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
• Geographic distribution of Google Cloud services (e.g., latency, regional services, zonal services)
• Loosely coupled applications using asynchronous Cloud Pub/Sub events
• Deploying and securing API services
• Caching solutions
• Evaluating different services and technologies
• 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)
• Graceful shutdown on platform termination
• Google-recommended practices and documentation
• User session management

Designing secure applications

• Storing and rotating application secrets using Cloud KMS
• 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)
• Security mechanisms that secure/scan application binaries and manifests
• Authenticating to Google services (e.g., application default credentials, JWT, OAuth 2.0)
• Set compute/workload identity to least privileged access
• Google-recommended practices and documentation
• IAM roles for users/groups/service accounts
• Securing service-to-service communications (e.g., service mesh, Kubernetes network policies, and Kubernetes namespaces)

Managing application data

• Choosing data storage options based on use case considerations, such as:
• Structured vs. unstructured data
• Strong vs. eventual consistency
• 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
• Data volume

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

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

Writing code

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

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
• Creating a Cloud Source Repository and committing code to it
• Reviewing and improving continuous integration pipeline efficacy
• Creating container images from code

3 Deploying applications

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

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

Deploying applications and services on Compute Engine

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

Deploying applications and services to Google Kubernetes Engine (GKE)

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

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

• 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

• Storing and retrieving objects from Cloud Storage
• 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

Integrating an application with compute services

• Reading instance metadata to obtain application configuration
• 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
• Implementing service discovery in Google Kubernetes Engine and Compute Engine

Integrating Google Cloud APIs with applications

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

5 Managing Application Performance Monitoring

Managing Compute Engine VMs

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

Managing Google Kubernetes Engine workloads

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

Troubleshooting application performance

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

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Google Professional-Cloud-Developer exam is a certification offered by Google Cloud to developers who want to validate their expertise in developing and deploying applications on the Google Cloud Platform. Google Certified Professional - Cloud Developer certification is intended for individuals who have experience in designing, building, testing, and deploying cloud-based applications using Google Cloud technologies such as App Engine, Compute Engine, Kubernetes, and Stackdriver. Professional-Cloud-Developer exam measures the candidate's proficiency in various areas such as application deployment, cloud storage, security, and monitoring.

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

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