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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

- Reading 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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