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

Designing secure applications

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

Managing application data

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

Refactoring applications to migrate to Google Cloud

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

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

• Load testing
• Integration testing
• Performance testing

Building

• 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
• 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
• Canary deployments
• Traffic-splitting deployments

Deploying applications and services on Compute Engine

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

Deploying applications and services to Google Kubernetes Engine (GKE)

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

Deploying a Cloud Function

• Cloud Functions that are invoked via HTTP
• Securing Cloud Functions
• 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

• 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
• Storing and retrieving objects from Cloud Storage
• Read/write data to/from various databases (e.g., SQL, JDBC)
• Writing an application that publishes/consumes data asynchronously (e.g., from Cloud Pub/Sub)

Integrating an application with compute services

• Authenticating users by using OAuth2.0 Web Flow and Identity Aware Proxy
• Reading instance metadata to obtain application configuration
• 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

• Caching results
• Enabling a Google Cloud API
• Using service accounts to make Google API calls
• Error handling (e.g., exponential backoff)
• Restricting return data
• Batching requests
• Paginating results
• 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
• Sending logs from a VM to Cloud Monitoring
• Analyzing a failed Compute Engine VM startup
• Debugging a custom VM image using the serial port

Managing Google Kubernetes Engine workloads

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

Troubleshooting application performance

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

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

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

Incorporating Google Cloud Services

• Incorporate Applications with Data & Storage Services: The considerations for this subsection include connecting to data stores, write/read data from/to different databases and write applications that consume/publish data asynchronously.
• Integrate Cloud APIs with Apps: This domain comes with caching results, restricting return data, batching requests, paginating results, and error handling.
• Integrate Applications with Compute Services: This part contains the measurement of the skills in implementing the service discovery in Compute Engine and GKE, authenticating utilizing Identity-Aware Proxy & OAuth2.0 Web Flow, and authenticating with Workload Identity to Cloud APIs.

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

Deploying Apps

• 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.
• 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 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.
• 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.