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.

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

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

Designing secure applications

Security mechanisms that protect services and resources
Certificate-based authentication (e.g., SSL, mTLS)
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)
IAM roles for users/groups/service accounts
Storing and rotating application secrets using Cloud KMS
Set compute/workload identity to least privileged access
Securing service-to-service communications (e.g., service mesh, Kubernetes network policies, and Kubernetes namespaces)
Google-recommended practices and documentation
Security mechanisms that secure/scan application binaries and manifests

Managing application data

Structured vs. unstructured data
Data volume
Frequency of data access in Cloud Storage
Following Google-recommended practices and documentation
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)
Choosing data storage options based on use case considerations, such as:
Strong vs. eventual consistency

Refactoring applications to migrate to Google Cloud

Using managed services
Google-recommended practices and documentation
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

Efficiency
Modern application patterns
Algorithm design
Unit testing
Agile software development

Testing

Performance testing
Load testing
Integration 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:

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

Deploying applications and services on Compute Engine

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

Deploying applications and services to Google Kubernetes Engine (GKE)

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

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

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)
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

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

Restricting return data
Error handling (e.g., exponential backoff)
Making API calls with a Cloud Client Library, the REST API, or the APIs Explorer, taking into consideration:
Enabling a Google Cloud API
Caching results
Batching requests
Paginating results
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
Debugging a custom VM image using the serial port
Analyzing logs
Analyzing a failed Compute Engine VM startup
Viewing syslogs from a VM
Inspecting resource utilization over time

Managing Google Kubernetes Engine workloads

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

Troubleshooting application performance

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

How much Google Professional Cloud Developer Exam cost

Google Professional Cloud Developer exam cost is $120 USD(40% discount on retail price of $200 USD).

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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.

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