Professional Cloud Developer Exam Questions

Google Cloud Professional Cloud Developer

Course by CertCraft Institute

The Google Cloud Professional Cloud Developer course by CertCraft Institute is built to help you gain the skills needed to build scalable, secure, and reliable cloud-native applications on Google Cloud. This course follows the official certification blueprint and includes instruction, labs, and practice content to prepare you for the certification exam and real-world developer tasks.

What You’ll Learn

• Develop, deploy, and monitor applications using Google Cloud-native services and APIs

• Manage application performance with Google Cloud Operations Suite (formerly Stackdriver)

• Secure cloud applications using IAM, secrets management, and service identities

• Integrate CI/CD pipelines and automated testing in a GCP development workflow

Requirements / Prerequisites

• Familiarity with at least one programming language (e.g., Python, Java, Go, or Node.js)

• Basic understanding of cloud concepts and software development life cycles

• Prior experience deploying applications or working with APIs is helpful

• Access to a Google Cloud account for hands-on practice

Who This Course Is For

• Developers preparing for the Google Cloud Professional Cloud Developer certification

• Software engineers working with cloud-native applications on GCP

• Backend or full-stack developers transitioning to Google Cloud environments

• DevOps or platform engineers integrating development workflows in cloud systems

Section 1: Designing Highly Scalable, Available, and Reliable Cloud-Native Applications (~36%)

1.1 Designing High-Performing Applications and APIs

• Choose appropriate platforms (e.g., Compute Engine, GKE, Cloud Run)

• Build, refactor, and deploy containers to Cloud Run and GKE

• Understand geographic distribution of Google Cloud services (latency, zones, regions)

• Configure load balancing and session affinity for performance

• Implement caching with Memorystore

• Create and deploy APIs using REST or gRPC

• Use tools like Apigee and Cloud API Gateway for rate limiting, authentication, observability

• Use asynchronous/event-driven approaches (Eventarc, Pub/Sub)

• Optimize applications for cost and resource usage

• Understand zonal/regional failover with data replication

• Use traffic splitting strategies (e.g., A/B testing, gradual rollouts) on Cloud Run or GKE

• Orchestrate services using Workflows, Eventarc, Cloud Tasks, and Cloud Scheduler

1.2 Designing Secure Applications

• Implement data retention with Cloud Storage lifecycle policies

• Use IAP and Web Security Scanner to identify and mitigate vulnerabilities

• Address vulnerabilities flagged by Artifact Analysis and Security Command Center

• Manage secrets, credentials, and encryption keys (Secret Manager, Cloud KMS)

• Authenticate with Application Default Credentials, JWT, OAuth 2.0, Auth Proxies

• Manage end-user accounts with Identity Platform

• Secure access using IAM roles and service accounts

• Secure service-to-service communication (Cloud Service Mesh, Network Policies)

• Apply least-privilege principles

• Use Binary Authorization to secure artifacts

1.3 Storing and Accessing Data

• Select the right storage system based on volume/performance

• Design schemas for structured (AlloyDB, Spanner) and unstructured (Bigtable, Datastore) data

• Understand consistency models for Cloud SQL, Spanner, Bigtable, etc.

• Create signed URLs to grant access to Cloud Storage

• Write data to BigQuery for analytics or AI/ML workloads

Section 2: Building and Testing Applications (~23%)

2.1 Setting Up Development Environment

• Use Google Cloud CLI to emulate services locally

• Work with Cloud Console, SDK, Cloud Code, Cloud Shell, Cloud Workstations

• Leverage Gemini Cloud Assist and Code Assist for development tasks

2.2 Building Applications

• Build and store containers using Cloud Build and Artifact Registry

• Configure provenance using Binary Authorization

2.3 Testing Applications

• Write unit tests (optionally with Gemini Code Assist)

• Run automated integration tests in Cloud Build

Section 3: Deploying Applications (~20%)

3.1 Deploying to Cloud Run

• Deploy from source code to Cloud Run

• Trigger Cloud Run services with Eventarc or Pub/Sub

• Configure event receivers

• Secure APIs using tools like Apigee

• Manage API versions using Cloud Endpoints with backward compatibility

3.2 Deploying to GKE

• Deploy containerized apps to GKE

• Define resource requirements for workloads

• Use Kubernetes health checks for availability

• Configure Horizontal Pod Autoscaler for cost optimization

Section 4: Integrating Applications with Google Cloud Services (~21%)

4.1 Data and Storage Integration

• Manage connections to Cloud SQL, Firestore, Cloud Storage

• Read/write data across Google Cloud datastores

• Build Pub/Sub applications for real-time data streaming

4.2 Consuming Google Cloud APIs

• Enable and call Google Cloud services via Cloud Client Libraries, REST, gRPC, API Explorer

• Handle batching, pagination, caching, and error retries (e.g., exponential backoff)

• Use service accounts securely when calling APIs

4.3 Troubleshooting and Observability

• Instrument apps with metrics, logs, traces (Cloud Monitoring/Logging/Trace)

• Diagnose and resolve issues using Google Cloud Observability tools

• Track and manage errors with Error Reporting

• Use trace IDs to follow issues across services

• Get AI-assisted help with Gemini Cloud Assist