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Preparation Sources and Resources

The candidates can find everything they need to prepare for the Google Professional Cloud Developer certification exam on the official website. To help the learners build the required skillset for the qualifying test, Google has developed a variety of tools. The best way to start your preparation process is to review the topics in the study guide. This will provide you with an understanding of what you need to cover while preparing for the exam.

After that, you can proceed with the Cloud Developer learning path. This is an online self-study training tool that includes lectures, demos, and hands-on labs for a better understanding of the Google Cloud products and services. Besides that, the official platform offers sample questions that enable the learners to familiarize themselves with the format of exam questions and content that may be covered in the certification test. To enhance your understanding of the exam domains, refer to the additional resources such as Google Cloud documentation and Google Cloud solutions. The students can also sign up for a subject-related webinar to get valuable tips and insights from the industry experts.

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

• Caching solutions
• Google-recommended practices and documentation
• 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)
• Loosely coupled applications using asynchronous Cloud Pub/Sub events
• Evaluating different services and technologies
• Defining a key structure for high-write applications using Cloud Storage, Cloud Bigtable, Cloud Spanner, or Cloud SQL
• Microservices
• Geographic distribution of Google Cloud services (e.g., latency, regional services, zonal services)
• User session management
• Deploying and securing API services

Designing secure applications

• Google-recommended practices and documentation
• IAM roles for users/groups/service accounts
• 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 protect services and resources
• Security mechanisms that secure/scan application binaries and manifests
• 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)
• Certificate-based authentication (e.g., SSL, mTLS)
• Set compute/workload identity to least privileged access

Managing application data

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

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

• Creating Google Cloud projects
• Emulating Google Cloud services for local application development

Writing code

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

Testing

• Load testing
• Integration testing
• Performance testing

Building

• Creating a Cloud Source Repository and committing code to it
• Developing a continuous integration pipeline using services (e.g., Cloud Build, Container Registry) that construct deployment artifacts
• Reviewing and improving continuous integration pipeline efficacy
• Creating container images from code

3 Deploying applications

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

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

Deploying applications and services on Compute Engine

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

Deploying applications and services to Google Kubernetes Engine (GKE)

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

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

• Downloading and using a service account private key file
• Creating a service account according to the principle of least privilege

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)
• Using the command-line interface (CLI), Google Cloud Console, and Cloud Shell tools
• Read/write data to/from various databases (e.g., SQL, JDBC)
• Writing an application that publishes/consumes data asynchronously (e.g., from Cloud Pub/Sub)
• Storing and retrieving objects from Cloud Storage

Integrating an application with compute services

• Implementing service discovery in Google Kubernetes Engine and Compute Engine
• Using the command-line interface (CLI), Google Cloud Console, and Cloud Shell tools
• Reading instance metadata to obtain application configuration
• Authenticating users by using OAuth2.0 Web Flow and Identity Aware Proxy

Integrating Google Cloud APIs with applications

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

5 Managing Application Performance Monitoring

Managing Compute Engine VMs

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

Managing Google Kubernetes Engine workloads

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

Troubleshooting application performance

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

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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.

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What is the duration, language, and format of Google Professional Cloud Developer Exam

• Passing score: 80%
• Language: English
• Number of Questions: 50-60
• Format: Multiple choices, multiple select
• Recommended experience: 3+ years of industry experience including 1+ years designing and managing solutions using GCP.
• Length of Examination: 240 minutes

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

Google Professional-Cloud-Developer Exam Syllabus Topics: