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Exam Overview

The Professional Cloud Developer certification exam is a 2-hour test covering the multiple-choice and multiple-select questions. It can be taken in English or Japanese and is available as an online proctored and an on-site proctored exam. The registration process costs $200, and this is for the delivery of a single test. The candidates who do not pass the exam must retake it to obtain the associated certificate. To do this, they must pay a new exam fee for their retake.

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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

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

Designing secure applications

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

Managing application data

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

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

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

Testing

• Performance testing
• Load testing
• Integration testing

Building

• 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 a Cloud Source Repository and committing code to it
• 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
• Blue/green 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
• Manually updating dependencies on a VM
• Modifying the VM service account

Deploying applications and services to Google Kubernetes Engine (GKE)

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

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

• 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

• Using the command-line interface (CLI), Google Cloud Console, and Cloud Shell tools
• 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)
• Storing and retrieving objects from Cloud Storage
• Connecting to a data store (e.g., Cloud SQL, Cloud Spanner, Cloud Firestore, Cloud Bigtable)

Integrating an application with compute services

• Reading instance metadata to obtain application configuration
• 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
• Implementing service discovery in Google Kubernetes Engine and Compute Engine

Integrating Google Cloud APIs with applications

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

5 Managing Application Performance Monitoring

Managing Compute Engine VMs

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

Managing Google Kubernetes Engine workloads

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

Troubleshooting application performance

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

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

Difficulty in writing the Google Professional Cloud Developer Exam

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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.

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