connectivity patterns to optimize for latency, reliability, and operational complexity.

Integrate with messaging services

  • Implement Pub/Sub integration with topic and subscription management, message ordering, dead-letter topics, exactly-once delivery, push and pull subscriptions, and message filtering for event-driven application architectures.
  • Implement Cloud Tasks for distributed task execution with queue configuration, rate limiting, retry policies, task deduplication, and HTTP/App Engine target routing for reliable asynchronous work dispatch.
  • Implement Cloud Scheduler for managed cron job execution with schedule definitions, retry configurations, target types including Pub/Sub, HTTP, and App Engine, and pause/resume lifecycle management.
  • Implement Eventarc for event-driven architectures with event routing from Google Cloud sources, third-party providers, and custom applications to Cloud Run, Cloud Functions, and Workflows targets.
  • Analyze messaging service tradeoffs across Pub/Sub, Cloud Tasks, Cloud Scheduler, and Eventarc and select the optimal service based on delivery guarantees, ordering requirements, latency sensitivity, and integration complexity.

Integrate with operations services

  • Implement Cloud Monitoring integration with custom metrics, uptime checks, alerting policies, notification channels, and dashboard configuration for application health visibility.
  • Implement Cloud Logging integration with structured logging, log-based metrics, log sinks to BigQuery and Cloud Storage, and log exclusion filters for application-level observability.
  • Implement Cloud Trace integration with distributed trace propagation, span annotations, latency analysis, and trace sampling configuration for request flow visualization across microservice architectures.
  • Implement Error Reporting integration with error grouping, notification configuration, exception tracking, and resolution workflow integration for application error management.
  • Design unified observability strategies that integrate Cloud Monitoring, Cloud Logging, Cloud Trace, and Error Reporting into cohesive application intelligence with SLO-based alerting and proactive anomaly detection.
5 Domain 5: Managing Deployed Applications
3 topics

Manage cloud compute services

  • Implement GKE cluster scaling with horizontal pod autoscaler, vertical pod autoscaler, cluster autoscaler, node auto-provisioning, and pod priority and preemption for dynamic workload capacity management.
  • Implement Cloud Run revision management with traffic splitting between revisions, minimum and maximum instance configuration, CPU throttling policies, and startup CPU boost for serverless workload optimization.
  • Implement App Engine version management with traffic migration strategies, instance class selection, automatic and manual scaling configurations, and warmup request handling for application lifecycle operations.
  • Analyze compute resource utilization patterns and optimize scaling configurations, instance sizing, and scheduling policies to balance performance SLAs against infrastructure cost across Cloud Run, GKE, and App Engine.

Troubleshoot applications

  • Implement application debugging workflows using Cloud Debugger with snapshots and logpoints for production issue investigation without stopping or degrading running application instances.
  • Implement performance analysis workflows using Cloud Trace for latency investigation and Cloud Profiler for CPU, heap, and thread profiling to identify application bottlenecks in production environments.
  • Implement error triage workflows using Error Reporting with error grouping, stack trace analysis, affected user impact assessment, and integration with issue tracking systems for systematic error resolution.
  • Analyze complex production incidents by correlating signals across Cloud Logging, Cloud Trace, Cloud Profiler, and Error Reporting to isolate root causes and identify systemic failure patterns.

Manage application infrastructure

  • Implement resource monitoring using Cloud Monitoring dashboards with resource utilization widgets, SLI/SLO tracking, burn rate alerts, and budget notifications for proactive capacity management.
  • Implement quota management strategies including quota monitoring, increase request workflows, committed use discounts, and resource budgeting for predictable infrastructure cost governance.
  • Analyze application infrastructure costs and optimize resource allocation using rightsizing recommendations, committed use discounts, preemptible/spot instances, and autoscaling configurations for cost-efficient operations.
  • Design comprehensive application lifecycle management strategies that integrate monitoring, scaling, cost optimization, incident response, and continuous improvement into sustainable operational frameworks.

Hands-On Labs

25 labs ~416 min total Console Simulator

Practice in a simulated cloud console or Python code sandbox — no account needed. Each lab runs entirely in your browser.

Certification Benefits

Salary Impact

$145,000
Average Salary

Related Job Roles

Cloud Developer Software Engineer Full-Stack Developer Backend Engineer

Industry Recognition

Google Cloud certifications are highly valued in data-driven and AI-focused organizations. The Professional Cloud Developer credential validates advanced cloud-native development skills on GCP, a platform increasingly adopted for its developer-friendly services and strong Kubernetes ecosystem.

Scope

Included Topics

  • All domains and task statements in the Google Cloud Professional Cloud Developer certification exam guide: Domain 1 Designing highly scalable, available, and reliable cloud-native applications (~26%), Domain 2 Building and testing applications (~20%), Domain 3 Deploying applications (~16%), Domain 4 Integrating Google Cloud services (~18%), and Domain 5 Managing deployed applications (~20%).
  • Professional-level cloud-native application development decisions for microservices architecture, API design, secure application patterns, data service selection, deployment strategies, service integration, and operational management on Google Cloud.
  • Complex scenario-based tradeoff analysis involving application performance, reliability engineering, security posture, deployment risk reduction, service mesh integration, and cost-optimized operations strategies.
  • Key Google Cloud services for cloud developers: Cloud Run, GKE, App Engine, Cloud Functions, Cloud Build, Artifact Registry, Cloud Deploy, Pub/Sub, Cloud Tasks, Cloud Scheduler, Eventarc, Cloud SQL, Spanner, Firestore, Bigtable, Memorystore, BigQuery, Cloud Storage, Cloud Endpoints, Apigee, IAM, Secret Manager, Identity-Aware Proxy, Cloud Monitoring, Cloud Logging, Cloud Trace, Cloud Profiler, Error Reporting, Cloud Debugger, Workflows, Cloud Shell, Cloud Code, Anthos Service Mesh, Istio.

Not Covered

  • Deep infrastructure and network engineering content unrelated to application development outcomes expected by the Professional Cloud Developer exam.
  • Provider-agnostic tooling detail that does not map to Google Cloud native services and integration patterns used in the exam objectives.
  • Data engineering and analytics pipeline design beyond what is required for application-level data service integration.
  • Exact short-lived pricing terms and transient promotional details not suitable for durable technical domain specifications.

Official Exam Page

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Google, Google Cloud, and Google Cloud Platform are trademarks of Google LLC. Google does not endorse this product.

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