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According to the current outline, the exam covers the skill areas listed below:

• Building as well as testing applications
• Creating scalable, reliable, and highly available cloud-native apps
• Handling integration of Google Cloud Services
• Managing the monitoring of application performance
• Performing applications deployment

The content of the qualifying test for the Google Professional Cloud Developer certification comprises of 5 topics covering specific knowledge and skills. The candidates need to thoroughly study a detailed exam guide available on the official website before attending the test. The highlights of the topics that constitute the structure of the exam are enumerated below:

Section 1: Designing Highly Scalable, Available, and Reliable Cloud-Native Apps

Within this subject area, the examinees need to demonstrate their proficiency in designing high-performing applications & APIs; designing secure applications; managing application data; executing application modernization.

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

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

• User session management
• Geographic distribution of Google Cloud services (e.g., latency, regional services, zonal services)
• Deploying and securing API services
• Caching solutions
• Defining a key structure for high-write applications using Cloud Storage, Cloud Bigtable, Cloud Spanner, or Cloud SQL
• Graceful shutdown on platform termination
• 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)
• Google-recommended practices and documentation
• Microservices
• Loosely coupled applications using asynchronous Cloud Pub/Sub events

Designing secure applications

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

Managing application data

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

Refactoring applications to migrate to Google Cloud

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

2 Building and Testing Applications

Setting up your local development environment

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

Writing code

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

Testing

• Integration testing
• Performance testing
• Load testing

Building

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

3 Deploying applications

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

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

Deploying applications and services on Compute Engine

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

Deploying applications and services to Google Kubernetes Engine (GKE)

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

Deploying a Cloud Function

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

Using service accounts

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

4 Integrating Google Cloud Platform Services

Integrating an application with data and storage services

• 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)
• Connecting to a data store (e.g., Cloud SQL, Cloud Spanner, Cloud Firestore, Cloud Bigtable)
• Using the command-line interface (CLI), Google Cloud Console, and Cloud Shell tools
• Storing and retrieving objects from Cloud Storage

Integrating an application with compute services

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

Integrating Google Cloud APIs with applications

• Paginating results
• Error handling (e.g., exponential backoff)
• 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:
• Caching results
• Batching requests
• Restricting return data
• Enabling a Google Cloud API

5 Managing Application Performance Monitoring

Managing Compute Engine VMs

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

Managing Google Kubernetes Engine workloads

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

Troubleshooting application performance

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

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Google Professional-Cloud-Developer Exam Syllabus Topics: