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

Designing secure applications

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

Managing application data

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

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

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

Writing code

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

Testing

• Load testing
• Integration 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:

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

Deploying applications and services on Compute Engine

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

Deploying applications and services to Google Kubernetes Engine (GKE)

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

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

• 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)
• 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

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

5 Managing Application Performance Monitoring

Managing Compute Engine VMs

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

Managing Google Kubernetes Engine workloads

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

Troubleshooting application performance

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

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.

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