Free GCP PCA Practice Questions
(Google Cloud Professional Cloud Architect)
The GCP PCA exam tests your knowledge of Google Cloud architecture and best practices. Practice real-world scenarios to prepare for the fundamentals of Google Cloud and best practices.
GCP PCA Practice Questions
10 Free Questions • Updated for 2026 • No dumps
Designed by experts and updated regularly based on exam changes.
1
A global e-commerce company requires an application to serve customers worldwide with extremely low latency and high availability. The database must be globally consistent across multiple regions. Which Google Cloud network and database architecture provides the best balance of performance, resilience, and global consistency?
A
Compute Engine instances with a full-mesh VPN and custom database replication.
B
BigQuery for transactional data and a single regional Cloud CDN instance.
C
Cloud Spanner with global instances and Cloud Load Balancing (Global External).
D
Cloud SQL with cross-region read replicas and regional external load balancers.
✅ Correct Answer: C
Explanation: Cloud Spanner is a globally distributed, strongly consistent, relational database service ideal for global applications needing high availability and consistency across regions. Cloud Load Balancing (Global External) distributes traffic globally for low latency. Cloud SQL provides regional consistency, not global. Compute Engine with VPN is high operational overhead. BigQuery is for analytics, not transactional data. This combination offers the best architecture for global consistency and performance.
2
A critical financial application has an RTO of 30 minutes and an RPO of 5 minutes. The application runs on Google Kubernetes Engine (GKE) and uses Cloud SQL for its database. The disaster recovery solution must be cost-optimized for a secondary region.
A
Cold standby GKE cluster with daily Cloud SQL backups to Cloud Storage.
B
Backup and restore for both GKE and Cloud SQL to Cloud Storage.
C
Warm standby GKE cluster with Cloud SQL cross-region read replica.
D
Hot standby GKE cluster with active-active Cloud SQL instances.
✅ Correct Answer: C
Explanation: A warm standby strategy (pre-provisioned infrastructure with data synchronization) aligns well with the RTO/RPO requirements while being cost-effective compared to hot standby. A Cloud SQL cross-region read replica provides a 5-minute RPO. A cold standby and backup/restore will not meet the 30-minute RTO. A hot standby is more expensive than necessary for the given RTO/RPO.
3
A company has a fluctuating batch processing workload that runs for several hours daily. The jobs can tolerate interruptions and require high compute capacity. The primary goal is to minimize costs. Which Google Cloud compute pricing model is most suitable?
A
Sole-tenant nodes with custom machine types.
B
Committed Use Discounts for custom machine types.
C
Compute Engine Preemptible VMs with Managed Instance Groups.
D
Compute Engine On-Demand VMs with autoscaling.
✅ Correct Answer: C
Explanation: Preemptible VMs offer significant cost savings for fault-tolerant batch workloads that can be interrupted and restarted. Using them with Managed Instance Groups provides automatic scaling and instance management. On-Demand VMs are more expensive. Sole-tenant nodes are for dedicated hardware, not cost optimization for flexible batch jobs. Committed Use Discounts are for stable, long-term workloads, not fluctuating, interruptible ones.
4
A multinational corporation has multiple VPCs across various projects and regions in Google Cloud. They need to establish secure, high-bandwidth connectivity between all VPCs and their on-premises data centers, ensuring central network control. Which Google Cloud networking service enables this architecture with minimal overhead?
A
VPC Service Controls with Shared VPC and Cloud Interconnect.
B
Establishing a full mesh of individual Cloud VPN tunnels.
C
Deploying a Network Virtual Appliance (NVA) in each VPC.
D
Multiple VPC Peering connections and individual Cloud VPNs.
✅ Correct Answer: A
Explanation: VPC Service Controls enhances security but isn't the primary network connectivity solution. Shared VPC centralizes network administration, allowing multiple projects to share a common VPC network. Cloud Interconnect provides private, high-bandwidth connectivity to on-premises. This combination simplifies management while providing robust connectivity. VPC Peering is not transitive and scales poorly. NVAs add significant operational overhead. A full mesh of VPNs is complex and not scalable.
5
A highly sensitive application requires stringent data exfiltration protection. User data stored in Cloud Storage must be inaccessible from any external network, even by accident. Additionally, API calls from specific projects to Cloud Storage must be restricted. How can these requirements be met?
A
VPC Service Controls to create a security perimeter.
B
Customer-managed encryption keys (CMEK) for all data.
C
IAM policies to restrict user access to Cloud Storage.
D
Cloud Firewall Rules to block egress traffic from VMs.
