Overview

Google Cloud Professional Cloud DeveloperCourse by CertCraft InstituteThe Google Cloud Professional Cloud Developer course by CertCraft Institute is built to help you gain the skills needed to build scalable, secure, and reliable cloud-native applications on Google Cloud. This course follows the official certification blueprint and includes instruction, labs, and practice content to prepare you for the certification exam and real-world developer tasks.What You'll LearnDevelop, deploy, and monitor applications using Google Cloud-native services and APIsManage application performance with Google Cloud Operations Suite (formerly Stackdriver)Secure cloud applications using IAM, secrets management, and service identitiesIntegrate CI/CD pipelines and automated testing in a GCP development workflowRequirements / PrerequisitesFamiliarity with at least one programming language (e.g., Python, Java, Go, or Node.js)Basic understanding of cloud concepts and software development life cyclesPrior experience deploying applications or working with APIs is helpfulAccess to a Google Cloud account for hands-on practiceWho This Course Is ForDevelopers preparing for the Google Cloud Professional Cloud Developer certificationSoftware engineers working with cloud-native applications on GCPBackend or full-stack developers transitioning to Google Cloud environmentsDevOps or platform engineers integrating development workflows in cloud systemsSection 1: Designing Highly Scalable, Available, and Reliable Cloud-Native Applications (~36%)1.1 Designing High-Performing Applications and APIsChoose appropriate platforms (e.g., Compute Engine, GKE, Cloud Run)Build, refactor, and deploy containers to Cloud Run and GKEUnderstand geographic distribution of Google Cloud services (latency, zones, regions)Configure load balancing and session affinity for performanceImplement caching with MemorystoreCreate and deploy APIs using REST or gRPCUse tools like Apigee and Cloud API Gateway for rate limiting, authentication, observabilityUse asynchronous/event-driven approaches (Eventarc, Pub/Sub)Optimize applications for cost and resource usageUnderstand zonal/regional failover with data replicationUse traffic splitting strategies (e.g., A/B testing, gradual rollouts) on Cloud Run or GKEOrchestrate services using Workflows, Eventarc, Cloud Tasks, and Cloud Scheduler1.2 Designing Secure ApplicationsImplement data retention with Cloud Storage lifecycle policiesUse IAP and Web Security Scanner to identify and mitigate vulnerabilitiesAddress vulnerabilities flagged by Artifact Analysis and Security Command CenterManage secrets, credentials, and encryption keys (Secret Manager, Cloud KMS)Authenticate with Application Default Credentials, JWT, OAuth 2.0, Auth ProxiesManage end-user accounts with Identity PlatformSecure access using IAM roles and service accountsSecure service-to-service communication (Cloud Service Mesh, Network Policies)Apply least-privilege principlesUse Binary Authorization to secure artifacts1.3 Storing and Accessing DataSelect the right storage system based on volume/performanceDesign schemas for structured (AlloyDB, Spanner) and unstructured (Bigtable, Datastore) dataUnderstand consistency models for Cloud SQL, Spanner, Bigtable, etc.Create signed URLs to grant access to Cloud StorageWrite data to BigQuery for analytics or AI/ML workloadsSection 2: Building and Testing Applications (~23%)2.1 Setting Up Development EnvironmentUse Google Cloud CLI to emulate services locallyWork with Cloud Console, SDK, Cloud Code, Cloud Shell, Cloud WorkstationsLeverage Gemini Cloud Assist and Code Assist for development tasks2.2 Building ApplicationsBuild and store containers using Cloud Build and Artifact RegistryConfigure provenance using Binary Authorization2.3 Testing ApplicationsWrite unit tests (optionally with Gemini Code Assist)Run automated integration tests in Cloud BuildSection 3: Deploying Applications (~20%)3.1 Deploying to Cloud RunDeploy from source code to Cloud RunTrigger Cloud Run services with Eventarc or Pub/SubConfigure event receiversSecure APIs using tools like ApigeeManage API versions using Cloud Endpoints with backward compatibility3.2 Deploying to GKEDeploy containerized apps to GKEDefine resource requirements for workloadsUse Kubernetes health checks for availabilityConfigure Horizontal Pod Autoscaler for cost optimizationSection 4: Integrating Applications with Google Cloud Services (~21%)4.1 Data and Storage IntegrationManage connections to Cloud SQL, Firestore, Cloud StorageRead/write data across Google Cloud datastoresBuild Pub/Sub applications for real-time data streaming4.2 Consuming Google Cloud APIsEnable and call Google Cloud services via Cloud Client Libraries, REST, gRPC, API ExplorerHandle batching, pagination, caching, and error retries (e.g., exponential backoff)Use service accounts securely when calling APIs4.3 Troubleshooting and ObservabilityInstrument apps with metrics, logs, traces (Cloud Monitoring/Logging/Trace)Diagnose and resolve issues using Google Cloud Observability toolsTrack and manage errors with Error ReportingUse trace IDs to follow issues across servicesGet AI-assisted help with Gemini Cloud Assist

Skills

Reviews