Google  Professional Cloud Architect Google Cloud Certified – Professional Cloud Architect Exam Dumps and Practice Test Questions Set1 Q1-20

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Question 1:

A retail company wants to migrate its on-premises e-commerce application to Google Cloud. The application has a relational database backend and processes millions of user requests daily. The company needs minimal downtime during migration and high availability. Which approach should the Cloud Architect recommend?

A) Lift-and-shift the entire application to Compute Engine without changing the database.
B) Use Cloud SQL with Database Migration Service and deploy the application on GKE with regional clusters.
C) Deploy the database to Cloud Bigtable and run the application on App Engine Standard Environment.
D) Re-architect the application to use Firestore and deploy on Cloud Functions.

Answer: B) Use Cloud SQL with Database Migration Service and deploy the application on GKE with regional clusters.

Explanation:

Migrating a large-scale e-commerce application with a relational database to Google Cloud requires addressing high availability, minimal downtime, and scalability. The Cloud SQL service is fully managed, supporting MySQL, PostgreSQL, and SQL Server. Using the Database Migration Service (DMS) allows for minimal downtime by continuously replicating transactions from the on-premises database to Cloud SQL. This replication ensures that the database remains synchronized until the final cutover, reducing disruption to business operations.

For the application, Google Kubernetes Engine (GKE) is ideal for containerized workloads, offering automated scaling, rolling updates, self-healing, and load balancing. By deploying regional clusters, nodes are spread across multiple zones, ensuring high availability even if one zone fails. Additionally, Cloud Load Balancing provides global distribution, directing traffic to the nearest healthy instance and automatically handling failover.

Security must be incorporated using IAM roles, the principle of least privilege, and VPC Service Controls to prevent unauthorized access. SSL/TLS encryption protects data in transit, while Cloud Monitoring and Logging allow real-time visibility into application and database performance, helping identify and mitigate issues quickly.

Option A (lift-and-shift) risks downtime and underutilizes managed services. Option C (Bigtable + App Engine) is unsuitable because Bigtable is NoSQL and does not support relational workloads efficiently. Option D (Firestore + Cloud Functions) requires significant re-architecture and may not handle transactional workloads at scale.

This architecture ensures highly available, scalable, secure, and minimally disruptive migration, aligning with Google Cloud best practices for enterprise applications.

Question 2:

A media company wants to build a global video streaming platform on Google Cloud. The platform should automatically scale with millions of concurrent users and ensure low latency worldwide. Which architecture should the Cloud Architect recommend?

A) Deploy video streaming servers on Compute Engine and use Cloud CDN for caching.
B) Store videos in Cloud Storage, use Cloud CDN, and serve content via Cloud Load Balancing.
C) Use App Engine Standard Environment to serve videos directly from local instance storage.
D) Store videos in BigQuery and stream using Cloud Functions.

Answer: B) Store videos in Cloud Storage, use Cloud CDN, and serve content via Cloud Load Balancing.

Explanation:

A global video streaming platform needs massive scalability, low latency, and cost-effective storage. Cloud Storage provides durable and highly available storage for large video files, and it integrates seamlessly with Cloud CDN, which caches content at edge locations worldwide, reducing latency for users across different regions. Using Cloud Load Balancing ensures that user requests are routed to the nearest edge location, optimizing performance and availability.

Option A (Compute Engine servers + CDN) is less efficient at scale because managing millions of concurrent sessions requires substantial operational overhead and scaling complexity. Option C (App Engine + local instance storage) is unsuitable because serving large media files from ephemeral instance storage is inefficient and does not scale globally. Option D (BigQuery + Cloud Functions) is inappropriate since BigQuery is designed for analytics, not media streaming.

For secure delivery, signed URLs and Cloud IAM can restrict access to authorized users. Videos can be stored in multiple resolutions, with Cloud CDN caching popular formats to optimize bandwidth. Cloud Monitoring provides insights into latency, traffic patterns, and cache hit ratios. This architecture balances scalability, low latency, and cost efficiency, adhering to Google Cloud best practices for content delivery and global streaming platforms.

Question 3:

A healthcare provider is designing a cloud architecture for storing patient records. They need to ensure HIPAA compliance, high availability, and disaster recovery. Which combination of Google Cloud services should the Cloud Architect recommend?

A) Store data in Cloud SQL, use Cloud Storage for backups, and Cloud Load Balancing for access.
B) Store sensitive patient data in Cloud Healthcare API, backups in Cloud Storage with versioning, and implement multi-region Cloud SQL.
C) Store patient records in BigQuery and access through Cloud Functions.
D) Use Firestore for all patient data with App Engine Standard for access.

Answer: B) Store sensitive patient data in Cloud Healthcare API, backups in Cloud Storage with versioning, and implement multi-region Cloud SQL.

Explanation:

Healthcare organizations are subject to HIPAA compliance, requiring secure storage and access controls. Google Cloud provides Cloud Healthcare API, designed for storing sensitive health data in formats like HL7, FHIR, and DICOM, and includes encryption at rest and in transit. Using Cloud Storage for backups with versioning ensures data durability and enables disaster recovery in case of corruption or accidental deletion. Multi-region Cloud SQL deployment provides high availability, distributing instances across multiple zones to ensure resilience against regional failures.

Security controls include IAM roles, VPC Service Controls, and audit logging to meet HIPAA requirements. Data encryption is enforced automatically in Cloud Healthcare API and Cloud Storage, and Cloud Key Management Service (KMS) can manage encryption keys if customer-managed keys are required. Disaster recovery strategies involve replicating backups to a different region, using versioning to maintain historical records and minimize data loss.

Option A lacks a HIPAA-specific design and multi-region deployment. Option C (BigQuery + Cloud Functions) is not ideal for transactional healthcare data due to latency and schema requirements. Option D (Firestore + App Engine) does not offer HIPAA-specific capabilities and may complicate regulatory compliance.

This architecture ensures regulatory compliance, high availability, and disaster recovery, providing a secure and scalable solution for sensitive healthcare data.

Question 4:

A financial services company wants to implement real-time fraud detection for transactions using Google Cloud. The system must handle millions of transactions per day with low latency. Which architecture should the Cloud Architect recommend?

A) Use Pub/Sub to ingest transaction data, Dataflow for processing, and BigQuery for storage and analytics.
B) Ingest data directly into Cloud Storage and process with scheduled Dataflow jobs daily.
C) Use Compute Engine instances with custom scripts for processing transactions.
D) Store transactions in Cloud SQL and use Cloud Functions for periodic checks.

Answer: A) Use Pub/Sub to ingest transaction data, Dataflow for processing, and BigQuery for storage and analytics.

Explanation: 

Real-time fraud detection requires processing large volumes of streaming data with low latency. Pub/Sub provides a fully managed, horizontally scalable messaging service capable of handling millions of messages per second, ideal for transaction ingestion. Dataflow supports stream processing with low latency, allowing real-time detection of anomalies using machine learning models or business rules. Processed results can be stored in BigQuery, enabling both real-time and historical analytics, dashboards, and reporting.

This architecture is serverless, fully managed, and automatically scales with workload demands. Pub/Sub ensures reliable message delivery with at-least-once semantics, while Dataflow provides exactly-once processing semantics, which is critical for financial transactions. Security measures include IAM, VPC Service Controls, CMEK encryption, and audit logging to meet regulatory requirements in financial services.

Option B (Cloud Storage + scheduled Dataflow) introduces delays and is unsuitable for real-time detection. Option C (Compute Engine + scripts) is operationally heavy and hard to scale efficiently. Option D (Cloud SQL + Cloud Functions) cannot handle high throughput or real-time processing effectively.

This solution ensures scalable, low-latency, secure, and auditable real-time fraud detection, meeting financial industry requirements and Google Cloud best practices.

Question 5:

A global e-commerce company experiences traffic spikes during holiday seasons. They want an architecture that auto-scales based on demand, provides high availability, and minimizes operational overhead. Which approach should the Cloud Architect recommend?

A) Deploy web servers on Compute Engine with a fixed number of instances and manual scaling.
B) Use App Engine Standard Environment for the web application with Cloud SQL for the database.
C) Deploy containers on GKE with a single-zone cluster and autoscaling enabled.
D) Store web content in Cloud Storage and use Cloud Functions for backend processing.

Answer: B) Use App Engine Standard Environment for the web application with Cloud SQL for the database.

Explanation:

For an e-commerce platform experiencing traffic spikes, minimizing operational overhead while ensuring high availability and auto-scaling is critical. App Engine Standard Environment is a fully managed Platform-as-a-Service (PaaS) that automatically scales instances up or down based on incoming traffic, without requiring manual intervention or infrastructure management. This is ideal for applications with highly variable workloads.

Cloud SQL provides a managed relational database service for transactional workloads. It supports automated backups, replication, failover, and multi-region deployment for high availability. App Engine integrates seamlessly with Cloud SQL, allowing secure connections using IAM authentication or Cloud SQL Proxy.

Option A (Compute Engine with fixed instances) cannot handle spikes efficiently and requires manual scaling, increasing operational complexity. Option C (GKE single-zone cluster) risks downtime if the zone fails and requires cluster management, adding operational overhead. Option D (Cloud Storage + Cloud Functions) is suitable for serverless static content and small workloads, but is not ideal for full-featured e-commerce platforms with transactional databases.

Security and monitoring are critical. Using VPCs, IAM roles, and Cloud Monitoring/Logging ensures compliance, observability, and performance tracking. App Engine handles load balancing, auto-scaling, and versioning, reducing operational effort. This architecture follows Google Cloud best practices for scalable, high-availability applications, especially for seasonal or unpredictable traffic patterns.

Question 6:

A company wants to migrate its monolithic Java application to Google Cloud to improve reliability and scalability. They also want minimal code changes. Which approach should the Cloud Architect recommend?

A) Break the monolith into microservices and deploy on GKE with regional clusters.
B) Lift-and-shift the application to App Engine Standard Environment.
C) Lift-and-shift the application to Compute Engine Managed Instance Groups with load balancing.
D) Rewrite the application for Cloud Functions using serverless architecture.

Answer: C) Lift-and-shift the application to Compute Engine Managed Instance Groups with load balancing.

Explanation:

The company wants minimal code changes, making a full re-architecture or serverless rewrite impractical. Compute Engine Managed Instance Groups (MIGs) provide a lift-and-shift migration path that enables deploying existing monolithic applications in VMs with auto-healing, auto-scaling, and load balancing. This allows the application to scale based on demand while improving reliability and availability without requiring major code modifications.

MIGs allow rolling updates, automatic replacement of unhealthy instances, and integration with Cloud Load Balancing to distribute traffic across zones and regions. Multi-zone deployment ensures high availability during zone failures. Operational tasks such as patching can be automated using OS patch management.

Option A (microservices + GKE) requires substantial refactoring and operational complexity, which conflicts with the “minimal code changes” requirement. Option B (App Engine Standard) may require significant code adjustments for App Engine runtime compatibility. Option D (Cloud Functions) requires a complete rewrite, which is not suitable for a monolithic Java application.

Security and compliance should be addressed using IAM roles, VPCs, firewall rules, and SSL/TLS connections. Monitoring and logging via Cloud Monitoring and Logging ensure visibility into application performance. This architecture ensures scalable, reliable deployment while preserving the existing application codebase, aligning with Google Cloud best practices for lift-and-shift migrations.

Question 7:

A company needs to analyze IoT sensor data in real-time to detect anomalies and trigger alerts. They want serverless, scalable, and low-latency processing without managing infrastructure. Which Google Cloud architecture should the Cloud Architect recommend?

A) Store sensor data in Cloud Storage and process it using nightly Dataflow batch jobs.
B) Stream data to Pub/Sub, process with Dataflow streaming pipelines, and store results in BigQuery.
C) Use Compute Engine VMs to process incoming IoT messages and store data in Cloud SQL.
D) Ingest sensor data into BigQuery directly using scheduled queries.

Answer: B) Stream data to Pub/Sub, process with Dataflow streaming pipelines, and store results in BigQuery.

Explanation:

For real-time IoT analytics, low latency, scalability, and serverless operations are key. Pub/Sub provides a fully managed, horizontally scalable messaging service that can ingest high-throughput IoT data reliably. By streaming data into Pub/Sub, you decouple ingestion from processing, ensuring durability and scalability.

Dataflow supports streaming pipelines, enabling real-time processing of messages with transformations, aggregation, anomaly detection, or machine learning scoring. It provides serverless, auto-scaling stream processing without infrastructure management, and guarantees exactly-once processing semantics critical for accurate IoT analytics.

Processed results can be stored in BigQuery, allowing real-time dashboards, reporting, and further analysis using SQL. Security can be implemented using IAM roles, VPC Service Controls, CMEK encryption, and secure Pub/Sub subscriptions. Alerts can be integrated via Cloud Functions or Cloud Monitoring, enabling near-instantaneous notifications on anomalies.

Option A (Cloud Storage + batch Dataflow) introduces latency and is unsuitable for real-time analytics. Option C (Compute Engine + Cloud SQL) requires manual scaling and infrastructure management, reducing operational efficiency. Option D (direct BigQuery ingestion) is not designed for high-throughput streaming workloads and may result in delays.

This architecture ensures real-time, serverless, scalable, and secure IoT processing, following Google Cloud best practices for event-driven analytics and anomaly detection.

