Google Cloud · PCD
Google Cloud Certified - Professional Cloud Developer (PCD) Practice Tests
Validates expertise in building and deploying scalable, secure, and highly available applications using Google Cloud tools and best practices.
Exam Details
Practice Questions
600
≈ 12 practice exams
Duration
120 minutes
Passing Score
Not disclosed
Difficulty
ProfessionalLast Updated
Jan 2026
Google Cloud Certified - Professional Cloud Developer (PCD) Practice Exam Preparation
Use this PCD practice exam to prepare for Google Cloud Certified - Professional Cloud Developer (PCD) with realistic questions, detailed explanations, and focused study modes. The practice bank includes 600 questions for Google Cloud PCD, so you can review the exam steadily instead of relying on one long cram session.
As you practice, pay extra attention to patterns in your missed answers. Start with short sessions to identify weak areas, then move into timed quizzes once your accuracy is consistent.
The explanations are especially useful when you want to connect exam wording to the responsibilities and scenarios described in the official certification guidance. Use the free preview first, then unlock the full question bank when you are ready to build a complete study routine.
Exam Domain Breakdown
Designing Highly Scalable, Available, and Reliable Cloud-Native Applications25%
Building and Testing Applications20%
Deploying Applications20%
Integrating Google Cloud Services20%
Managing Application Performance15%
Exam Overview
The Google Cloud Certified Professional Cloud Developer (PCD) certification validates a practitioner's ability to design, build, test, deploy, and manage scalable, secure, and highly available cloud-native applications on Google Cloud. The exam covers the full application development lifecycle on Google Cloud, assessing proficiency with compute platforms such as Cloud Run, Google Kubernetes Engine (GKE), App Engine, and Cloud Functions, as well as managed data stores including Cloud SQL, AlloyDB, Spanner, Firestore, Bigtable, and Cloud Storage. Candidates must demonstrate fluency in integrating Google Cloud services—such as Pub/Sub, Eventarc, Cloud Tasks, Cloud Scheduler, API Gateway, and Cloud Endpoints—into event-driven and API-centric architectures.
The certification also tests knowledge of modern DevOps practices, including CI/CD pipeline construction with Cloud Build and Cloud Deploy, container image management via Artifact Registry, supply chain security with Binary Authorization, and application security using IAM, Identity-Aware Proxy, Secret Manager, and Cloud KMS. Observability skills—covering Cloud Monitoring, Cloud Logging, Cloud Trace, and Cloud Profiler—are also assessed, reflecting Google Cloud's emphasis on instrumenting applications to produce actionable metrics, logs, and traces.
Who Should Take This Exam
This certification is designed for software engineers and developers who build and deploy applications on Google Cloud as part of their day-to-day responsibilities. Ideal candidates have hands-on experience with cloud-native patterns such as microservices, containerization, and serverless computing, and are comfortable with at least one general-purpose programming language. The exam is equally relevant to full-stack developers, backend engineers, and DevOps engineers who own the deployment and operational health of cloud-hosted applications.
Professionals transitioning from on-premises or other cloud environments who want to formalize their Google Cloud development skills will also benefit. Google recommends candidates have at least three years of industry experience, including one or more years of designing and managing solutions on Google Cloud.
Prerequisites
Google Cloud does not enforce formal prerequisites for the Professional Cloud Developer exam; any candidate may register directly. However, Google recommends a minimum of three years of industry software development experience, with at least one year spent designing and managing solutions on Google Cloud. Candidates should be comfortable working with cloud-native application architectures, container orchestration, RESTful and gRPC APIs, managed databases, and serverless platforms before attempting the exam.
A strong practical foundation is more valuable than theoretical study alone. Candidates without significant hands-on Google Cloud experience are advised to complete the Professional Cloud Developer learning path on Google Cloud Skills Boost, which includes guided labs covering Cloud Run, GKE, Cloud Build, Cloud Functions, and core data services before sitting for the exam.
Exam Format
The Professional Cloud Developer exam consists of 50–60 multiple-choice and multiple-select questions and must be completed within 120 minutes. The registration fee is $200 USD plus applicable taxes. The exam is available in English and Japanese and can be taken either via online proctoring (remote, from any location with a webcam and stable internet connection) or in person at an authorized Kryterion testing center worldwide.
Google does not publicly disclose a specific passing score for this exam. Scoring uses a compensatory model in which overall performance across all domains determines the outcome, rather than requiring minimum scores in each individual section. The certification is valid for two years, after which candidates must recertify through the renewal process.
Skills Measured
- 1.Domain 1 – Designing Highly Scalable, Available, and Reliable Cloud-Native Applications (~25%): Covers selecting appropriate compute platforms (App Engine, Cloud Run, GKE, Cloud Functions), designing microservices architectures, applying high-availability and disaster recovery patterns, choosing data storage technologies (Cloud SQL, Firestore, Bigtable, Spanner, Cloud Storage), implementing caching with Memorystore, and designing event-driven systems using Pub/Sub and Eventarc.
- 2.Domain 2 – Building and Testing Applications (~20%): Covers creating and managing container images with Cloud Build and Artifact Registry, implementing unit, integration, and end-to-end test strategies, building CI/CD pipelines with Cloud Build and Cloud Deploy, applying software supply chain security controls (Binary Authorization, provenance attestation), and configuring development environments for Google Cloud.
- 3.Domain 3 – Deploying Applications (~20%): Covers deploying workloads to App Engine (Standard and Flexible environments), Cloud Run, GKE, and Cloud Functions; executing deployment strategies including blue-green, canary, and rolling releases; managing traffic splitting and version control; configuring IAM, service accounts, and Secret Manager for secure deployments; and enforcing security and compliance policies during release.
- 4.Domain 4 – Integrating Google Cloud Services (~20%): Covers asynchronous messaging with Pub/Sub, deferred job processing with Cloud Tasks, scheduled execution with Cloud Scheduler, API exposure and management via API Gateway and Cloud Endpoints, service mesh configuration, and application security using Identity-Aware Proxy (IAP) and VPC Service Controls.
- 5.Domain 5 – Managing Application Performance (~15%): Covers creating dashboards and alerts in Cloud Monitoring, analyzing and routing logs with Cloud Logging, implementing distributed tracing with Cloud Trace, profiling application performance using Cloud Profiler, tracking errors with Error Reporting, and defining and measuring SLIs and SLOs for production services.
Study Tips
- Start with the official exam guide (downloadable PDF linked from cloud.google.com/learn/certification/cloud-developer) and map every subtopic to a hands-on lab or Google Cloud documentation page before studying anything else.
- Complete the Professional Cloud Developer learning path on Google Cloud Skills Boost. The path includes Qwiklabs that let you deploy real workloads to Cloud Run, GKE, and App Engine—experience that directly mirrors scenario-based exam questions.
- Practice reading and interpreting code snippets. The exam presents architecture scenarios and code fragments; use the official sample questions to calibrate how deeply the exam tests actual implementation knowledge versus conceptual understanding.
- Build a personal decision framework for compute platform selection: know precisely when to choose Cloud Run versus GKE versus App Engine Standard/Flexible versus Cloud Functions, including the trade-offs in cost, operational overhead, scaling behavior, and concurrency model.
- Focus dedicated study time on CI/CD and supply chain security topics—Cloud Build, Cloud Deploy, Artifact Registry, Binary Authorization, and software provenance—as these reflect recent updates to the exam guide (revised March 2025) and are heavily weighted in the Building and Testing domain.
- Use Google Cloud's official documentation for each managed service (Pub/Sub, Cloud Tasks, Eventarc, Secret Manager, Cloud KMS) rather than relying solely on third-party summaries. The exam tests nuanced configuration details that are only accurately described in official docs.
- Review Cloud Monitoring, Cloud Logging, and Cloud Trace documentation together as a unified observability stack. Practice writing log-based metrics, configuring uptime checks, and interpreting trace waterfall diagrams, as the Managing Application Performance domain tests these skills in integrated scenarios.
Career Benefits
The Professional Cloud Developer certification is recognized by organizations that run production workloads on Google Cloud, making it a strong differentiator for software engineers, DevOps practitioners, and cloud architects operating in GCP environments. Certified professionals typically pursue roles such as Cloud Application Developer, Site Reliability Engineer, Cloud Solutions Engineer, and DevOps Engineer. The certification demonstrates mastery of the full cloud-native development lifecycle—from architecture design through deployment and observability—which aligns with the skillsets most in demand at enterprises undergoing cloud modernization.
Salary data from multiple industry sources places certified Google Cloud Professional Cloud Developer professionals in the $140,000–$180,000 USD range in the United States, varying by role, seniority, and geography. Compared to associate-level Google Cloud certifications, the PCD signals hands-on production experience and is often weighted alongside certifications like the AWS Certified Developer – Associate or Microsoft Azure Developer Associate, though the PCD's scope—covering security, CI/CD pipelines, and multi-service integration in depth—is generally considered broader.
Sample Questions
5 sample questions with answers and explanations. The full bank has 600 questions, enough for 12 full-length practice exams.
Preview — answers shown1. A REST API experiences high latency on queries that fetch nested resources (users with their orders, orders with line items and products). Clients often request only specific fields. You're considering switching to GraphQL. What is the primary performance benefit GraphQL would provide in this scenario?
AGraphQL eliminates over-fetching by allowing clients to specify exactly which fields they need, reducing payload size and number of round trips for nested resources
BGraphQL uses HTTP/2 multiplexing while REST uses HTTP/1.1
CGraphQL queries execute faster than REST queries because of its optimized query parser
DGraphQL automatically caches responses better than REST APIs
Explanation
GraphQL's primary performance advantage is solving the over-fetching and under-fetching problems common in REST APIs. Clients request exactly the fields they need in a single query, even across nested relationships. The server returns precisely that data in one round trip, eliminating multiple REST calls to fetch related resources. GraphQL doesn't provide automatic caching advantages - caching is actually more challenging than REST due to dynamic queries. Both GraphQL and REST can use HTTP/2. Query parsing overhead is negligible compared to database operations - the performance benefit comes from reducing data transfer and round trips.
