Google Cloud · PCD
Validates expertise in building and deploying scalable, secure, and highly available applications using Google Cloud tools and best practices.
Practice Questions
600
≈ 12 practice exams
Duration
120 minutes
Passing Score
Not disclosed
Difficulty
ProfessionalLast Updated
Jan 2026
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.
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.
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.
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.
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.
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.
5 sample questions with answers and explanations. The full bank has 600 questions, enough for 12 full-length practice exams.
Preview — answers shown1. A data pipeline must read CSV files from Cloud Storage, transform data, and load into BigQuery. Files arrive throughout the day ranging from 100MB to 10GB. The pipeline must handle schema evolution. Which solution provides the most maintainable implementation?
Explanation
Dataflow with Apache Beam provides declarative pipeline definition with built-in transformations and BigQuery schema evolution support. Beam handles files of varying sizes efficiently with autoscaling workers. Schema auto-detection adapts to schema changes without manual intervention. The pipeline is testable and maintainable. BigQuery Data Transfer Service supports direct file loads but lacks custom transformation capabilities. Cloud Functions work for small files but struggle with 10GB files due to execution time limits. Cloud Composer adds orchestration overhead when Dataflow provides the necessary capabilities.
2. A global e-commerce platform serves traffic from Asia, Europe, and North America. They deploy a Cloud CDN in front of Cloud Storage buckets containing product images. Cache hit rates are only 40%, causing high origin requests and egress costs. What CDN configuration change would most improve cache hit rates?
Explanation
Product image URLs often include query parameters for variants, tracking, or personalization that create unique cache keys for identical images. Configuring custom cache keys to exclude unnecessary query parameters allows different URLs to share cached responses, dramatically improving hit rates. For example, excluding tracking parameters means image.jpg?utm_source=email and image.jpg?utm_source=social serve the same cached image. This single change can increase hit rates from 40% to 80%+ in e-commerce scenarios. FORCE_CACHE_ALL already caches all content but doesn't address the cache key fragmentation causing low hit rates. Request collapsing reduces origin load when multiple users simultaneously request the same uncached object, but doesn't improve cache hit rates for subsequent requests. Increasing TTL helps cache entries stay valid longer but doesn't address the fundamental problem of cache key fragmentation creating multiple entries for the same content. Custom cache keys solve the root cause of poor hit rates.
3. A multi-tenant SaaS application serves hundreds of customers from shared GKE infrastructure. Each customer's data must be isolated and the application must scale resources based on per-tenant usage. How should you implement tenant isolation and resource management?
Explanation
Kubernetes namespaces provide logical isolation for multi-tenant applications. ResourceQuotas limit total resource consumption per namespace (tenant), while LimitRanges control individual container resources. This approach balances isolation with resource efficiency. Separate clusters per tenant is extremely expensive and operationally complex for hundreds of tenants. Separate node pools per tenant is also wasteful and doesn't scale to hundreds of tenants. Application-level isolation alone doesn't provide resource management and billing separation that Kubernetes namespaces with quotas enable.
4. Your application uses Cloud Spanner for transaction processing and BigQuery for analytics. You need to replicate Spanner data to BigQuery with minimal latency for real-time dashboards. Which solution provides the most efficient replication?
Explanation
Datastream provides native integration with Spanner change streams for change data capture and continuous replication to BigQuery. It handles schema evolution, provides exactly-once delivery, and minimizes latency for real-time analytics. Datastream manages the complexity of change stream processing and BigQuery streaming inserts. Cloud Functions with change streams work but require custom code for handling backpressure, retries, and batching. Cloud Storage export with scheduled loads introduces significant latency. Dataflow periodic reads cause unnecessary full table scans and cannot capture changes in real-time.
5. Your application receives traffic from a global user base. Cloud Run services are deployed in us-central1. European users experience 300ms latency. The application is stateless and can run in any region. How should you reduce latency for global users while maintaining simple operations?
Explanation
Global external Application Load Balancer with Cloud Run backends in multiple regions provides automatic routing to the nearest healthy region, minimizing latency. This is the standard Google Cloud pattern for global application deployment. The load balancer handles routing automatically without DNS complexity. Cloud CDN works for cacheable content but doesn't help with dynamic API responses. Separate services with DNS routing requires manual DNS management and doesn't provide automatic failover. Cloud Armor is for security, not routing optimization.
Google Cloud Certified - Generative AI Leader (GEN-AI-LEADER)
GEN-AI-LEADER · 811 questions
Google Cloud Certified - Professional Cloud Architect (PCA)
PCA · 1397 questions
Google Cloud Certified - Professional Cloud Database Engineer (PCDE)
PCDE · 608 questions
Google Cloud Certified - Professional Cloud DevOps Engineer (PCDOps)
PCDOps · 1132 questions
Google Cloud Certified - Professional Cloud Network Engineer (PCNE)
PCNE · 881 questions
Google Cloud Certified - Professional Cloud Security Engineer (PCSE)
PCSE · 1075 questions
$17.99
One-time access to this exam