Question 8:

A gaming company wants to provide global multiplayer game servers with low latency and automatic scaling during peak hours. Which Google Cloud architecture should the Cloud Architect recommend?

A) Deploy game servers on Compute Engine with manual load balancing and fixed instances.
B) Use GKE with regional clusters, horizontal pod autoscaling, and Cloud Load Balancing.
C) Deploy servers in a single region on App Engine Standard.
D) Use Cloud Functions to host multiplayer game logic.

Answer: B) Use GKE with regional clusters, horizontal pod autoscaling, and Cloud Load Balancing.

Explanation:

Global multiplayer games require low latency, high availability, and dynamic scaling. Deploying on GKE with regional clusters distributes game server pods across multiple zones, ensuring fault tolerance in case of zone failures. Horizontal Pod Autoscaling allows the cluster to automatically scale the number of game server pods based on CPU, memory, or custom metrics, handling peak traffic without manual intervention.

Cloud Load Balancing routes players to the nearest healthy server, minimizing latency and optimizing user experience. Security and networking considerations include VPC configuration, firewall rules, IAM roles, and private clusters to protect game traffic. Cloud Monitoring and Logging provide operational visibility, allowing proactive management of performance and user experience.

Option A (Compute Engine fixed instances) cannot efficiently handle dynamic load and requires manual intervention. Option C (App Engine Standard) is unsuitable for stateful, low-latency game servers. Option D (Cloud Functions) is not suitable for multiplayer gaming due to stateless execution, cold-start latency, and session persistence limitations.

This architecture provides scalable, low-latency, highly available multiplayer game hosting, aligning with Google Cloud best practices for global online gaming and high-performance workloads.

Question 13:

A company is running a global e-commerce application on Google Cloud and wants to improve performance by caching frequently accessed data while ensuring data consistency. Which architecture should the Cloud Architect recommend?

A) Use Memorystore for Redis with regional replication and integrate with Cloud SQL.
B) Use Cloud Storage as the only data store for all transactions.
C) Deploy Compute Engine instances with local memory caching on each instance.
D) Store frequently accessed data in BigQuery and query in real-time for every request.

Answer: A) Use Memorystore for Redis with regional replication and integrate with Cloud SQL.

Explanation:

To improve application performance, caching frequently accessed data reduces latency and offloads read operations from the primary database. Memorystore for Redis is a fully managed, in-memory caching service that supports high throughput and low latency. Using regional replication ensures availability in case of zone failure. Integrating Memorystore with Cloud SQL allows consistent data access; Redis can store hot data, while Cloud SQL remains the authoritative source.

Option B (Cloud Storage only) is unsuitable because Cloud Storage has higher latency and is not designed for transactional workloads. Option C (local memory caching on Compute Engine) is operationally heavy and not shared across instances, making it unsuitable for distributed applications. Option D (BigQuery queries for every request) introduces high latency and unnecessary cost for frequently accessed data.

Security considerations include using VPC Service Controls, IAM roles, and TLS connections. Cloud Monitoring and Logging help track cache hits, latency, and memory usage. This architecture ensures low latency, high availability, and consistent caching, aligning with Google Cloud best practices for distributed web applications.

Question 14:

A company wants to implement a highly available, fault-tolerant database architecture for a transactional application that requires strong consistency and automatic failover. Which architecture should the Cloud Architect recommend?

A) Deploy Cloud SQL with a high-availability (HA) configuration and automatic failover enabled.
B) Use Cloud Bigtable with single-zone clusters for cost efficiency.
C) Store data in Firestore with eventual consistency.
D) Deploy multiple Compute Engine VMs with MySQL installed manually and configure replication manually.

Answer: A) Deploy Cloud SQL with high-availability (HA) configuration and automatic failover enabled.

Explanation:

For transactional applications requiring strong consistency and automatic failover, Cloud SQL with HA configuration is ideal. It automatically provisions a standby instance in a different zone, continuously replicates data, and handles failover seamlessly if the primary instance fails. This ensures minimal downtime and maintains consistency across transactions.

Option B (Cloud Bigtable single-zone) is not suitable because Bigtable is designed for wide-column, NoSQL workloads and does not guarantee strong transactional consistency. Option C (Firestore) provides eventual consistency in some cases and may not meet strict transactional requirements. Option D (manual MySQL replication) increases operational overhead, risk of misconfiguration, and lacks Google Cloud-managed failover mechanisms.

Security and compliance should be implemented using IAM, VPC Service Controls, CMEK, and audit logging. Monitoring with Cloud Monitoring tracks replication lag, failover events, and performance metrics. This architecture ensures high availability, fault tolerance, and transactional consistency following Google Cloud best practices.

