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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

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

Designing secure applications

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

Managing application data

• Frequency of data access in Cloud Storage
• Cloud Storage-signed URLs for user-uploaded content
• Strong vs. eventual consistency
• 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
• Structured vs. unstructured data
• Choosing data storage options based on use case considerations, such as:

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

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

Testing

• Load testing
• Performance testing
• Integration testing

Building

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

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

Deploying applications and services on Compute Engine

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

Deploying applications and services to Google Kubernetes Engine (GKE)

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

Deploying a Cloud Function

• Securing Cloud Functions
• 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

Using service accounts

• Downloading and using a service account private key file
• Creating a service account according to the principle of least privilege

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

Integrating an application with compute services

• Authenticating users by using OAuth2.0 Web Flow and Identity Aware Proxy
• Implementing service discovery in Google Kubernetes Engine and Compute Engine
• Reading instance metadata to obtain application configuration
• Using the command-line interface (CLI), Google Cloud Console, and Cloud Shell tools

Integrating Google Cloud APIs with applications

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

5 Managing Application Performance Monitoring

Managing Compute Engine VMs

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

Managing Google Kubernetes Engine workloads

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

Troubleshooting application performance

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

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Google Professional-Cloud-Developer (Google Certified Professional - Cloud Developer) Certification Exam is designed to validate the skills and expertise of cloud developers who are responsible for developing and deploying applications on the Google Cloud Platform (GCP). Google Certified Professional - Cloud Developer certification exam is intended for software developers who have experience in creating and deploying applications on GCP. Google Cloud Platform is a cloud computing platform provided by Google that offers a range of cloud services including computing, storage, analytics, and machine learning.

As businesses worldwide continue to adopt cloud computing, the demand for experts who can develop cloud-based applications using various cloud services is experiencing an upward trend. It is in recognition of this need that Google Cloud offers the Professional-Cloud-Developer Exam - also known as Google Certified Professional - Cloud Developer.

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

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