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

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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

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

Designing secure applications

• Certificate-based authentication (e.g., SSL, mTLS)
• Securing service-to-service communications (e.g., service mesh, Kubernetes network policies, and Kubernetes namespaces)
• IAM roles for users/groups/service accounts
• Security mechanisms that secure/scan application binaries and manifests
• Google-recommended practices and documentation
• Security mechanisms that protect services and resources
• 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
• Storing and rotating application secrets using Cloud KMS

Managing application data

• Following Google-recommended practices and documentation
• Structured vs. unstructured data
• Data volume
• Cloud Storage-signed URLs for user-uploaded content
• Frequency of data access in Cloud Storage
• 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)
• Strong vs. eventual consistency

Refactoring applications to migrate to Google Cloud

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

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

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

Testing

• Integration testing
• Performance testing
• Load testing

Building

• Creating container images from code
• 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

3 Deploying applications

Recommend appropriate deployment strategies for the target compute environment (Compute Engine, Google Kubernetes Engine). Strategies include:

• Canary deployments
• Blue/green deployments
• Rolling 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)

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

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

• Using the command-line interface (CLI), Google Cloud Console, and Cloud Shell tools
• 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)
• 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

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

Integrating Google Cloud APIs with applications

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

5 Managing Application Performance Monitoring

Managing Compute Engine VMs

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

Managing Google Kubernetes Engine workloads

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

Troubleshooting application performance

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

Who should take the Google Professional Cloud Developer exam

Individuals should pursue the Google Professional Cloud Developer Exam if they want to demonstrate their expertise and ability to design highly scalable, available, and reliable cloud-native applications and deploy applications. It's perfect for solutions and/or enterprise architects, systems administrators or operations team members or simply any professional who wants in on this specific area of IT and cloud. A Professional Cloud Developer should have skills at producing meaningful metrics and logs to debug and trace code and proficiency with at least one general-purpose programming language.

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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.

Building & Testing Apps

• Write Efficient Code: The potential candidates should understand algorithm design, software development methodologies, modern app patterns, and debugging & profiling code.
• Testing: Here, an applicant needs to have the skills in unit testing, performance testing, integration testing, and load testing.
• Set up a Local Development Environment: This subsection measures your skills in creating different Google Cloud projects, using developing tools and command-line interfaces, Cloud Shell tools, and Google Cloud Console, as well as imitating Google Cloud services for the development of the local application.
• Building: It covers the skills in managing source control, improving and reviewing continuous efficiency in the integration pipeline, as well as creating different secure container images through code.

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

Google Professional-Cloud-Developer Exam Syllabus Topics: