Incorporating Google Cloud Services

• 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.
• 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.

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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

• Deploying and securing API services
• Loosely coupled applications using asynchronous Cloud Pub/Sub events
• Google-recommended practices and documentation
• Graceful shutdown on platform termination
• Evaluating different services and technologies
• Defining a key structure for high-write applications using Cloud Storage, Cloud Bigtable, Cloud Spanner, or Cloud SQL
• 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)
• Caching solutions
• User session management

Designing secure applications

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

Managing application data

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

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

Testing

• Load testing
• Performance testing
• Integration testing

Building

• 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
• 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
• Traffic-splitting deployments
• Canary deployments
• Rolling deployments

Deploying applications and services on Compute Engine

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

Deploying applications and services to Google Kubernetes Engine (GKE)

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

• 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)
• 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
• Read/write data to/from various databases (e.g., SQL, JDBC)

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
• Implementing service discovery in Google Kubernetes Engine and Compute Engine
• Using the command-line interface (CLI), Google Cloud Console, and Cloud Shell tools

Integrating Google Cloud APIs with applications

• Batching requests
• Paginating results
• Caching results
• Making API calls with a Cloud Client Library, the REST API, or the APIs Explorer, taking into consideration:
• Error handling (e.g., exponential backoff)
• Enabling a Google Cloud API
• 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
• Inspecting resource utilization over time
• Analyzing logs
• Debugging a custom VM image using the serial port
• Analyzing a failed Compute Engine VM startup
• Viewing syslogs from a VM

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

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

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Google Professional-Cloud-Developer Exam Syllabus Topics:

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

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

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