Question 15:

A company wants to deploy a data processing pipeline that handles large volumes of unstructured log files. They want to process the data in near real-time, store it for analytics, and minimize operational management. Which architecture should the Cloud Architect recommend?

A) Store logs in Cloud Storage, use Cloud Functions to process, and store results in BigQuery.
B) Stream logs into Pub/Sub, process with Dataflow streaming pipelines, and store processed data in BigQuery.
C) Use Compute Engine instances to process logs in batches every night and store them in Cloud SQL.
D) Store logs in Firestore and process them with Cloud Functions in batches.

Answer: B) Stream logs into Pub/Sub, process with Dataflow streaming pipelines, and store processed data in BigQuery.

Explanation:

Near real-time processing of large volumes of unstructured log files requires a serverless, scalable streaming architecture. Pub/Sub allows ingestion of massive amounts of log data reliably and asynchronously. Dataflow provides fully managed stream processing with low latency, automatic scaling, and exactly-once processing semantics, critical for accurate analytics. Processed results can be stored in BigQuery, enabling fast analytics and reporting over large datasets.

Option A (Cloud Storage + Cloud Functions) introduces latency, is less scalable, and is better suited for event-driven workloads rather than high-throughput streaming logs. Option C (Compute Engine batch processing) increases operational complexity and does not support real-time processing. Option D (Firestore + Cloud Functions) is not designed for high-volume log analytics and may incur performance and cost issues.

Security should be enforced using IAM, VPC Service Controls, CMEK, and audit logging. Cloud Monitoring tracks ingestion rates, processing latency, and pipeline health. This architecture ensures near real-time, scalable, secure, and serverless log processing, aligning with Google Cloud best practices for analytics pipelines.

Question 16:

A company wants to implement a serverless, event-driven architecture to process images uploaded by users. The solution must automatically scale with load and integrate with machine learning models for image recognition. Which Google Cloud architecture should the Cloud Architect recommend?

A) Use Compute Engine instances to poll Cloud Storage and process images.
B) Trigger Cloud Functions on Cloud Storage uploads, process images, and call the AI Platform Prediction for ML inference.
C) Store images in Firestore and process with App Engine Standard.
D) Use Cloud Storage and perform batch processing daily with Dataflow.

Answer: B) Trigger Cloud Functions on Cloud Storage uploads, process images, and call AI Platform Prediction for ML inference.

Explanation:

Serverless event-driven architectures require automatic scaling, minimal infrastructure management, and real-time processing. Cloud Functions can be triggered directly by Cloud Storage upload events, ensuring that every image is processed immediately after upload. This eliminates the need for polling or manually managing servers. For ML inference, AI Platform Prediction (Vertex AI) provides managed machine learning model serving, allowing Cloud Functions to send images for recognition and receive predictions in real-time.

Option A (Compute Engine polling) introduces latency, requires scaling and patching management, and increases operational complexity. Option C (Firestore + App Engine) is unsuitable because Firestore is not optimized for large binary objects, and App Engine may not scale efficiently for bursty image processing. Option D (batch Dataflow) does not meet real-time processing requirements.

Security measures include IAM, VPC Service Controls, encryption at rest (CMEK), and signed URLs for access control. Cloud Monitoring and Logging track function invocations, error rates, and processing latency. This architecture ensures scalable, event-driven, serverless image processing integrated with ML inference, aligning with Google Cloud best practices for event-driven workloads.

Using Cloud Functions triggered by Cloud Storage uploads, processing the images immediately, and then calling AI Platform Prediction for machine learning inference is the most efficient, scalable, and modern solution among the provided choices. This architecture enables real-time image processing, minimizes operational overhead, and fully leverages Google Cloud’s serverless and managed ML services. It stands out because it supports event-driven automation, seamless scaling, and high-performance ML inference without requiring manual intervention or infrastructure maintenance.

When an image is uploaded to Cloud Storage, it automatically generates an event that can trigger a Cloud Function. This event-driven model eliminates the need for polling or manual checks and ensures immediate action as soon as new data becomes available. Cloud Functions are serverless, meaning developers do not manage servers, instances, or scaling policies. The platform automatically provisions resources and scales up or down based on the volume of incoming events. This approach makes it highly suitable for unpredictable workloads, such as spikes in image uploads or periods of low activity.

