Preparation Sources and Resources

The candidates can find everything they need to prepare for the Google Professional Cloud Developer certification exam on the official website. To help the learners build the required skillset for the qualifying test, Google has developed a variety of tools. The best way to start your preparation process is to review the topics in the study guide. This will provide you with an understanding of what you need to cover while preparing for the exam.

After that, you can proceed with the Cloud Developer learning path. This is an online self-study training tool that includes lectures, demos, and hands-on labs for a better understanding of the Google Cloud products and services. Besides that, the official platform offers sample questions that enable the learners to familiarize themselves with the format of exam questions and content that may be covered in the certification test. To enhance your understanding of the exam domains, refer to the additional resources such as Google Cloud documentation and Google Cloud solutions. The students can also sign up for a subject-related webinar to get valuable tips and insights from the industry experts.

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

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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

• Google-recommended practices and documentation
• Caching solutions
• Geographic distribution of Google Cloud services (e.g., latency, regional services, zonal services)
• User session management
• Graceful shutdown on platform termination
• Loosely coupled applications using asynchronous Cloud Pub/Sub events
• Defining a key structure for high-write applications using Cloud Storage, Cloud Bigtable, Cloud Spanner, or Cloud SQL
• Deploying and securing API services
• Evaluating different services and technologies
• Microservices
• 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
• Securing service-to-service communications (e.g., service mesh, Kubernetes network policies, and Kubernetes namespaces)
• Certificate-based authentication (e.g., SSL, mTLS)
• Authenticating to Google services (e.g., application default credentials, JWT, OAuth 2.0)
• Storing and rotating application secrets using Cloud KMS
• 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)
• Security mechanisms that protect services and resources
• Security mechanisms that secure/scan application binaries and manifests

Managing application data

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

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

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

Testing

• Integration testing
• Performance testing
• Load testing

Building

• Creating container images from code
• Creating a Cloud Source Repository and committing code to it
• Developing a continuous integration pipeline using services (e.g., Cloud Build, Container Registry) that construct deployment artifacts
• Reviewing and improving continuous integration pipeline efficacy

3 Deploying applications

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

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

Deploying applications and services on Compute Engine

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

Deploying applications and services to Google Kubernetes Engine (GKE)

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

Deploying a Cloud Function

• 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)
• 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

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

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

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

5 Managing Application Performance Monitoring

Managing Compute Engine VMs

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

Managing Google Kubernetes Engine workloads

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

Troubleshooting application performance

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

The Google Professional Cloud Developer certification is created to validate the candidates’ understanding of Cloud-native software design principles. It also measures the ability of the specialists to build, test and deploy applications with the help of best practices for scalability, reliability, and security. To earn this sought-after certificate, the applicants need to ace a single exam.