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Exam Number : Google-PCD
Exam Name : Professional Cloud Developer
Vendor Name : Google
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Google-PCD exam Format | Google-PCD Course Contents | Google-PCD Course Outline | Google-PCD exam Syllabus | Google-PCD exam Objectives


A Professional Cloud Developer builds scalable and highly available applications using Google-recommended practices and tools that leverage fully managed services. This individual has experience with cloud-native applications, runtime environments, developer tools, and next-generation databases. A Professional Cloud Developer also has proficiency with at least one general-purpose programming language and is skilled at producing meaningful metrics and logs to debug and trace code.

The Professional Cloud Developer exam assesses your ability to:
Design highly scalable, available, and reliable cloud-native applications
Build and test applications
Deploy applications
Integrate Google Cloud Platform services
Manage application performance monitoring

A Professional Cloud Developer builds scalable and highly available applications using Google-recommended practices and tools that leverage fully managed services. This individual has experience with cloud-native applications, runtime environments, developer tools, and next-generation databases. A Professional Cloud Developer also has proficiency with at least one general-purpose programming language and is skilled at producing meaningful metrics and logs to debug and trace code.

Section 1: Designing highly scalable, available, and reliable cloud-native applications
1.1 Designing high-performing applications and APIs. Considerations include:
- Microservices
- Scaling velocity characteristics/tradeoffs of IaaS (infrastructure as a service) vs. CaaS (container as a service) vs. PaaS (platform as a service)
- Evaluating different services and technologies
- 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
- User session management
- Caching solutions
- Deploying and securing API services
- Loosely coupled applications using asynchronous Cloud Pub/Sub events
- Graceful shutdown on platform termination
- Google-recommended practices and documentation

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

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

1.4 Refactoring applications to migrate to Google Cloud. Tasks include:
- Using managed services
- Migrating a monolith to microservices
- Google-recommended practices and documentation

Section 2: Building and Testing Applications
2.1 Setting up your local development environment. Considerations include:
- Emulating Google Cloud services for local application development
- Creating Google Cloud projects

2.2 Writing code. Considerations include:
- Algorithm design
- Modern application patterns
- Efficiency
- Agile software development
- Unit testing

2.3 Testing. Considerations include:
- Performance testing
- Integration testing
- Load testing

2.4 Building. Considerations include:
- Creating a Cloud Source Repository and committing code to it
- Creating container images from code
- Developing a continuous integration pipeline using services (e.g., Cloud Build, Container Registry) that construct deployment artifacts
- Reviewing and improving continuous integration pipeline efficacy

Section 3: Deploying applications
3.1 Recommend appropriate deployment strategies for the target compute environment (Compute Engine, Google Kubernetes Engine). Strategies include:
- Blue/green deployments
- Traffic-splitting deployments
- Rolling deployments
- Canary deployments

3.2 Deploying applications and services on Compute Engine. Tasks include:
- Installing an application into a VM
- Modifying the VM service account
- Manually updating dependencies on a VM
- Exporting application logs and metrics
- Managing Compute Engine VM images and binaries

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

3.5 Using service accounts. Tasks include:
- Creating a service account according to the principle of least privilege
- Downloading and using a service account private key file

Section 4: Integrating Google Cloud Platform Services
4.1 Integrating an application with data and storage services. Tasks include:
- Read/write data to/from various databases (e.g., SQL, JDBC)
- Connecting to a data store (e.g., Cloud SQL, Cloud Spanner, Cloud Firestore, Cloud Bigtable)
- Writing an application that publishes/consumes data asynchronously (e.g., from Cloud Pub/Sub)
- Storing and retrieving objects from Cloud Storage
- Using the command-line interface (CLI), Google Cloud Console, and Cloud Shell tools

4.2 Integrating an application with compute services. Tasks include:
- Implementing service discovery in Google Kubernetes Engine and Compute Engine
- practicing instance metadata to obtain application configuration
- Authenticating users by using OAuth2.0 Web Flow and Identity Aware Proxy
- Using the command-line interface (CLI), Google Cloud Console, and Cloud Shell tools

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

Section 5: Managing Application Performance Monitoring
5.1 Managing Compute Engine VMs. Tasks include:
- Debugging a custom VM image using the serial port
- Analyzing a failed Compute Engine VM startup
- Analyzing logs
- Sending logs from a VM to Cloud Monitoring
- Inspecting resource utilization over time
- Viewing syslogs from a VM

5.2 Managing Google Kubernetes Engine workloads. Tasks include:
- Configuring logging and monitoring
- Analyzing container lifecycle events (e.g., CrashLoopBackOff, ImagePullErr)
- Analyzing logs
- Using external metrics and corresponding alerts
- Configuring workload autoscaling

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



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