2. A development team uses Artifact Registry to store Docker images. They need to cache dependencies from Docker Hub while maintaining control over what versions are available to developers and enabling vulnerability scanning of third-party images. What type of Artifact Registry repository should they configure?
ACreate a remote repository pointing to Docker Hub, which caches images and enables vulnerability scanning
BCreate a virtual repository that combines multiple standard repositories
CCreate a standard repository and manually copy images from Docker Hub using docker pull and push
DUse a standard repository and configure Cloud Build to download from Docker Hub during builds
Explanation
Remote repositories in Artifact Registry cache dependencies from upstream public sources like Docker Hub while enabling vulnerability scanning and providing version control. This gives teams greater control over third-party dependencies and allows security scanning of external images before use. Standard repositories store images but do not automatically cache from external sources. Manually copying images introduces operational overhead and does not automate caching. Virtual repositories group multiple repositories but do not cache external sources. Configuring Cloud Build to download directly from Docker Hub does not provide caching, scanning, or version control benefits.
3. Your organization is implementing a hybrid cloud architecture connecting on-premises data centers with Google Cloud. Applications require sub-10ms latency between environments and must transfer 50TB monthly. Security policies mandate private connectivity without internet exposure. Which connectivity solution should you implement?
ACloud VPN with high-availability configuration and multiple tunnels for bandwidth
BPartner Interconnect through a supported service provider with redundant connections
CDedicated Interconnect with 10 Gbps connection and Cloud Router for dynamic routing
DDirect Peering at Google edge locations for optimized network paths
Explanation
Dedicated Interconnect provides the low-latency private connectivity required for hybrid architectures with sub-10ms requirements. The 10 Gbps bandwidth easily handles 50TB monthly transfers, and Cloud Router enables dynamic BGP routing for optimal path selection. This solution meets all requirements for latency, bandwidth, and private connectivity. Cloud VPN cannot achieve sub-10ms latency due to encryption overhead and internet routing, despite high-availability configurations. Partner Interconnect adds another party and potential latency, though it could work if Dedicated Interconnect isn't available at the location. Direct Peering is for Google Workspace and YouTube, not for private Google Cloud resource access.
4. A GKE workload uses Workload Identity to access Cloud Storage. After enabling a new security policy, pods receive 'Permission denied' errors when accessing buckets. The Kubernetes Service Account is correctly annotated with the Google Service Account email, and the IAM binding exists. What should you verify?
ACheck if the Google Service Account has the iam.workloadIdentityUser role binding for the Kubernetes Service Account in the format serviceAccount:PROJECT_ID.svc.id.goog[NAMESPACE/KSA_NAME]
BVerify the GKE cluster has Workload Identity enabled at the cluster level
CConfirm the pods are using the correct Kubernetes Service Account in their pod specification
DAll of the above - Workload Identity requires correct configuration at cluster, service account, and pod levels
Explanation
Workload Identity requires configuration at three levels: cluster level (Workload Identity enabled), service account level (IAM binding granting iam.workloadIdentityUser role to the KSA in the correct format), and pod level (pods must use the annotated KSA). A failure at any level causes permission denied errors. After enabling a new security policy, any of these could have been affected. Checking only the IAM binding misses potential cluster or pod configuration issues. Verifying only cluster level misses service account binding or pod configuration problems. Checking only pod configuration misses cluster or IAM binding issues.
5. A development team is implementing blue-green deployment for a Cloud Run service. They want to deploy the new version (green) alongside the current version (blue), test it with internal traffic, and then switch all production traffic to green instantly. How should they implement this strategy?
ADeploy the new revision with --no-traffic flag, test it, then delete the blue revision
BCreate a separate Cloud Run service for the green version and use Cloud Load Balancing to switch traffic
CDeploy a new revision with tag 'green', send test traffic to the tagged URL, then use 'gcloud run services update-traffic' to set green to 100%
DUse Cloud Deploy's automated canary deployment with a single phase of 100%
Explanation
Cloud Run's revision tagging feature enables blue-green deployments by allowing multiple revisions to run simultaneously with specific tags. Deploying with tag 'green' creates a dedicated URL (green---service-hash-region.run.app) for testing the new revision without receiving production traffic. After testing succeeds, 'gcloud run services update-traffic --to-revisions=green=100' instantly switches all traffic to the green revision. This provides zero-downtime deployment with the ability to instantly rollback by switching traffic back to blue if issues arise. Both revisions run concurrently during the testing phase. Creating a separate Cloud Run service requires managing multiple services and more complex load balancing configuration, defeating Cloud Run's built-in traffic management. Cloud Deploy's canary deployment gradually shifts traffic in phases rather than the instant cutover blue-green requires. Deleting the blue revision removes the rollback capability - blue-green deployments keep both versions running to enable instant rollback. The tag-based approach leverages Cloud Run's native traffic management and provides clean URL separation for testing without affecting production traffic.
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