Google Cloud Professional Cloud Developer Exam Prep

What's inside

1. Level 1: Exam Orientation and Reading Scenario QuestionsFree
   [Exam orientation – all sections] Learn how the Professional Cloud Developer exam works: it lasts 2 hours, has 50-60 multiple-choice and multiple-select questions and costs $200. The exam guide belongs to Google, is not reproduced here and is free on Google's certification page. This course is independent of Google, is not endorsed by it and cannot promise a pass. The level also shows how the four sections are weighted (~32/23/24/21%) and how to break down scenario 'best answer' questions.
2. Level 2: Choosing Compute Engine, GKE or Cloud RunFree
   [Section 1.1 Designing high-performing applications and APIs] Choose the right platform for a use case by comparing Compute Engine, Google Kubernetes Engine and Cloud Run on control, scaling, operational load and cost, so you can optimize for cost and resource usage.
3. Level 3: Building Containers and Defining Resource Requirements
   [Section 1.1 Designing high-performing applications and APIs] Build and refactor application containers for Cloud Run and GKE (stateless design, listening on $PORT, small images) and define the CPU, memory and concurrency each workload needs.
4. Level 4: Geography, Latency and Failover Replication
   [Section 1.1 Designing high-performing applications and APIs] Learn how Google Cloud services are distributed across global, regional and zonal scopes, how that affects latency, and how data replication supports zonal and regional failover models.
5. Level 5: Load Balancers and Session Affinity
   [Section 1.1 Designing high-performing applications and APIs] Learn the use cases for Google Cloud load balancers (global versus regional, external versus internal, application versus network) and how to enable session affinity for performant content delivery.
6. Level 6: Caching Solutions with Memorystore
   [Section 1.1 Designing high-performing applications and APIs] Put caching solutions in place with Memorystore, using cache-aside, TTLs and invalidation, and connect to it privately from Cloud Run and GKE.
7. Level 7: REST and gRPC APIs with Apigee and API Gateway
   [Section 1.1 Designing high-performing applications and APIs] Create and deploy HTTP REST and gRPC APIs, then add application rate limiting, authentication and observability with Apigee or Cloud API Gateway.
8. Level 8: Asynchronous Integration with Pub/Sub and Eventarc
   [Section 1.1 Designing high-performing applications and APIs] Connect applications asynchronously and through events with Pub/Sub (topics, push and pull subscriptions, dead-letter topics, ordering) and Eventarc (routing events from Google Cloud sources to targets).
9. Level 9: Orchestrating with Workflows, Cloud Tasks, Scheduler
   [Section 1.1 Designing high-performing applications and APIs] Orchestrate application services with Workflows, Eventarc, Cloud Tasks and Cloud Scheduler, and learn which tool fits multi-step processes, rate-controlled dispatch and cron-style jobs.
10. Level 10: Traffic Splitting on Cloud Run and GKE
    [Section 1.1 Designing high-performing applications and APIs] Use traffic splitting strategies on a new service on Cloud Run or GKE for gradual rollouts, rollbacks and A/B testing.
11. Level 11: Data Retention and Organization Policies
    [Section 1.2 Designing secure applications] Put data retention and organization policies in place with Cloud Storage Object Lifecycle Management, retention policies, retention policy lock, object holds and organization policy constraints.
12. Level 12: Finding and Fixing Vulnerabilities
    [Section 1.2 Designing secure applications] Use IAP and Web Security Scanner to find vulnerabilities and protect services and resources, and respond to and resolve vulnerabilities found by Artifact Analysis and Security Command Center.
13. Level 13: Secrets, Encryption Keys and Workload Identity Federation
    [Section 1.2 Designing secure applications] Store, access and rotate application secrets, credentials and encryption keys with Secret Manager, Cloud Key Management Service and Workload Identity Federation.
14. Level 14: Authenticating to Google Cloud Services
    [Section 1.2 Designing secure applications] Authenticate to Google Cloud services using Application Default Credentials, JWT, OAuth 2.0, Cloud SQL Auth Proxy, AlloyDB Auth Proxy, Identity Platform and WIF.
