Professional Cloud Developer Exam Questions

Master Google Cloud Development: Build Scalable Applications with CertCraft Institute

The Google Cloud Professional Cloud Developer course by CertCraft Institute equips you with the essential skills to design, build, deploy, and manage cloud-native applications on Google Cloud Platform (GCP). This comprehensive training aligns directly with the official certification blueprint, providing you with the knowledge, hands-on labs, and practice questions needed to excel in your career as a Google Cloud Developer.

What You’ll Learn

This course covers a wide range of topics crucial for modern cloud development, including:

• Developing, Deploying, and Monitoring Applications: Learn to utilize powerful Google Cloud services and APIs for efficient application lifecycle management.
• Performance Management: Master the Google Cloud Operations Suite (formerly Stackdriver) to monitor and optimize application performance.
• Cloud Security: Implement robust security measures using IAM, secrets management, and service identities to protect your cloud applications.
• CI/CD Integration: Automate your development workflow by integrating CI/CD pipelines and automated testing within the GCP development environment.

Who This Course Is For

This course is ideal for:

• Developers preparing for the Google Cloud Professional Cloud Developer certification.
• Software Engineers building cloud-native applications on GCP.
• Backend and Full-Stack Developers transitioning to Google Cloud environments.
• DevOps and Platform Engineers integrating development workflows in cloud systems.

Course Modules

Section 1: Designing Highly Scalable, Available, and Reliable Cloud-Native Applications (~36%)

• Designing High-Performing Applications and APIs: Explore options like Compute Engine, GKE, and Cloud Run. Learn to build and deploy containers, understand geographic distribution of Google Cloud services, implement load balancing, and utilize Memorystore for caching. Master API creation with REST and gRPC, and leverage tools like Apigee and Cloud API Gateway for security and observability. Implement asynchronous/event-driven architectures using Eventarc and Pub/Sub, and optimize for cost and resource usage.
• Designing Secure Applications: Implement data retention using Cloud Storage lifecycle policies, utilize IAP and Web Security Scanner for vulnerability management, and leverage Artifact Analysis and Security Command Center. Secure sensitive information with Secret Manager and Cloud KMS, manage authentication with Application Default Credentials, JWT, OAuth 2.0, and Auth Proxies, and secure access with IAM roles and service accounts. Ensure secure service-to-service communication with Cloud Service Mesh and Network Policies, and apply least-privilege principles and Binary Authorization for artifact security.
• Storing and Accessing Data: Choose the optimal storage system for your needs, design schemas for both structured (AlloyDB, Spanner) and unstructured (Bigtable, Datastore) data, and understand consistency models. Utilize Cloud Storage with signed URLs for controlled access and leverage BigQuery for data analytics and AI/ML workloads.

Section 2: Building and Testing Applications (~23%)

• Setting Up Development Environment: Effectively use the Google Cloud CLI, Cloud Console, SDK, Cloud Code, Cloud Shell, and Cloud Workstations. Leverage Gemini Cloud Assist and Code Assist to enhance your development experience.
• Building Applications: Build and store containers using Cloud Build and Artifact Registry. Implement provenance using Binary Authorization.
• Testing Applications: Write effective unit tests (with optional assistance from Gemini Code Assist) and automate integration tests within Cloud Build.

Section 3: Deploying Applications (~20%)

• Deploying to Cloud Run: Deploy applications directly from source code to Cloud Run, and trigger services using Eventarc or Pub/Sub. Secure APIs with Apigee and manage API versions with Cloud Endpoints for backward compatibility.
• Deploying to GKE: Deploy containerized apps to GKE, define resource requirements, and utilize Kubernetes health checks and Horizontal Pod Autoscaler for cost optimization and high availability.

Section 4: Integrating Applications with Google Cloud Services (~21%)

• Data and Storage Integration: Connect to Cloud SQL, Firestore, and Cloud Storage. Read and write data across various Google Cloud datastores and build Pub/Sub applications for real-time data streaming.