✅ Correct Answer: A
Explanation: VPC Service Controls (VPC-SC) creates security perimeters around Google Cloud resources (like Cloud Storage buckets) to restrict data movement and API access, preventing unauthorized data exfiltration. While IAM controls user access, it doesn't prevent authorized users from exfiltrating data. Firewall rules protect VMs, not direct API access to services like Cloud Storage. CMEK encrypts data at rest, but doesn't prevent exfiltration.
6
A large enterprise is struggling with slow, inconsistent software deployments across multiple development teams. They want to implement a standardized, automated CI/CD pipeline that supports multiple languages and easily integrates with existing source control. Which Google Cloud solution provides this capability with high efficiency?
A
Cloud Composer for orchestrating data pipelines.
B
Manual shell scripts on Compute Engine for deployment.
C
Cloud Build and Cloud Deploy with Cloud Source Repositories.
D
Cloud Functions for event-driven deployments.
✅ Correct Answer: C
Explanation: Cloud Build provides a serverless CI/CD platform that integrates with Cloud Source Repositories (or other Git providers) and supports various languages/frameworks. Cloud Deploy automates continuous delivery to target environments. This combination offers a robust, automated CI/CD solution. Manual scripts are error-prone. Cloud Composer is for data workflows. Cloud Functions are for lightweight event handlers, not full CI/CD pipelines.
7
A company has a consistent, predictable compute workload running on Compute Engine instances 24/7 across multiple projects. They want to maximize cost savings over a 3-year period while maintaining operational flexibility regarding instance types. Which Google Cloud billing option is most effective?
A
Compute Engine Resource-based Committed Use Discounts (CUDs).
B
Sustained Use Discounts and manual instance type selection.
C
Compute Engine Flexible Committed Use Discounts (CUDs).
D
Preemptible VMs for all workloads.
✅ Correct Answer: C
Explanation: Flexible Committed Use Discounts (CUDs) provide significant discounts for sustained compute usage, automatically applying to eligible vCPUs and memory across any machine type, region, and project within the billing account, offering both cost savings and flexibility. Resource-based CUDs lock into specific machine types. Preemptible VMs are for interruptible workloads. Sustained Use Discounts are automatic but less significant than CUDs.
8
A high-traffic web application relies on a critical microservice experiencing intermittent errors and slow response times. The application team needs to identify the exact service calls causing the issues, including latency between microservices. Which Google Cloud tool provides distributed tracing capabilities for detailed performance analysis?
A
Cloud Logging for aggregated application logs.
B
Cloud Monitoring for infrastructure metrics.
C
Cloud Diagnostics for error reporting.
D
Cloud Trace for end-to-end request tracing.
✅ Correct Answer: D
Explanation: Cloud Trace is specifically designed for distributed tracing, allowing developers to visualize the flow of requests through complex microservices architectures, identify latency bottlenecks, and pinpoint service-to-service communication issues. Cloud Logging aggregates logs but doesn't show the request flow. Cloud Monitoring collects metrics. Cloud Diagnostics helps with error reporting but not distributed tracing across services.
9
An organization needs to consistently provision identical development, staging, and production environments for their applications on Google Cloud. They require an infrastructure-as-code solution that supports declarative configuration and can manage both network and compute resources. Which Google Cloud service is the most appropriate?
A
Using custom Python scripts with the Google Cloud SDK.
B
Manually creating resources through the Google Cloud Console.
C
Deployment Manager or Terraform for declarative infrastructure.
D
Cloud Shell scripts for imperative resource creation.
✅ Correct Answer: C
Explanation: Deployment Manager (Google Cloud's native IaC) and Terraform (third-party, widely adopted IaC) both allow defining infrastructure declaratively in configuration files, ensuring consistent, repeatable environment provisioning. Cloud Shell scripts or Python scripts are imperative and less manageable for complex environments. Manual console creation is not scalable or consistent. This enables high consistency and version control for infrastructure.
10
A gaming company collects vast amounts of real-time telemetry data (billions of events per second) from active players. They need to analyze this data instantly for live game adjustments and trend analysis, with automatic scaling. Which Google Cloud service is best for ingesting and processing this streaming data at scale?
A
Cloud Storage with periodic batch processing.
B
Cloud Pub/Sub for ingestion and Dataflow for processing.
C
BigQuery for direct real-time ingestion and analysis.
D
Cloud SQL for transactional data storage.
✅ Correct Answer: B
Explanation: Cloud Pub/Sub is a highly scalable, real-time messaging service for ingesting millions of events per second. Dataflow (a fully managed service for executing Apache Beam pipelines) is ideal for high-volume stream processing. This combination is designed for real-time analytics at massive scale. Cloud SQL is for relational data. BigQuery can do real-time analysis but Pub/Sub and Dataflow are the ingestion and processing layers for continuous streams. Cloud Storage with batch processing is not real-time.
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