Google Professional-Cloud-Developer : Google Certified Professional - Cloud Developer

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

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

Designing secure applications

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

Managing application data

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

Refactoring applications to migrate to Google Cloud

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

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

Testing

• Load testing
• Integration testing
• Performance testing

Building

• Reviewing and improving continuous integration pipeline efficacy
• 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
• 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
• Canary deployments
• Traffic-splitting deployments
• Rolling deployments

Deploying applications and services on Compute Engine

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

Deploying applications and services to Google Kubernetes Engine (GKE)

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

Deploying a Cloud Function

• 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
• 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

• 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)
• Storing and retrieving objects from Cloud Storage

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

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

5 Managing Application Performance Monitoring

Managing Compute Engine VMs

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

Managing Google Kubernetes Engine workloads

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

Troubleshooting application performance

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

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According to the current outline, the exam covers the skill areas listed below:

• Managing the monitoring of application performance
• Creating scalable, reliable, and highly available cloud-native apps
• Handling integration of Google Cloud Services
• Building as well as testing applications
• Performing applications deployment

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Google Professional Cloud Developer Practice Test Questions, Google Professional Cloud Developer Exam Practice Test Questions

The Professional Cloud Developer certificate validates the skills of the interested candidates in building highly available and scalable applications with the use of the tools and practices recommended by Google. The potential applicants for this certification must demonstrate practical experience with developer tools, Cloud-native applications, next-gen databases, and managed services. They also have the expertise in at least one programming language and can develop meaningful logs and metrics to trace and debug code. Those individuals pursuing this option must pass one qualifying exam.

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

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