How to study the Google Professional Cloud Developer Exam

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How to Prepare For Google Professional Cloud Developer Exam

Preparation Guide for Google Professional Cloud Developer Exam

Introduction

Google has designed different Role Based Certifications for the IT professionals to validate their skillset one among them is Google Professional Cloud Developer. This program gives Google cloud professionals a way to demonstrate their skills. The evaluation relies on a meticulous exam using industry standard methodology to validate aspirants proficiency standards to meet Google's proficiency standards.

According to Google, a Google Certified Professional Cloud Developer facilitate organizations to influence Google Cloud technologies. With a thorough understanding of cloud developer and Google Cloud Platform, this individual can design, develop, and manage robust, secure, scalable, highly available, and dynamic solutions to drive business objectives.

In this guide, we will cover the Google Professional Cloud Developer Exam, Google Professional Cloud Developer Certified Professionals salary and all aspects of the Google Professional Cloud Developer Certification.

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

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Google Professional-Cloud-Developer Exam Syllabus Topics:

Section	Weight	Objectives
Configuring cloud-native applications for deployment	24%	- Deploying applications to Google Kubernetes Engine 1. Creating and managing workloads and services 2. Managing configurations and secrets 3. Configuring scaling, auto-scaling, and updates - Deploying to App Engine or Cloud Functions 1. Configuring serverless deployments 2. Managing versions and traffic splitting - Deploying applications to Cloud Run 1. Configuring scaling, networking, and security 2. Integrating with event-driven services 3. Deploying from source code or containers
Designing highly scalable, secure, and reliable cloud-native applications	32%	- Designing secure applications 1. Applying security best practices and compliance requirements 2. Implementing authentication and authorization mechanisms 3. Securing data in transit and at rest - Designing for observability and monitoring 1. Designing for debugging and troubleshooting 2. Planning logging, metrics, and tracing strategies - Designing high-performing applications and APIs 1. Designing for high availability and fault tolerance 2. Selecting appropriate compute and deployment platforms 3. Optimizing for latency and throughput
Integrating applications with Google Cloud services	21%	- Implementing application performance monitoring 1. Instrumenting code for metrics and traces 2. Using operations suite tools - Consuming Google Cloud APIs and services 1. Integrating with AI/ML and generative AI services 2. Enabling and managing APIs 3. Using client libraries, REST, and gRPC - Integrating with data and storage services 1. Working with relational, NoSQL, and in-memory databases 2. Using object storage and file storage solutions 3. Implementing messaging and event streaming
Building and testing applications	23%	- Setting up development environments 1. Emulating Google Cloud services locally 2. Using Cloud SDK, Cloud Code, Cloud Shell, and Cloud Workstations - Testing applications 1. Writing unit, integration, and end-to-end tests 2. Automating testing in CI/CD workflows 3. Using AI-assisted development tools - Building applications and containers 1. Using Cloud Build and Artifact Registry 2. Implementing containerization best practices 3. Configuring build pipelines and provenance