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Main Exam Details

Google Professional Cloud Developer exam lasts for 2 hours and costs $200. Usually, the questions in it come in form of multiple choice and multiple select items. The exam is available across the globe in Japanese and English languages and has two recognized delivery modes. Firstly, candidates can enroll in the online-proctored test and take it from any location. However, if you fancy the idea of sitting for the exam from a physical location, choosing the onsite proctored format makes more sense.

Usually, there are no prerequisites for the Google Professional Cloud Developer test. However, Google recommends that learners should have a minimum of 3 years of proven industry experience that includes one year of creating as well as managing solutions with GCP.

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Career Opportunity

Those individuals who have the Google Professional Cloud Developer certification can take up the job titles, such as Software Engineers, Solutions Architects, Operations Analysts, Web Application Developers, Senior Software Engineers, Information Security Managers, or Data Engineers. The average salary for these positions is $87,000 per annum.

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Deploying Apps

  • Deploy Cloud Functions: The next objective requires having the skills in securing Cloud functions, Cloud functions invoked through HTTP, and Cloud functions triggered through events from Google Cloud services.
  • Recommend the Relevant Deployment Strategies with the Relevant Tools for a Target Compute Environment: The consideration for this section includes traffic-splitting deployments, canary deployments, rolling deployments, and green/blue deployments.
  • Use a Service Account: This one covers the students’ skills in downloading and utilizing service account private key files as well as constructing service accounts based on the ethics of least privilege.
  • Deploy Applications & Services to GKE: This subtopic includes the evaluation of one’s skills in deploying containerized applications to GCE, configuring Google Cloud IAM and Kubernetes RBAC relationships, identifying workload specifications, and configuring the Kubernetes namespaces, among others.
  • Deploy Applications & Services on the Compute Engine: This area covers bootstrapping of applications, management of service accounts for virtual machines, management of the Compute Engine virtual machine binaries and images, and exporting of application metrics and logs.

Reference: https://cloud.google.com/certification/cloud-developer

Google Professional-Cloud-Developer Exam Syllabus Topics:

SectionWeightObjectives
Building and testing applications23%- Setting up development environments
  • 1. Emulating Google Cloud services locally
  • 2. Using Cloud SDK, Cloud Code, Cloud Shell, and Cloud Workstations
- Building applications and containers
  • 1. Implementing containerization best practices
  • 2. Configuring build pipelines and provenance
  • 3. Using Cloud Build and Artifact Registry
- Testing applications
  • 1. Using AI-assisted development tools
  • 2. Automating testing in CI/CD workflows
  • 3. Writing unit, integration, and end-to-end tests
Designing highly scalable, secure, and reliable cloud-native applications32%- Designing for observability and monitoring
  • 1. Designing for debugging and troubleshooting
  • 2. Planning logging, metrics, and tracing strategies
- Designing secure applications
  • 1. Implementing authentication and authorization mechanisms
  • 2. Applying security best practices and compliance requirements
  • 3. Securing data in transit and at rest
- Designing high-performing applications and APIs
  • 1. Optimizing for latency and throughput
  • 2. Selecting appropriate compute and deployment platforms
  • 3. Designing for high availability and fault tolerance
Integrating applications with Google Cloud services21%- Implementing application performance monitoring
  • 1. Instrumenting code for metrics and traces
  • 2. Using operations suite tools
- Integrating with data and storage services
  • 1. Working with relational, NoSQL, and in-memory databases
  • 2. Implementing messaging and event streaming
  • 3. Using object storage and file storage solutions
- Consuming Google Cloud APIs and services
  • 1. Integrating with AI/ML and generative AI services
  • 2. Enabling and managing APIs
  • 3. Using client libraries, REST, and gRPC
Configuring cloud-native applications for deployment24%- Deploying applications to Google Kubernetes Engine
  • 1. Configuring scaling, auto-scaling, and updates
  • 2. Creating and managing workloads and services
  • 3. Managing configurations and secrets
- Deploying to App Engine or Cloud Functions
  • 1. Managing versions and traffic splitting
  • 2. Configuring serverless deployments
- Deploying applications to Cloud Run
  • 1. Configuring scaling, networking, and security
  • 2. Integrating with event-driven services
  • 3. Deploying from source code or containers

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