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

• Microservices
• Graceful shutdown on platform termination
• Defining a key structure for high-write applications using Cloud Storage, Cloud Bigtable, Cloud Spanner, or Cloud SQL
• User session management
• Google-recommended practices and documentation
• Geographic distribution of Google Cloud services (e.g., latency, regional services, zonal services)
• 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
• Caching solutions
• Evaluating different services and technologies
• Deploying and securing API services

Designing secure applications

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

Managing application data

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

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

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

Testing

• Load testing
• Performance testing
• Integration testing

Building

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

3 Deploying applications

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

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

Deploying applications and services on Compute Engine

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

Deploying applications and services to Google Kubernetes Engine (GKE)

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

Deploying a Cloud Function

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

• 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)
• Storing and retrieving objects from Cloud Storage
• Connecting to a data store (e.g., Cloud SQL, Cloud Spanner, Cloud Firestore, Cloud Bigtable)
• Read/write data to/from various databases (e.g., SQL, JDBC)

Integrating an application with compute services

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

Integrating Google Cloud APIs with applications

• Restricting return data
• Enabling a Google Cloud API
• Batching requests
• Using service accounts to make Google API calls
• Caching results
• Error handling (e.g., exponential backoff)
• Paginating results
• 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
• Analyzing logs
• Debugging a custom VM image using the serial port
• 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

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

Troubleshooting application performance

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

Target Audience and Prerequisites

The Google Professional Cloud Developer certification exam is intended for those App Developers who are involved in designing and building applications to run on Google Cloud Platform. These specialists should be familiar with the Google Cloud Platform products, such as Cloud Storage, Compute Engine, Security Key Enforcement, as well as BigQuery.

There are no obligatory requirements that the candidates need to meet to become eligible for the qualifying test. Nevertheless, the target individuals are recommended to have three or more years of industry experience, including one or more years of experience in designing and managing solutions using Google Cloud. Besides that, the students need to develop a comprehensive understanding of the exam topics.

Google Professional Cloud Developer Practice Test Questions, Google Professional Cloud Developer Exam Practice Test Questions

The Professional Cloud Developer certificate validates the skills of the interested candidates in building highly available and scalable applications with the use of the tools and practices recommended by Google. The potential applicants for this certification must demonstrate practical experience with developer tools, Cloud-native applications, next-gen databases, and managed services. They also have the expertise in at least one programming language and can develop meaningful logs and metrics to trace and debug code. Those individuals pursuing this option must pass one qualifying exam.

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

Managing Application Performance Monitoring

• 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 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.
• 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.