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. Your application on Cloud Run needs to call a third-party API that requires a static IP address for allowlist configuration. Cloud Run instances use dynamic IP addresses. How should you enable Cloud Run to access the third-party API?
AConfigure Cloud Run with a Serverless VPC Access connector and route traffic through Cloud NAT with a static external IP
BConfigure Cloud Armor to provide a static IP for outbound Cloud Run traffic
CDeploy the application on GKE instead of Cloud Run to control egress IP addresses
DUse an HTTP proxy on Compute Engine with a static IP and configure Cloud Run to route requests through the proxy
Explanation
Serverless VPC Access connector allows Cloud Run to access resources in a VPC network. Configuring Cloud NAT with a static external IP for the VPC subnet provides a consistent egress IP that can be allowlisted by the third-party API. This is the recommended Google Cloud approach for static IP requirements with serverless services. Migrating to GKE for static IP alone is unnecessary complexity. An HTTP proxy works but adds a single point of failure and operational overhead. Cloud Armor is for inbound traffic protection, not egress IP management.
2. A retail company's checkout service experiences transaction failures during high-traffic periods. Investigation shows Cloud SQL connection pool exhaustion causing timeouts. The service runs 50 replicas on GKE during peak hours, each opening 10 database connections. The Cloud SQL instance has a maximum connection limit of 100. How should you resolve this issue?
AImplement connection pooling in the application using a library like HikariCP with a maximum pool size of 2 connections per replica
BAdd a Cloud SQL read replica and distribute read queries across primary and replica instances
CUpgrade the Cloud SQL instance to a larger machine type with higher connection limits
DEnable Cloud SQL high availability to double the available connection capacity
Explanation
Implementing proper connection pooling with a small pool size per replica prevents connection exhaustion. With 50 replicas and 2 connections each, total connections would be 100, staying within limits. Connection pooling reuses existing connections efficiently rather than creating new connections for each request. Upgrading the Cloud SQL instance provides more connections but doesn't address the inefficient connection usage pattern. Cloud SQL high availability provides redundancy but doesn't increase connection limits on the primary instance. Read replicas help with read scalability but don't solve connection exhaustion on the primary instance handling write transactions during checkout.
3. A real-time collaboration tool requires sub-100ms latency for users in Singapore, Sydney, and Tokyo. The application state must be consistent across all regions. Which database deployment strategy meets these requirements?
ADeploy Cloud SQL for PostgreSQL with cross-region read replicas in each region
BDeploy Memorystore for Redis clusters in each region with application-level synchronization
CDeploy Cloud Spanner with asia-southeast1, australia-southeast1, and asia-northeast1 as read-write regions
DDeploy Firestore in nam5 multi-region configuration with global strong consistency
Explanation
Cloud Spanner configured with read-write replicas in all three regions provides strong consistency with low-latency reads and writes. Each region can serve reads locally for sub-100ms latency while synchronous replication ensures consistency across regions. The multi-region configuration handles network partitions gracefully while maintaining ACID guarantees. Cloud SQL read replicas use asynchronous replication that cannot guarantee strong consistency and read replicas cannot accept writes, limiting functionality. Firestore in nam5 serves North America, not the Asia-Pacific regions needed. Memorystore clusters lack built-in cross-region replication and require complex application logic for synchronization and conflict resolution.
4. Your application needs to paginate results from a BigQuery table containing 100 million rows. Users should be able to navigate forward and backward through pages. Which approach provides the best performance?
AUse query result caching with pagination in application code
BUse BigQuery sessions to maintain query state between page requests
CImplement cursor-based pagination using row IDs
DUse OFFSET and LIMIT clauses with page number calculation
Explanation
Cursor-based pagination using row IDs or timestamps provides consistent performance regardless of page depth. Each query filters rows greater than the last seen cursor value, avoiding the performance degradation of OFFSET with large values. OFFSET requires scanning all previous rows even when paginating to later pages. Query result caching helps but does not address the fundamental inefficiency of OFFSET. BigQuery sessions maintain connection state but do not solve pagination performance issues.
5. A real-time analytics dashboard queries BigQuery every 30 seconds displaying sales metrics for the current day. The 5 TB table is partitioned by date. Each dashboard query scans the current day's partition (2 GB) costing $0.01 per query. With 100 concurrent users, query costs are $50/hour. How can they reduce query costs?
ACreate a materialized view that incrementally updates with new sales data
BSwitch from on-demand to capacity-based pricing with slot reservations
CAdd clustering on metrics columns to reduce data scanned per query
DImplement client-side caching with 30-second TTL to reduce query frequency
Explanation
Materialized views in BigQuery incrementally update as new data arrives, allowing dashboard queries to scan only the materialized view which is much smaller and faster than scanning the partition. Incremental updates cost less than full partition scans. Query cost shifts from per-query scanning to incremental maintenance, dramatically reducing costs for frequently repeated queries. Materialized views are optimized for this exact scenario of dashboard queries on frequently updated data. Capacity-based pricing provides cost predictability but doesn't reduce the actual data scanning - queries still process 2 GB. With 100 users querying every 30 seconds, slot usage would be high. Client-side caching reduces query frequency but creates stale data problems when sales update in real-time. Dashboard users expect current data. Clustering within the daily partition helps but queries still scan most of the 2 GB partition as dashboard metrics typically aggregate across many dimensions. Materialized views provide pre-aggregated results at lower cost.
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