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

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

Designing secure applications

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

Managing application 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
• Choosing data storage options based on use case considerations, such as:
• Strong vs. eventual consistency
• Cloud Storage-signed URLs for user-uploaded content
• Structured vs. unstructured data

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

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

Testing

• Integration testing
• Load testing
• Performance 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 container images from code
• 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:

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

Deploying applications and services on Compute Engine

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

Deploying applications and services to Google Kubernetes Engine (GKE)

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

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

• Connecting to a data store (e.g., Cloud SQL, Cloud Spanner, Cloud Firestore, Cloud Bigtable)
• Storing and retrieving objects from Cloud Storage
• 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)
• Read/write data to/from various databases (e.g., SQL, JDBC)

Integrating an application with compute services

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

5 Managing Application Performance Monitoring

Managing Compute Engine VMs

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

Managing Google Kubernetes Engine workloads

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

Troubleshooting application performance

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

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

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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.