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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

• Geographic distribution of Google Cloud services (e.g., latency, regional services, zonal services)
• Caching solutions
• Loosely coupled applications using asynchronous Cloud Pub/Sub events
• 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
• 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
• Graceful shutdown on platform termination
• Microservices
• Google-recommended practices and documentation

Designing secure applications

• Security mechanisms that secure/scan application binaries and manifests
• Certificate-based authentication (e.g., SSL, mTLS)
• 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
• Implementing requirements that are relevant for applicable regulations (e.g., data wipeout)
• Authenticating to Google services (e.g., application default credentials, JWT, OAuth 2.0)
• Set compute/workload identity to least privileged access
• Google-recommended practices and documentation

Managing application data

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

Refactoring applications to migrate to Google Cloud

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

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

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

Testing

• Performance testing
• Load testing
• Integration testing

Building

• 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

3 Deploying applications

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

• Canary deployments
• Blue/green deployments
• Traffic-splitting deployments
• Rolling deployments

Deploying applications and services on Compute Engine

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

Deploying applications and services to Google Kubernetes Engine (GKE)

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

Deploying a Cloud Function

• Cloud Functions that are invoked via HTTP
• Cloud Functions that are triggered via an event (e.g., Cloud Pub/Sub events, Cloud Storage object change notification events)
• 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

• Storing and retrieving objects from Cloud Storage
• Read/write data to/from various databases (e.g., SQL, JDBC)
• Using the command-line interface (CLI), Google Cloud Console, and Cloud Shell tools
• 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)

Integrating an application with compute services

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

Integrating Google Cloud APIs with applications

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

5 Managing Application Performance Monitoring

Managing Compute Engine VMs

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

Managing Google Kubernetes Engine workloads

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

Troubleshooting application performance

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

Important Details

The candidates for this certification are the Cloud developers who have competence in building highly available and scalable apps with the Google-recommended tools and practices. They also have proficiency in a minimum of one general-purpose programming language. It is recommended that the individuals for the Professional Cloud Developer certificate have at least three years of practical experience with Google Cloud solutions and products. They should also possess at least one year of designing and managing solutions with the use of GCP. Please note that you should also understand the content of the prerequisite exam before attempting it.

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

Managing Application Performance Monitoring

• Manage Compute Engine Virtual Machines: It covers the applicants’ skills in analyzing & viewing logs; debugging custom VM images with serial ports, sending logs to Cloud Logging from VMs, and inspecting resource usage over time.
• Troubleshoot Application Performance: This one covers the skills of the test takers in using Cloud Debugger, creating and writing custom and log-based metrics, exporting the logs from GC, and using forums, Google Cloud support, and documentation, among others.
• Manage Workloads for the Google Kubernetes Engine: The considerations for this topic include configuring monitoring & logging, viewing & analyzing logs, configuring workload auto-scaling, writing & exporting custom metrics, and analyzing the lifecycle events of a container.

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.