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

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

Designing secure applications

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

Managing application data

• 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)
• 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:
• Data volume
• Strong vs. eventual consistency

Refactoring applications to migrate to Google Cloud

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

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

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

Testing

• Integration testing
• Load testing
• Performance 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
• Canary deployments
• Traffic-splitting deployments
• Blue/green deployments

Deploying applications and services on Compute Engine

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

Deploying applications and services to Google Kubernetes Engine (GKE)

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

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
• Writing an application that publishes/consumes data asynchronously (e.g., from Cloud Pub/Sub)
• Connecting to a data store (e.g., Cloud SQL, Cloud Spanner, Cloud Firestore, Cloud Bigtable)
• Storing and retrieving objects from Cloud Storage
• Read/write data to/from various databases (e.g., SQL, JDBC)

Integrating an application with compute services

• 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

Integrating Google Cloud APIs with applications

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

5 Managing Application Performance Monitoring

Managing Compute Engine VMs

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

Managing Google Kubernetes Engine workloads

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

Troubleshooting application performance

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

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Revision Books

Let’s now focus on the details of the revision books that will supplement the above training programs well:

• Cloud Native DevOps with Kubernetes: Building, Deploying, and Scaling Modern Applications in the Cloud (1st Edition)

  This book is perfectly designed to cover everything there’s to know about Kubernetes. It is a valuable study material that addresses vital details starting with what Kubernetes is, its origin, and what the future holds for this platform. You will build a high-level knowledge of containers including how they work, how to manage them, and most importantly, the tips for designing cloud-native infrastructure and similar services.

  This guide suits both experienced IT professionals and beginner-level individuals who are only getting started with Kubernetes. Through this resource, candidates will gain important hands-on skills related to the writing and deployment of Kubernetes apps, configuration and operation of Kubernetes clusters, and cloud infrastructure automation using popular tools such as Helm. What’s more, this book will also help you build fundamental knowledge of Kubernetes security, including Role Based Access Control (RBAC). When opting to study using this book, be sure to get the official resource that’s written by John Arundel alongside Justin Domingus.

• Building Secure & Reliable Systems: Best Practices for Designing, Implementing, and Maintaining Systems (1st Edition)

  The authors of this book (Heather Adkins, Piotr Lewandoski, Betsy Beyer, Ana Oprea, and others) created this comprehensive self-study tool with an aim to help candidates learn how to accommodate security and reliability solutions in the lifecycle of systems and software. This material gives explicit knowledge of highly secure systems through coding, debugging, and testing practices, design techniques, and strategies required to address security incidents. Check it out from Amazon and get easy access to one of the most valuable study resources you will ever find for the Google Professional Cloud Developer exam preparation.

For more info visit:

Security & Identity Fundamentals Kubernetes in the Google Cloud Google Cloud documentation Google Cloud solutions

Google Professional-Cloud-Developer Exam Syllabus Topics:

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