15. Level 15: Least-Privilege Service Accounts and Binary Authorization
    [Section 1.2 Designing secure applications] Secure cloud resources with IAM roles for service accounts, run services with least privileged access, and secure application artifacts with Binary Authorization.
16. Level 16: Secure Service-to-Service Communications
    [Section 1.2 Designing secure applications] Set up secure service-to-service communications with Cloud Service Mesh, Kubernetes Network Policies, Direct VPC egress and private service connectivity.
17. Level 17: Choosing Storage, Schemas and Consistency
    [Section 1.3 Storing and accessing data] Choose a storage system based on data volume and performance requirements, and design schemas for AlloyDB, Spanner, Bigtable and Firestore. Compare eventual and strong consistency across AlloyDB, Bigtable, Cloud SQL, Spanner and Cloud Storage, create signed URLs, and write data to BigQuery for analytics and AI/ML workloads.
18. Level 18: Emulating Google Cloud Services Locally
    [Section 2.1 Setting up your development environment] Use the Google Cloud CLI to emulate Google Cloud services such as Pub/Sub, Firestore, Bigtable, Spanner and Datastore for local application development and local unit testing.
19. Level 19: Console, Cloud SDK, Cloud Shell, Workstations, Code
    [Section 2.1 Setting up your development environment] Use the Google Cloud console, Cloud SDK, Cloud Code, Cloud Shell and Cloud Workstations, and choose the right one for each development task.
20. Level 20: Gemini Cloud Assist, AI Assistants and MCP
    [Section 2.1 Setting up your development environment] Use Gemini Cloud Assist, and configure IDEs with the right integrations, including Cloud SDK, AI coding assistants and MCP servers that give assistants access to project context.
21. Level 21: Cloud Build Fundamentals: Build Configs and Steps
    [Section 2: Building and testing applications — 2.2 Building] Learn how Cloud Build runs a cloudbuild.yaml as a series of container steps. The level covers the shared /workspace, substitutions, the images and artifacts fields, and starting builds with gcloud builds submit, so you can turn source code into a container image without a local Docker install.
22. Level 22: Storing Containers in Artifact Registry
    [Section 2: Building and testing applications — 2.2 Building] Learn how to create and use Artifact Registry repositories to store the containers Cloud Build produces. The level covers repository formats and locations, image paths, tags versus immutable digests, repository-level IAM, and cleanup policies.
23. Level 23: Build Triggers, Build Service Accounts and Pipelines
    [Section 2: Building and testing applications — 2.2 Building] Learn how to automate builds with Cloud Build triggers on repository events. The level covers choosing and scoping the service account a build runs as, and organising a multi-step build pipeline that goes from source to a stored container on every commit.
24. Level 24: Configuring Build Provenance in Cloud Build
    [Section 2: Building and testing applications — 2.2 Building] Learn what build provenance is and how Cloud Build generates SLSA level 3 provenance. The level covers enforcing provenance with options.requestedVerifyOption: VERIFIED and inspecting the provenance attached to an image in Artifact Registry.
25. Level 25: Wiring Provenance into Binary Authorization
    [Section 2: Building and testing applications — 2.2 Building] Connect a Cloud Build pipeline to Binary Authorization in practice. The level covers using build attestations and attestors so that only images built and verified by your pipeline can be deployed to Cloud Run or GKE, building on the security design concepts from Section 1.2.
26. Level 26: Writing Unit Tests with AI Coding Assistants
    [Section 2: Building and testing applications — 2.3 Testing] Learn how to use an AI coding assistant such as Gemini Code Assist to generate, extend and refactor unit tests for cloud application code, and how to review what it produces. The level also covers isolating Google Cloud dependencies with mocks or the local emulators from earlier levels.
27. Level 27: Automated Integration Tests in Cloud Build
    [Section 2: Building and testing applications — 2.3 Testing] Learn how to run automated integration tests as Cloud Build steps. The level covers starting emulators or dependent containers inside the build, running tests against a freshly built image, and failing the pipeline before a bad image is pushed or deployed.
28. Level 28: Section 2 Checkpoint: Build and Test Scenarios
    [Section 2: Building and testing applications — practice and exam craft] Practise exam-style scenario and code-reading questions across all of Section 2: the development environment, emulators, Cloud Build, Artifact Registry, provenance, Binary Authorization and testing. Each wrong option comes with an explanation.
29. Level 29: Deploying to Cloud Run from Source Code
    [Section 3: Configuring cloud-native applications for deployment — 3.1 Cloud Run] Learn how gcloud run deploy --source builds and deploys a service in one command, using a Dockerfile if one is present and Google Cloud's buildpacks if not. The level covers the auto-created cloud-run-source-deploy Artifact Registry repository and deploying Cloud Run functions with --function.
30. Level 30: Invoking Cloud Run with Pub/Sub Push Triggers
    [Section 3: Configuring cloud-native applications for deployment — 3.1 Cloud Run] Learn how to trigger a private Cloud Run service from Pub/Sub using a push subscription. The level covers authenticating pushes with an OIDC token from a service account that holds roles/run.invoker, how HTTP status codes acknowledge messages, and how retries and dead-letter topics work.
31. Level 31: Invoking Cloud Run with Eventarc Triggers
    [Section 3: Configuring cloud-native applications for deployment — 3.1 Cloud Run] Learn how to route events to Cloud Run with Eventarc Standard triggers. The level covers direct events, such as a Cloud Storage object being finalized, Cloud Audit Logs events and Pub/Sub sources, plus event filters and the trigger's service account.
32. Level 32: Configuring Event Receivers: CloudEvents and Idempotency
    [Section 3: Configuring cloud-native applications for deployment — 3.1 Cloud Run] Learn how to write a Cloud Run event receiver that correctly parses CloudEvents and Pub/Sub push envelopes, responds with the right status codes, and handles duplicate or out-of-order delivery safely.
33. Level 33: Eventarc Advanced: Buses, Pipelines and Routing
    [Section 3: Configuring cloud-native applications for deployment — 3.1 Cloud Run] Learn when to move from Eventarc Standard to Eventarc Advanced. Advanced can receive, filter, transform and route messages through a central bus to multiple destinations, including Cloud Run and external HTTP targets, across teams and projects.
34. Level 34: Versioning APIs Served from Cloud Run
    [Section 3: Configuring cloud-native applications for deployment — 3.1 Cloud Run] Learn how to version an API running on Cloud Run with path or header versioning and OpenAPI specifications. The level also covers Cloud Run revision tags, so that v1 and v2 can run side by side and be tested at their own URLs before traffic moves.
35. Level 35: Exposing and Securing Cloud Run APIs with Apigee
    [Section 3: Configuring cloud-native applications for deployment — 3.1 Cloud Run] Learn how to put an Apigee API proxy in front of a Cloud Run backend. The level covers API products, API keys or OAuth 2.0 for consumers, quotas and Spike Arrest for traffic control, and the proxy calling a private Cloud Run service with a Google-signed identity token.
36. Level 36: Deploying Containerized Applications to GKE
    [Section 3: Configuring cloud-native applications for deployment — 3.2 GKE] Learn how to deploy a container image from Artifact Registry to GKE with Deployments, Services, ConfigMaps and Secrets. The level covers Autopilot versus Standard clusters, rolling updates, and Workload Identity Federation for GKE so that pods call Google Cloud APIs as a least-privilege identity.
37. Level 37: Exposing GKE Workloads: Services, Ingress and Gateway
    [Section 3: Configuring cloud-native applications for deployment — 3.2 GKE] Learn how to expose a deployed GKE workload inside and outside the cluster using ClusterIP and LoadBalancer Services, Ingress and the Gateway API with Google Cloud load balancers.
38. Level 38: Kubernetes Health Checks: Liveness, Readiness, Startup
    [Section 3: Configuring cloud-native applications for deployment — 3.2 GKE] Learn how to use liveness, readiness and startup probes to increase application availability on GKE. The level covers what each probe controls, how to tune its timings, and the outage patterns that badly designed probes cause.
39. Level 39: Horizontal Pod Autoscaler: Scaling and Metrics
    [Section 3: Configuring cloud-native applications for deployment — 3.2 GKE] Learn how to set Horizontal Pod Autoscaler attributes: minReplicas, maxReplicas, CPU and memory utilisation targets, custom and external metrics such as Pub/Sub backlog, and scaling behavior. The level also covers why HPA depends on resource requests.
