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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
• Scaling velocity characteristics/tradeoffs of IaaS (infrastructure as a service) vs. CaaS (container as a service) vs. PaaS (platform as a service)
• Caching solutions
• Loosely coupled applications using asynchronous Cloud Pub/Sub events
• Google-recommended practices and documentation
• Geographic distribution of Google Cloud services (e.g., latency, regional services, zonal services)
• Graceful shutdown on platform termination
• Microservices
• User session management
• Evaluating different services and technologies
• Defining a key structure for high-write applications using Cloud Storage, Cloud Bigtable, Cloud Spanner, or Cloud SQL

Designing secure applications

• Google-recommended practices and documentation
• Storing and rotating application secrets using Cloud KMS
• Securing service-to-service communications (e.g., service mesh, Kubernetes network policies, and Kubernetes namespaces)
• Security mechanisms that secure/scan application binaries and manifests
• Security mechanisms that protect services and resources
• 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)
• Implementing requirements that are relevant for applicable regulations (e.g., data wipeout)
• Set compute/workload identity to least privileged access

Managing application data

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

• Creating Google Cloud projects
• Emulating Google Cloud services for local application development

Writing code

• Modern application patterns
• Unit testing
• Efficiency
• Algorithm design
• 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:

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

Deploying applications and services on Compute Engine

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

Deploying applications and services to Google Kubernetes Engine (GKE)

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

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

• 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

• Writing an application that publishes/consumes data asynchronously (e.g., from Cloud Pub/Sub)
• Read/write data to/from various databases (e.g., SQL, JDBC)
• Using the command-line interface (CLI), Google Cloud Console, and Cloud Shell tools
• Connecting to a data store (e.g., Cloud SQL, Cloud Spanner, Cloud Firestore, Cloud Bigtable)
• Storing and retrieving objects from Cloud Storage

Integrating an application with compute services

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

Integrating Google Cloud APIs with applications

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

5 Managing Application Performance Monitoring

Managing Compute Engine VMs

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

Managing Google Kubernetes Engine workloads

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

Troubleshooting application performance

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

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How to Prepare For Google Professional Cloud Developer Exam

Preparation Guide for Google Professional Cloud Developer Exam

Introduction

Google has designed different Role Based Certifications for the IT professionals to validate their skillset one among them is Google Professional Cloud Developer. This program gives Google cloud professionals a way to demonstrate their skills. The evaluation relies on a meticulous exam using industry standard methodology to validate aspirants proficiency standards to meet Google's proficiency standards.

According to Google, a Google Certified Professional Cloud Developer facilitate organizations to influence Google Cloud technologies. With a thorough understanding of cloud developer and Google Cloud Platform, this individual can design, develop, and manage robust, secure, scalable, highly available, and dynamic solutions to drive business objectives.

In this guide, we will cover the Google Professional Cloud Developer Exam, Google Professional Cloud Developer Certified Professionals salary and all aspects of the Google Professional Cloud Developer Certification.

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Career Opportunity

Those individuals who have the Google Professional Cloud Developer certification can take up the job titles, such as Software Engineers, Solutions Architects, Operations Analysts, Web Application Developers, Senior Software Engineers, Information Security Managers, or Data Engineers. The average salary for these positions is $87,000 per annum.

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