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Building & Testing Apps

Testing: Here, an applicant needs to have the skills in unit testing, performance testing, integration testing, and load testing.
Set up a Local Development Environment: This subsection measures your skills in creating different Google Cloud projects, using developing tools and command-line interfaces, Cloud Shell tools, and Google Cloud Console, as well as imitating Google Cloud services for the development of the local application.
Building: It covers the skills in managing source control, improving and reviewing continuous efficiency in the integration pipeline, as well as creating different secure container images through code.
Write Efficient Code: The potential candidates should understand algorithm design, software development methodologies, modern app patterns, and debugging & profiling code.

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

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)
Graceful shutdown on platform termination
Microservices
Loosely coupled applications using asynchronous Cloud Pub/Sub events
Defining a key structure for high-write applications using Cloud Storage, Cloud Bigtable, Cloud Spanner, or Cloud SQL
Deploying and securing API services
Caching solutions
Google-recommended practices and documentation

Designing secure applications

Storing and rotating application secrets using Cloud KMS
Security mechanisms that secure/scan application binaries and manifests
Set compute/workload identity to least privileged access
Security mechanisms that protect services and resources
Securing service-to-service communications (e.g., service mesh, Kubernetes network policies, and Kubernetes namespaces)
Certificate-based authentication (e.g., SSL, mTLS)
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)
IAM roles for users/groups/service accounts
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)
Choosing data storage options based on use case considerations, such as:
Data volume
Frequency of data access in Cloud Storage
Structured vs. unstructured data
Strong vs. eventual consistency
Cloud Storage-signed URLs for user-uploaded content
Following Google-recommended practices and documentation

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

Algorithm design
Modern application patterns
Efficiency
Unit testing
Agile software development

Testing

Performance testing
Load testing
Integration testing

Building

Reviewing and improving continuous integration pipeline efficacy
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

3 Deploying applications

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

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

Deploying applications and services on Compute Engine

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

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
Defining workload specifications (e.g., resource requirements)
Building a container image using Cloud Build
Managing Kubernetes RBAC and Google Cloud IAM relationship
Configuring Kubernetes namespaces and access control
Define deployments, services, and pod configurations

Deploying a Cloud Function

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
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

Read/write data to/from various databases (e.g., SQL, JDBC)
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
Storing and retrieving objects from Cloud Storage
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
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

Using service accounts to make Google API calls
Paginating results
Error handling (e.g., exponential backoff)
Caching results
Batching requests
Restricting return data
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