40. Level 40: Section 3 Checkpoint: Deployment Scenarios
    [Section 3: Configuring cloud-native applications for deployment — practice and exam craft] Practise exam-style scenario and code-reading questions across all of Section 3: Cloud Run source deploys, Pub/Sub and Eventarc triggers, event receivers, API versioning with Apigee, GKE deployments, probes and HPA. Each wrong option comes with an explanation.
41. Level 41: Managing Connections to Google Cloud Datastores
    [Section 4: Integrating applications with Google Cloud services, 4.1] Learn to manage connections to Cloud SQL, Firestore and Cloud Storage from Cloud Run and GKE: set connection pool sizes, keep each client for the life of the instance instead of creating one per request, and keep the connection count under Cloud SQL's limits as instances scale out.
42. Level 42: Reading and Writing Data Across Google Cloud Sources
    [Section 4: Integrating applications with Google Cloud services, 4.1] Learn to read and write data correctly in Firestore, Cloud SQL and Cloud Storage: when to use Firestore transactions or batched writes, how to write parameterized Cloud SQL queries, and when to use streaming or resumable Cloud Storage uploads.
43. Level 43: Publishing and Consuming Messages with Pub/Sub
    [Section 4: Integrating applications with Google Cloud services, 4.1] Learn to write applications that publish and consume data with Pub/Sub: publisher batching, pull versus push subscriptions, acknowledgement deadlines, ordering keys, dead-letter topics and idempotent consumers.
44. Level 44: Enabling Services and Choosing How to Call Cloud APIs
    [Section 4: Integrating applications with Google Cloud services, 4.2] Learn to enable Google Cloud services and choose the right way to call them: Cloud Client Libraries, the REST API, gRPC, or API Explorer for trying calls out.
45. Level 45: Efficient API Calls: Batch, Filter, Paginate, Cache, Retry
    [Section 4: Integrating applications with Google Cloud services, 4.2] Learn the five efficiency practices the guide names for Cloud API calls: batching requests, restricting return data with field masks or partial responses, paginating with page tokens, caching results, and handling errors with exponential backoff and jitter.
46. Level 46: Using Service Accounts to Make Cloud API Calls
    [Section 4: Integrating applications with Google Cloud services, 4.2] Learn how code uses service accounts to call Cloud APIs: attached service accounts found through Application Default Credentials, service account impersonation for local development and CI, and why you avoid downloaded keys.
47. Level 47: Instrumenting Code with Logs, Metrics and Trace IDs
    [Section 4: Integrating applications with Google Cloud services, 4.3] Learn to instrument code for Google Cloud Observability: structured JSON logging to Cloud Logging, custom metrics and Cloud Trace spans with OpenTelemetry, and passing trace IDs (the traceparent header and the logging.googleapis.com/trace log field) so spans and logs connect across services.
48. Level 48: Identifying and Resolving Issues with Cloud Observability
    [Section 4: Integrating applications with Google Cloud services, 4.3] Learn to find and fix production issues with Google Cloud Observability: query in Logs Explorer, chart in Metrics Explorer, set alerting policies and log-based metrics, read latency in Cloud Trace, and find CPU and memory hotspots with Cloud Profiler.
49. Level 49: Error Reporting and AI-Assisted Observability
    [Section 4: Integrating applications with Google Cloud services, 4.3] Learn to manage application issues with Error Reporting (how errors are grouped, the Open, Acknowledged, Resolved and Muted states, notifications) and to speed up root-cause analysis with AI-assisted observability in Gemini Cloud Assist, staying aware of its limits.
50. Level 50: Timed Mock Exam: Professional Cloud Developer Timed mock
    [All sections] A timed mock exam of 50 questions in 120 minutes, with a 70% pass mark, made up of original scenario and code-reading questions weighted like the exam guide across Sections 1-4: designing, building and testing, deploying, and integrating Google Cloud services.

Access

The first 2 levels are free with a free account. Every level, the podcast edition and the AI tutor come with All Access at £4.99/month or any Creator plan — see pricing.

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