The Cloud Function can run lightweight image preprocessing tasks, such as resizing, format conversion, or metadata extraction, without requiring a dedicated server environment. After preprocessing, the function can make a direct call to AI Platform Prediction (Vertex AI Prediction in modern deployments) to perform machine learning inference. This allows the application to use powerful ML models—such as object detection, image classification, or custom-trained models—without hosting or managing the model infrastructure manually. AI Platform Prediction provides automatic scaling, GPU/TPU options, low latency, and model versioning, making it ideal for production inference. Once the prediction results are generated, they can be stored in BigQuery, Firestore, or Cloud Storage, depending on the use case. This workflow ensures a seamless, automated pipeline from ingestion to ML output.

In contrast, option A, which uses Compute Engine instances to poll Cloud Storage and process images, introduces several inefficiencies. Polling is resource-intensive, inefficient, and may introduce delays. Compute Engine requires managing VMs, patching systems, controlling autoscaling, and ensuring uptime. This increases operational burden and cost. The model also fails to take advantage of serverless event-driven architectures, which are generally more efficient for intermittent workloads.

Option C suggests storing images in Firestore and processing them with App Engine Standard, which is fundamentally unsuitable. Firestore is built for document-based structured data, not for binary large objects like images. Storing large media files in Firestore is costly, slow, and technically inappropriate. App Engine Standard can process requests, but is not optimized for large-scale image processing or continuous asynchronous workloads. This architecture would neither scale efficiently nor follow recommended best practices.

Option D proposes daily batch processing with Dataflow, which is appropriate only when real-time inference is not required. Daily batch jobs introduce significant latency and cannot support use cases such as content moderation, anomaly detection, real-time user feedback, or automated pipeline responses. While Dataflow is powerful for large-scale batch or streaming pipelines, using it for daily image processing misses the opportunity to automate immediate responses to new uploads.

Therefore, option B provides a modern, highly scalable, cost-efficient, and fully automated architecture. It integrates Cloud Storage’s event system, the serverless power of Cloud Functions, and the high-performance ML inference capabilities of AI Platform Prediction. This solution minimizes infrastructure management, supports real-time processing, adapts easily to variable workloads, and aligns perfectly with cloud-native best practices—making it unquestionably the best choice among the four options.

Question 17:

A company wants to implement a multi-tenant SaaS application on Google Cloud that requires strong isolation between tenants, automatic scaling, and minimal operational overhead. Which architecture should the Cloud Architect recommend?

A) Deploy the application on GKE using namespaces for tenant isolation and Cloud SQL with separate databases per tenant.
B) Deploy all tenants on App Engine Standard Environment using a single shared Cloud SQL database.
C) Use Compute Engine VMs with manual tenant isolation and one Cloud SQL instance for all tenants.
D) Store tenant data in Firestore and serve via Cloud Functions.

Answer: A) Deploy the application on GKE using namespaces for tenant isolation and Cloud SQL with separate databases per tenant.

Explanation:

Multi-tenant SaaS applications require strong isolation between tenants to prevent data leakage while supporting scalability. GKE namespaces provide logical isolation for tenants within the same cluster, allowing resource quotas and network policies to be applied individually. Using Cloud SQL with separate databases per tenant ensures that each tenant’s data is isolated at the database level, supporting compliance requirements.

GKE provides auto-scaling, rolling updates, and high availability, reducing operational overhead while managing containerized workloads. Cloud Load Balancing routes traffic efficiently across regions, ensuring global availability. Security best practices include IAM, VPC Service Controls, CMEK encryption, and audit logging. Monitoring using Cloud Monitoring provides insights into tenant-specific resource utilization and application health.

Option B (App Engine with shared database) reduces isolation and may not meet compliance requirements. Option C (Compute Engine + manual isolation) increases operational complexity and management overhead. Option D (Firestore + Cloud Functions) is suitable for small-scale workloads but may struggle with multi-tenant isolation and transactional consistency.

This architecture ensures secure, scalable, and isolated multi-tenant deployment following Google Cloud best practices.

Question 18:

A company wants to deploy a real-time analytics platform to monitor user interactions on its website. The system must handle millions of events per second and provide low-latency dashboards. Which architecture should the Cloud Architect recommend?

A) Store events in Cloud Storage and analyze daily using batch Dataflow jobs.
B) Stream events to Pub/Sub, process with Dataflow streaming pipelines, and store in BigQuery for dashboards.
C) Use Compute Engine instances to aggregate events hourly and store them in Cloud SQL.
D) Use Firestore to store events and Cloud Functions to process them periodically.

Answer: B) Stream events to Pub/Sub, process with Dataflow streaming pipelines, and store in BigQuery for dashboards.

Explanation:

Real-time analytics at millions of events per second requires a scalable, low-latency streaming architecture. Pub/Sub allows high-throughput, durable ingestion of events. Dataflow streaming pipelines process events in near real-time, supporting transformations, aggregations, and enrichment. Processed data stored in BigQuery enables interactive dashboards and low-latency queries for visualization.

Option A (Cloud Storage + batch Dataflow) introduces high latency and is unsuitable for real-time analytics. Option C (Compute Engine hourly aggregation) cannot handle the scale or provide near-real-time insights. Option D (Firestore + Cloud Functions) is limited in throughput and introduces periodic delays.

Streaming events through Pub/Sub, processing them with Dataflow streaming pipelines, and storing the results in BigQuery represents the most effective real-time analytics architecture on Google Cloud. Pub/Sub is a globally distributed messaging service built for high-throughput event ingestion, capable of handling millions of messages per second with minimal latency. Its ability to automatically scale and maintain durability ensures that incoming events are captured reliably without requiring the developer to manage underlying infrastructure. By using Pub/Sub as the entry point of the pipeline, the system becomes decoupled, flexible, and robust enough to support diverse producers and consumers simultaneously.

Dataflow complements Pub/Sub by offering fully managed stream processing with autoscaling and continuous execution. In streaming mode, Dataflow can apply complex transformations such as filtering, aggregation, windowing, enrichment, and anomaly detection in real time. This enables organizations to process events as soon as they arrive rather than waiting for scheduled batch intervals. With its unified programming model based on Apache Beam, Dataflow ensures that pipelines remain portable, maintainable, and optimized for both performance and cost. Autoscaling further reduces operational burden by allowing the pipeline to expand and contract based on traffic volume.

BigQuery is the ideal destination for storing processed events because it is built for fast analytical queries on massive datasets. It supports near–real-time ingestion, allowing dashboards to update within seconds of events being processed. Analysts and business users can run SQL queries without worrying about indexing, storage management, or capacity planning. When combined with visualization tools like Looker or Looker Studio, BigQuery enables interactive dashboards, live monitoring, and reporting that reflect the latest business activity. This level of responsiveness is crucial for use cases such as user behavior tracking, fraud detection, IoT monitoring, and operational intelligence.

By contrast, the alternative solutions lack the scalability and immediacy required for real-time analytics. Storing events in Cloud Storage and processing them with daily batch Dataflow jobs introduces significant delays and is suitable only for offline or historical analysis. Using Compute Engine instances to aggregate events hourly and store them in Cloud SQL adds maintenance complexity and limits scalability, because Cloud SQL is not designed for extremely high ingestion rates or analytical querying at large scale. Storing events in Firestore and processing them periodically with Cloud Functions is similarly restrictive, as Firestore is optimized for transactional workloads rather than continuous streaming, and periodic functions create batch-like behavior that prevents real-time insights.

Overall, streaming events to Pub/Sub, processing them with Dataflow, and storing the results in BigQuery provides a modern, scalable, and fully managed architecture. It supports true real-time analytics, reduces operational overhead, and enables continuous insight generation—making it the most suitable and efficient choice among the four options.

Security measures include IAM roles, VPC Service Controls, CMEK, and audit logging. Cloud Monitoring tracks ingestion rates, processing latency, and pipeline health. This architecture ensures scalable, real-time, low-latency analytics following Google Cloud best practices.

Question 19:

A company wants to build a global file-sharing platform with high availability, low latency, and the ability to serve content close to users. Which architecture should the Cloud Architect recommend?

A) Store files in Cloud Storage, use Cloud CDN for caching, and serve via Cloud Load Balancing.
B) Deploy all file servers on Compute Engine in a single region with manual load balancing.
C) Store files in BigQuery and serve using Cloud Functions.
D) Store files in Firestore and use App Engine Standard to serve content.

Answer: A) Store files in Cloud Storage, use Cloud CDN for caching, and serve via Cloud Load Balancing.

Explanation:

A global file-sharing platform requires high availability and low latency, which is achieved by storing files in Cloud Storage, a durable and highly available object storage service. Cloud CDN caches frequently accessed files at edge locations worldwide, minimizing latency for end-users. Cloud Load Balancing distributes traffic across regions and ensures automatic failover.

Option B (single-region Compute Engine) risks downtime during regional outages and requires manual management. Option C (BigQuery + Cloud Functions) is not optimized for serving large binary files. Option D (Firestore + App Engine) has storage and throughput limitations, making it unsuitable for high-volume file sharing.

Security is critical: use IAM roles, signed URLs, VPC Service Controls, and CMEK encryption. Cloud Monitoring tracks access patterns, latency, and cache effectiveness. This architecture ensures a global, secure, and high-performance file-sharing platform in alignment with Google Cloud best practices.

Question 20:

A company wants to implement continuous integration and continuous deployment (CI/CD) for its cloud applications, ensuring automated testing, deployment, and rollback. Which Google Cloud architecture should the Cloud Architect recommend?

A) Use Cloud Build for automated builds, Cloud Source Repositories for source control, and Spinnaker or Cloud Deploy for deployment pipelines.
B) Use Compute Engine instances to manually build and deploy applications.
C) Store application code in Cloud Storage and run manual scripts for deployment.
D) Use App Engine Standard Environment with manual uploads for each version.

Answer: A) Use Cloud Build for automated builds, Cloud Source Repositories for source control, and Spinnaker or Cloud Deploy for deployment pipelines.

Explanation: 

CI/CD pipelines require automation, repeatability, and reliability. Cloud Build allows automated building and testing of application code with integration to source repositories like Cloud Source Repositories, GitHub, or GitLab. Spinnaker or Cloud Deploy provides automated deployment pipelines, supporting rolling updates, canary deployments, and automatic rollback on failure.

Option B (manual builds on Compute Engine) increases operational overhead and is prone to errors. Option C (Cloud Storage + scripts) lacks automation, testing, and version control. Option D (manual uploads to App Engine) is slow and does not support full CI/CD practices.

Security measures include IAM roles for build and deploy permissions, VPC Service Controls for network isolation, and audit logging. Cloud Monitoring tracks build, test, and deployment success rates. This architecture ensures automated, secure, and reliable CI/CD pipelines, following Google Cloud best practices for DevOps.

Implementing a modern, reliable, and scalable CI/CD pipeline is essential for maintaining code quality, increasing deployment speed, and reducing operational risk. When comparing the four options provided, it becomes clear why option A—using Cloud Build, Cloud Source Repositories, and Spinnaker or Cloud Deploy—offers the most robust, automated, and future-ready solution for continuous integration and continuous delivery on Google Cloud. Option A leverages fully managed Google Cloud services that integrate seamlessly to streamline the entire software development lifecycle. Cloud Source Repositories provides a secure, high-performance Git repository system hosted directly on Google Cloud, enabling teams to store, manage, and version their code without relying on external platforms. This native integration allows developers to trigger Cloud Build automatically whenever changes are pushed, ensuring immediate and consistent builds. Cloud Build performs automated builds, tests, and packaging with full reproducibility and isolation. It supports Docker, custom build steps, parallel execution, and deep integration with IAM for security and access control. When paired with Cloud Deploy or Spinnaker, the pipeline extends effortlessly into continuous delivery or continuous deployment, enabling automated rollouts, progressive deployments like canary or blue-green releases, and detailed audit trails. Option A represents a fully automated pipeline where every stage—from code commit to production deployment—is standardized, traceable, and scalable. In contrast, option B suggests using Compute Engine instances to manually build and deploy applications. While Compute Engine is a powerful and flexible IaaS offering, using virtual machines for manual builds is inefficient, labor-intensive, and error-prone. This approach lacks automated triggers, consistent build environments, and the repeatability necessary for modern DevOps practices. It also increases operational overhead and introduces configuration drift as machines evolve differently over time. Option C, which proposes storing application code in Cloud Storage and using manual scripts for deployment, is similarly problematic. Cloud Storage is not a version-controlled system and lacks the collaboration features developers require. Manual deployment scripts can easily become inconsistent, fragile, and hard to maintain as teams and applications grow. This approach also lacks proper CI/CD automation, visibility into pipeline performance, and the safeguards that professional deployment tools provide. Option D suggests using App Engine Standard Environment with manual uploads for each version. Although App Engine Standard is a fully managed platform designed to simplify deployment, manually uploading each application version severely limits automation and scalability. This method might work for small, infrequent deployments, but it becomes unsuitable for organizations practicing continuous integration or frequent releases. It provides little support for automated testing, multi-stage deployment workflows, or advanced release strategies. Taken together, the comparison shows that only option A supports a fully modern CI/CD workflow. The combination of Cloud Build, Cloud Source Repositories, and Cloud Deploy or Spinnaker provides automation, consistency, and enterprise-grade deployment capabilities. It enables faster innovation, reduces human error, enhances security, and supports scalable operations. Therefore, option A is the most efficient, maintainable, and future-proof choice for teams adopting CI/CD on Google Cloud.