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Section 4: Integrating Google Cloud Services
The aim of this domain is to evaluate the learners based on their capacity to integrate an app with storage & data services, integrate an app with compute services, as well as integrate Cloud APIs with apps.
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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
- Microservices
- Deploying and securing API services
- Graceful shutdown on platform termination
- Geographic distribution of Google Cloud services (e.g., latency, regional services, zonal services)
- Google-recommended practices and documentation
- Caching solutions
- Evaluating different services and technologies
- Loosely coupled applications using asynchronous Cloud Pub/Sub events
- User session management
- Defining a key structure for high-write applications using Cloud Storage, Cloud Bigtable, Cloud Spanner, or Cloud SQL
- Scaling velocity characteristics/tradeoffs of IaaS (infrastructure as a service) vs. CaaS (container as a service) vs. PaaS (platform as a service)
Designing secure applications
- Storing and rotating application secrets using Cloud KMS
- Securing service-to-service communications (e.g., service mesh, Kubernetes network policies, and Kubernetes namespaces)
- Security mechanisms that secure/scan application binaries and manifests
- IAM roles for users/groups/service accounts
- Google-recommended practices and documentation
- Authenticating to Google services (e.g., application default credentials, JWT, OAuth 2.0)
- Security mechanisms that protect services and resources
- Implementing requirements that are relevant for applicable regulations (e.g., data wipeout)
- Certificate-based authentication (e.g., SSL, mTLS)
- Set compute/workload identity to least privileged access
Managing application data
- Structured vs. unstructured data
- Strong vs. eventual consistency
- Choosing data storage options based on use case considerations, such as:
- Data volume
- Following Google-recommended practices and documentation
- Defining database schemas for Google-managed databases (e.g., Cloud Firestore, Cloud Spanner, Cloud Bigtable, Cloud SQL)
- Cloud Storage-signed URLs for user-uploaded content
- Frequency of data access in Cloud Storage
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
- Creating Google Cloud projects
- Emulating Google Cloud services for local application development
Writing code
- Efficiency
- Algorithm design
- Modern application patterns
- Unit testing
- Agile software development
Testing
- Performance testing
- Integration testing
- Load testing
Building
- Creating container images from code
- Reviewing and improving continuous integration pipeline efficacy
- Developing a continuous integration pipeline using services (e.g., Cloud Build, Container Registry) that construct deployment artifacts
- Creating a Cloud Source Repository and committing code to it
3 Deploying applications
Recommend appropriate deployment strategies for the target compute environment (Compute Engine, Google Kubernetes Engine). Strategies include:
- Rolling deployments
- Traffic-splitting deployments
- Canary deployments
- Blue/green deployments
Deploying applications and services on Compute Engine
- Modifying the VM service account
- Installing an application into a VM
- Managing Compute Engine VM images and binaries
- Exporting application logs and metrics
- Manually updating dependencies on a VM
Deploying applications and services to Google Kubernetes Engine (GKE)
- Configuring Kubernetes namespaces and access control
- Define deployments, services, and pod configurations
- Configuring application accessibility to user traffic and other services
- Building a container image using Cloud Build
- Managing container lifecycle
- Defining workload specifications (e.g., resource requirements)
- Managing Kubernetes RBAC and Google Cloud IAM relationship
- Deploying a containerized application to GKE
Deploying a Cloud Function
- Securing Cloud Functions
- Cloud Functions that are triggered via an event (e.g., Cloud Pub/Sub events, Cloud Storage object change notification events)
- 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
- 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
- Writing an application that publishes/consumes data asynchronously (e.g., from Cloud Pub/Sub)
- Storing and retrieving objects from Cloud Storage
Integrating an application with compute services
- Implementing service discovery in Google Kubernetes Engine and Compute Engine
- Using the command-line interface (CLI), Google Cloud Console, and Cloud Shell tools
- Reading instance metadata to obtain application configuration
- Authenticating users by using OAuth2.0 Web Flow and Identity Aware Proxy
Integrating Google Cloud APIs with applications
- Enabling a Google Cloud API
- Caching results
- 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:
- Error handling (e.g., exponential backoff)
- Batching requests
- Paginating results
- Restricting return data
5 Managing Application Performance Monitoring
Managing Compute Engine VMs
- Analyzing logs
- Inspecting resource utilization over time
- Viewing syslogs from a VM
- Analyzing a failed Compute Engine VM startup
- Sending logs from a VM to Cloud Monitoring
- Debugging a custom VM image using the serial port
Managing Google Kubernetes Engine workloads
- Using external metrics and corresponding alerts
- Analyzing container lifecycle events (e.g., CrashLoopBackOff, ImagePullErr)
- Analyzing logs
- Configuring workload autoscaling
- Configuring logging and monitoring
Troubleshooting application performance
- Reviewing application performance (e.g., Cloud Trace, Prometheus, OpenCensus)
- Profiling performance of request-response
- Graphing metrics
- Using Cloud Debugger
- Creating a monitoring dashboard
- Exporting logs from Google Cloud
- Using documentation, forums, and Google support
- Profiling services
- Monitoring and profiling a running application
- Writing custom metrics and creating metrics from logs
- Viewing logs in the Google Cloud Console
- Reviewing stack traces for error analysis
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Reference: https://cloud.google.com/certification/cloud-developer
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Section 2: Building & Testing Apps
To answer the questions related to this section, the test takers should be capable of setting up their local development environment as well as writing efficient code. It also measures the ability of the candidates to perform testing, including unit testing, integration testing, performance testing, and load testing. They should also demonstrate that they know how to perform building. This involves executing source control management; creating secure container images from code; developing a continuous integration pipeline with the help of services that construct deployment artifacts; reviewing & improving continuous integration pipeline effectiveness.
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