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What is the duration, language, and format of Google Professional Cloud Developer Exam

• Language: English
• Number of Questions: 50-60
• Recommended experience: 3+ years of industry experience including 1+ years designing and managing solutions using GCP.
• Format: Multiple choices, multiple select
• Passing score: 80%
• Length of Examination: 240 minutes

Conclusion

Cloud technology is trending these days and it is easy to see why. Moreover, as the industry expands, the demand for cloud professionals raises too. Thus, the decision to become an accredited specialist in this field is worth all the efforts you are going to put into the process of getting certified. Now you know more about the Google Professional Cloud Developer exam, what you need to master to ace it successfully, and what are the best training courses and books that will help you prepare for the test. Futureproof your skills by obtaining your certificate today.

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
• Scaling velocity characteristics/tradeoffs of IaaS (infrastructure as a service) vs. CaaS (container as a service) vs. PaaS (platform as a service)
• Caching solutions
• 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
• Evaluating different services and technologies
• Google-recommended practices and documentation
• Geographic distribution of Google Cloud services (e.g., latency, regional services, zonal services)
• Graceful shutdown on platform termination
• User session management

Designing secure applications

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

• 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
• Strong vs. eventual consistency
• Data volume
• Choosing data storage options based on use case considerations, such as:
• Frequency of data access in Cloud Storage
• Following Google-recommended practices and documentation
• Structured vs. unstructured data

Refactoring applications to migrate to Google Cloud

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

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

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

Testing

• Load testing
• Integration testing
• Performance testing

Building

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

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

Deploying applications and services on Compute Engine

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

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
• Define deployments, services, and pod configurations
• Defining workload specifications (e.g., resource requirements)
• Managing container lifecycle
• Configuring Kubernetes namespaces and access control
• Managing Kubernetes RBAC and Google Cloud IAM relationship

Deploying a Cloud Function

• 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
• Securing Cloud Functions

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

• 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)
• Read/write data to/from various databases (e.g., SQL, JDBC)
• Storing and retrieving objects from Cloud Storage

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

Integrating Google Cloud APIs with applications

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

5 Managing Application Performance Monitoring

Managing Compute Engine VMs

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

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

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

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

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