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

Designing secure applications

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

Managing application data

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

Refactoring applications to migrate to Google Cloud

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

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

Testing

• Integration testing
• Performance testing
• Load testing

Building

• Creating container images from code
• 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
• 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
• Rolling deployments
• Traffic-splitting deployments

Deploying applications and services on Compute Engine

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

Deploying applications and services to Google Kubernetes Engine (GKE)

• Configuring application accessibility to user traffic and other services
• Managing container lifecycle
• 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)
• Configuring Kubernetes namespaces and access control

Deploying a Cloud Function

• Securing Cloud Functions
• 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)

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

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

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

• Caching results
• Batching requests
• Paginating results
• Restricting return data
• Error handling (e.g., exponential backoff)
• Enabling a Google Cloud API
• 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

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

Managing Google Kubernetes Engine workloads

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

Troubleshooting application performance

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

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

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Section 5: Managing Application Performance Monitoring

The last module of the certification test focuses on the ability of the applicants to perform the following technical tasks: managing Compute Engine Virtual Machines (VMs), managing Google Kubernetes Engine workloads, as well as troubleshooting the application performance.

Some of the questions included in the Google Professional Cloud Developer certification exam may guide you to a case study describing a fictitious solution and business concept. This will provide you with additional context to choose the right answer(s). You can get an idea of a possible case study by studying the example contained in the official study guide.

How to Prepare For Google Professional Cloud Developer Exam

Preparation Guide for Google Professional Cloud Developer Exam

Introduction

Google has designed different Role Based Certifications for the IT professionals to validate their skillset one among them is Google Professional Cloud Developer. This program gives Google cloud professionals a way to demonstrate their skills. The evaluation relies on a meticulous exam using industry standard methodology to validate aspirants proficiency standards to meet Google's proficiency standards.

According to Google, a Google Certified Professional Cloud Developer facilitate organizations to influence Google Cloud technologies. With a thorough understanding of cloud developer and Google Cloud Platform, this individual can design, develop, and manage robust, secure, scalable, highly available, and dynamic solutions to drive business objectives.

In this guide, we will cover the Google Professional Cloud Developer Exam, Google Professional Cloud Developer Certified Professionals salary and all aspects of the Google Professional Cloud Developer Certification.

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The Google Professional Cloud Developer certificate is intended to validate the competence of IT professionals tasked with building highly available and scalable applications with the aid of Google endorsed practices and tools. Such individuals will have to demonstrate an understanding of developer tools, cloud-native applications, modern databases, and managed services during the corresponding exam. What’s more, targeted specialists need to have background experience working with at least one programming language and should be proficient in creating useful metrics and logs for debugging and trace coding.