Google Professional Cloud Developer (GCP) Practice Exam
Google Cloud Certified - Professional Cloud Developer Practice Exam
Google Cloud Certified - Professional Cloud Developer builds scalable and highly available applications using Google recommended practices and tools that leverage fully managed services. This individual has experience with next generation databases, runtime environments and developer tools. They also have proficiency with at least one general purpose programming language and are skilled with using Stackdriver to produce meaningful metrics and logs to debug and trace code.
Google Cloud Certified - Professional Cloud Developer exam assesses your ability to -
- Design highly scalable, available, and reliable cloud-native applications
- Build and test applications
- Deploy applications
- Integrate Google Cloud Platform services
- Manage application performance monitoring
Exam Structure
- Exam Duration: 2 hours
- Exam Languages: English'
- Exam format: Multiple choice and multiple select, taken in person at a test center. Locate a test center near you.
- Exam Prerequisites: None
- Recommended experience: 3+ years of industry experience including 1+ years designing and managing solutions using GCP.
Course Structure
The Google Professional Cloud Developer (GCP) covers latest and updated exam topics -
Domain 1 - Understanding to design Scalable, Available, and Reliable Cloud-Native Applications (approx 33%)
1.1 Explain High-Performance Applications and APIs Design
Key considerations include:
- Microservices Architecture
- Platform Selection: Choosing the right platform based on specific use cases and requirements (e.g., IaaS, CaaS, PaaS, FaaS).
- Application Modernization: Techniques like containerization.
- Geographical Distribution of Services: Understanding Google Cloud services in terms of latency, regional, and zonal services.
- User Session Management
- Caching Mechanisms
- Comparative Analysis: HTTP REST vs. gRPC (Google Remote Procedure Call).
- Service Control Integration: Utilizing API service capabilities (e.g., Apigee).
- Asynchronous Applications: Designing loosely coupled applications using tools like Apache Kafka, Pub/Sub, or Eventarc.
- Code Instrumentation: Producing metrics, logs, and traces.
- Cost and Resource Optimization
- Error Handling and Disaster Recovery: Managing errors, disasters, and scaling events gracefully.
1.2 Explain Application Security Design
Key considerations include:
- Data Lifecycle and Residency Compliance: Implementing solutions for regulatory requirements.
- Vulnerability Identification: Security measures to protect services and resources (e.g., Identity-Aware Proxy, Web Security Scanner).
- Application Security: Scanning and securing application binaries, dependencies, and manifests (e.g., Container Analysis).
- Secrets and Key Management: Storing, accessing, and rotating application secrets and encryption keys (e.g., Secret Manager, Cloud Key Management Service).
- Authentication to Google Cloud Services: Using application default credentials, JWT, and OAuth 2.0.
- End-User Account Management: Authentication with Identity Platform.
- Identity and Access Management (IAM): Managing roles for users, groups, and service accounts.
- Service-to-Service Security: Techniques for securing communications (e.g., service mesh, Kubernetes Network Policies).
- Keyless Access Management: Employing least privileged access strategies (e.g., Workload Identity).
- Certificate-Based Authentication: Implementing SSL and mTLS.
- Software Supply Chain Security: Understanding Supply-chain Levels for Software Artifacts (SLSA).
1.3 Explain Application Data Storage Solutions
Key considerations include:
- Time-Limited Access: Managing access durations for objects.
- Data Retention Needs: Establishing retention policies.
- Data Types: Structured vs. unstructured data (e.g., SQL vs. NoSQL).
- Consistency Models: Strong versus eventual consistency.
- Data Volume and Access Patterns
- Processing Types: Online transaction processing (OLTP) vs. data warehousing.
Domain 2 - Understanding Application Development and Testing (approx 26%)
2.1 Explain Local Development Environment Setup
Key considerations include:
- Emulation of Google Cloud Services: Setting up local environments for application development.
- Tool Utilization: Using Google Cloud console, SDK, Cloud Shell, and Cloud Workstations.
- Developer Tools: Incorporating common IDEs, Cloud Code, and Skaffold.
- Service Authentication: Methods for authenticating to Google Cloud services (e.g., Cloud SQL Auth proxy).
2.2 Explain Application Building
Key considerations include:
- Source Control Management: Implementing effective version control practices.
- Secure Container Image Creation: Building secure container images from source code.
- Continuous Integration Pipeline Development: Utilizing services like Cloud Build and Artifact Registry to create deployment artifacts.
- Optimization Techniques: Improving code and test build processes.
2.3 Explain Application Testing
Key considerations include:
- Unit Testing
- Integration Testing: Utilizing emulators for comprehensive testing.
- Performance and Load Testing
- Chaos Engineering: Conducting failure testing.
Domain 3 - Understanding Application Deployment (approx 19%)
3.1 Explain Feature Rollout Strategies
Key considerations include:
- A/B Testing
- Feature Flags Implementation
- Backward Compatibility
- API Versioning: Techniques for effective API management (e.g., Apigee).
3.2 Explain Serverless Application Deployment
Key considerations include:
- Source Code Deployment
- Event-Driven Function Invocation: Using triggers to activate functions.
- Event Receiver Configuration: Implementing solutions like Eventarc and Pub/Sub.
- API Security: Exposing and securing application APIs (e.g., Apigee).
3.3 Explain Deployment on Google Kubernetes Engine (GKE)
Key considerations include:
- Containerized Application Deployment: Deploying applications in GKE.
- Role-Based Access Control (RBAC): Integrating Kubernetes RBAC with IAM.
- Workload Specification Definition: Establishing resource requirements for workloads.
- Container Image Building: Utilizing Cloud Build for image creation.
Domain 4: Understanding Application Integration with Google Cloud Services ( approx 22)
4.1 Explain Data and Storage Services Integration
Key considerations include:
- Datastore Connections Management: Connecting to services like Cloud SQL, Firestore, Bigtable, and Cloud Storage.
- Data Interaction: Reading from and writing to various datastores.
- Asynchronous Data Publication and Consumption: Implementing solutions like Pub/Sub for streaming data.
- Service Orchestration: Using tools like Workflows, Eventarc, Cloud Tasks, and Cloud Scheduler.
4.2 Explain Google Cloud API Integration
Key considerations include:
- Service Activation: Enabling necessary Google Cloud services.
- API Call Implementation: Utilizing options like Cloud Client Library, REST API, or gRPC, with considerations for - Request Batching, Data Return , Restrictions, Result Pagination and Result Caching
- Error Management: Techniques such as exponential backoff.
- Service Account Utilization: Making API calls with service accounts.
- Integration with Google Cloud Operations Suite: Ensuring comprehensive monitoring and management.
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What are our Practice Exams?
- Practice exams have been designed by professionals and domain experts that simulate real time exam scenario.
- Practice exam questions have been created on the basis of content outlined in the official documentation.
- Each set in the practice exam contains unique questions built with the intent to provide real-time experience to the candidates as well as gain more confidence during exam preparation.
- Practice exams help to self-evaluate against the exam content and work towards building strength to clear the exam.
- You can also create your own practice exam based on your choice and preference
