Google Professional-Cloud-Developer Exam : Google Certified Professional - Cloud Developer

Google Professional Cloud Developer Certification Path

The Google Professional Cloud Developer Certification is the highest level of certification mainly focussing to the Google Professional Cloud Developer. There is no prerequisite for this exam but still it would be best to follow some sequence in order to prove immense knowledge as a Google Professional Cloud Developer. You can complete Google Associate Certifications then approach for the professional certification. For more information related to Google cloud certification track Google-certification-path

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

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Topics of Google Professional Cloud Developer Exam

Candidates must know the exam topics before they start of preparation. because it will really help them in hitting the core. Our Google Professional Cloud Developer Dumps will include the following topics:

1. Designing highly scalable, available, and reliable cloud-native applications

Designing high-performing applications and APIs

• Geographic distribution of Google Cloud services (e.g., latency, regional services, zonal services)
• User session management
• Defining a key structure for high-write applications using Cloud Storage, Cloud Bigtable, Cloud Spanner, or Cloud SQL
• Loosely coupled applications using asynchronous Cloud Pub/Sub events
• Microservices
• Google-recommended practices and documentation
• Graceful shutdown on platform termination
• Deploying and securing API services
• Evaluating different services and technologies
• Scaling velocity characteristics/tradeoffs of IaaS (infrastructure as a service) vs. CaaS (container as a service) vs. PaaS (platform as a service)
• Caching solutions

Designing secure applications

• Implementing requirements that are relevant for applicable regulations (e.g., data wipeout)
• Security mechanisms that secure/scan application binaries and manifests
• Google-recommended practices and documentation
• Storing and rotating application secrets using Cloud KMS
• Authenticating to Google services (e.g., application default credentials, JWT, OAuth 2.0)
• IAM roles for users/groups/service accounts
• Set compute/workload identity to least privileged access
• Certificate-based authentication (e.g., SSL, mTLS)
• Securing service-to-service communications (e.g., service mesh, Kubernetes network policies, and Kubernetes namespaces)
• Security mechanisms that protect services and resources

Managing application data

• Strong vs. eventual consistency
• Cloud Storage-signed URLs for user-uploaded content
• Choosing data storage options based on use case considerations, such as:
• Structured vs. unstructured data
• Frequency of data access in Cloud Storage
• Defining database schemas for Google-managed databases (e.g., Cloud Firestore, Cloud Spanner, Cloud Bigtable, Cloud SQL)
• Following Google-recommended practices and documentation
• Data volume

Refactoring applications to migrate to Google Cloud

• Google-recommended practices and documentation
• Using managed services
• Migrating a monolith to microservices

2 Building and Testing Applications

Setting up your local development environment

• Emulating Google Cloud services for local application development
• Creating Google Cloud projects

Writing code

• Unit testing
• Algorithm design
• Agile software development
• Efficiency
• Modern application patterns

Testing

• Load testing
• Performance testing
• Integration testing

Building

• Developing a continuous integration pipeline using services (e.g., Cloud Build, Container Registry) that construct deployment artifacts
• Reviewing and improving continuous integration pipeline efficacy
• Creating a Cloud Source Repository and committing code to it
• Creating container images from code

3 Deploying applications

Recommend appropriate deployment strategies for the target compute environment (Compute Engine, Google Kubernetes Engine). Strategies include:

• Traffic-splitting deployments
• Canary deployments
• Rolling deployments
• Blue/green deployments

Deploying applications and services on Compute Engine

• Manually updating dependencies on a VM
• Modifying the VM service account
• Exporting application logs and metrics
• Installing an application into a VM
• Managing Compute Engine VM images and binaries

Deploying applications and services to Google Kubernetes Engine (GKE)

• Deploying a containerized application to GKE
• Define deployments, services, and pod configurations
• Configuring Kubernetes namespaces and access control
• Managing Kubernetes RBAC and Google Cloud IAM relationship
• Building a container image using Cloud Build
• Defining workload specifications (e.g., resource requirements)
• Configuring application accessibility to user traffic and other services
• Managing container lifecycle

Deploying a Cloud Function

• Securing Cloud Functions
• Cloud Functions that are invoked via HTTP
• Cloud Functions that are triggered via an event (e.g., Cloud Pub/Sub events, Cloud Storage object change notification events)

Using service accounts

• Creating a service account according to the principle of least privilege
• Downloading and using a service account private key file

4 Integrating Google Cloud Platform Services

Integrating an application with data and storage services

• Using the command-line interface (CLI), Google Cloud Console, and Cloud Shell tools
• Storing and retrieving objects from Cloud Storage
• Connecting to a data store (e.g., Cloud SQL, Cloud Spanner, Cloud Firestore, Cloud Bigtable)
• Writing an application that publishes/consumes data asynchronously (e.g., from Cloud Pub/Sub)
• Read/write data to/from various databases (e.g., SQL, JDBC)

Integrating an application with compute services

• Using the command-line interface (CLI), Google Cloud Console, and Cloud Shell tools
• Implementing service discovery in Google Kubernetes Engine and Compute Engine
• Authenticating users by using OAuth2.0 Web Flow and Identity Aware Proxy
• Reading instance metadata to obtain application configuration

Integrating Google Cloud APIs with applications

• Enabling a Google Cloud API
• Using service accounts to make Google API calls
• Making API calls with a Cloud Client Library, the REST API, or the APIs Explorer, taking into consideration:
• Batching requests
• Error handling (e.g., exponential backoff)
• Caching results
• Restricting return data
• Paginating results

5 Managing Application Performance Monitoring

Managing Compute Engine VMs

• Debugging a custom VM image using the serial port
• Inspecting resource utilization over time
• Sending logs from a VM to Cloud Monitoring
• Analyzing logs
• Analyzing a failed Compute Engine VM startup
• Viewing syslogs from a VM

Managing Google Kubernetes Engine workloads

• Configuring workload autoscaling
• Analyzing container lifecycle events (e.g., CrashLoopBackOff, ImagePullErr)
• Analyzing logs
• Using external metrics and corresponding alerts
• Configuring logging and monitoring

Troubleshooting application performance

• Reviewing stack traces for error analysis
• Viewing logs in the Google Cloud Console
• Exporting logs from Google Cloud
• Reviewing application performance (e.g., Cloud Trace, Prometheus, OpenCensus)
• Writing custom metrics and creating metrics from logs
• Profiling performance of request-response
• Monitoring and profiling a running application
• Graphing metrics
• Profiling services
• Using Cloud Debugger
• Using documentation, forums, and Google support
• Creating a monitoring dashboard

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Google Professional-Cloud-Developer Exam Syllabus Topics: