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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

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

Designing secure applications

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

Managing application data

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

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

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

Writing code

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

Testing

• Integration testing
• Performance testing
• Load testing

Building

• Creating container images from code
• 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 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
• Blue/green deployments
• Traffic-splitting deployments
• Canary deployments

Deploying applications and services on Compute Engine

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

Deploying applications and services to Google Kubernetes Engine (GKE)

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

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

• 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

• Storing and retrieving objects from Cloud Storage
• Writing an application that publishes/consumes data asynchronously (e.g., from Cloud Pub/Sub)
• Using the command-line interface (CLI), Google Cloud Console, and Cloud Shell tools
• 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)

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)
• Batching requests
• Caching results
• Paginating results
• Enabling a Google Cloud API
• Restricting return data
• 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:

5 Managing Application Performance Monitoring

Managing Compute Engine VMs

• Viewing syslogs from a VM
• Inspecting resource utilization over time
• Analyzing logs
• 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

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

Troubleshooting application performance

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

Employment and Salary Opportunities

After completing the Google Professional Cloud Developer certification exam, the candidates possess all the knowledge and skills necessary for building scalable and highly available applications with the help of Google-recommended practices and tools. The expertise that you gain while preparing for the qualifying test provides you with access to numerous in-demand and high-paying jobs. Some of the roles that you can apply for after getting certified include a Cloud Infrastructure Engineer, a Google Cloud Platform Cloud Engineer, a Senior Software Engineer, a Python Backend Developer, a Java Developer, a Cloud DevOps Engineer, a Cloud Technical Solutions Developer, and a Google Cloud Platform (GCP) Architect, among others. The average salary associated with these job titles ranges between $86,500 and $207,500 per year.

Deploying Apps

• Recommend the Relevant Deployment Strategies with the Relevant Tools for a Target Compute Environment: The consideration for this section includes traffic-splitting deployments, canary deployments, rolling deployments, and green/blue deployments.
• Deploy Applications & Services to GKE: This subtopic includes the evaluation of one’s skills in deploying containerized applications to GCE, configuring Google Cloud IAM and Kubernetes RBAC relationships, identifying workload specifications, and configuring the Kubernetes namespaces, among others.
• Deploy Applications & Services on the Compute Engine: This area covers bootstrapping of applications, management of service accounts for virtual machines, management of the Compute Engine virtual machine binaries and images, and exporting of application metrics and logs.
• Use a Service Account: This one covers the students’ skills in downloading and utilizing service account private key files as well as constructing service accounts based on the ethics of least privilege.
• Deploy Cloud Functions: The next objective requires having the skills in securing Cloud functions, Cloud functions invoked through HTTP, and Cloud functions triggered through events from Google Cloud services.

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

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Google Professional-Cloud-Developer Exam Syllabus Topics: