Google  Professional Cloud Architect Google Cloud Certified – Professional Cloud Architect Exam Dumps and Practice Test Questions Set 2 Q21-40

Visit here for our full Google Professional Cloud Architect exam dumps and practice test questions.

Question 21:

A company wants to migrate its data warehouse from on-premises Hadoop to Google Cloud. The solution must support batch and near real-time analytics while minimizing operational overhead. Which architecture should the Cloud Architect recommend?

A) Lift-and-shift Hadoop to Compute Engine.
B) Use Cloud Storage for raw data, Cloud Pub/Sub for streaming data, Dataflow for ETL, and BigQuery for analytics.
C) Use Cloud SQL for all datasets and schedule nightly batch jobs.
D) Use Firestore to store all datasets and analyze with Cloud Functions.

Answer: B) Use Cloud Storage for raw data, Cloud Pub/Sub for streaming data, Dataflow for ETL, and BigQuery for analytics.

Explanation:

Migrating from Hadoop requires separating storage and processing. Cloud Storage provides durable, cost-efficient object storage. Pub/Sub handles streaming data ingestion. Dataflow performs ETL in batch or streaming mode with auto-scaling, and BigQuery supports analytics on structured and semi-structured data with low latency. This architecture reduces operational overhead compared to managing Hadoop clusters on Compute Engine. Options A, C, and D either increase complexity or cannot handle high-throughput data efficiently. Security, monitoring, and governance follow Google Cloud best practices.

Question 22:

A company wants to implement a multi-region disaster recovery strategy for its critical business applications. They require RPO of less than 5 minutes and RTO of less than 15 minutes. Which approach should the Cloud Architect recommend?

A) Deploy primary and standby instances in a single region with automated snapshots.
B) Deploy applications in active-passive mode across regions with Cloud SQL cross-region replicas and global load balancing.
C) Deploy Compute Engine instances in multiple zones within a single region.
D) Backup applications to Cloud Storage daily and restore when needed.

Answer: B) Deploy applications in active-passive mode across regions with Cloud SQL cross-region replicas and global load balancing.

Explanation:

Achieving low RPO and RTO requires multi-region active-passive deployment. Cloud SQL cross-region replicas ensure near real-time replication, meeting RPO <5 minutes. Global load balancing allows traffic to failover automatically to the standby region, meeting RTO <15 minutes. Option A is limited to a single region, Option C does not cover regional failures, and Option D introduces significant latency, failing RTO/RPO requirements. Security, monitoring, and compliance practices ensure reliability and data protection.

Question 23:

A retail company wants to analyze user clickstream data in real-time to personalize recommendations on its website. They want a serverless, scalable solution with minimal latency. Which architecture should the Cloud Architect recommend?

A) Store clickstream events in Cloud Storage and process with nightly Dataflow batch jobs.
B) Stream events to Pub/Sub, process using Dataflow streaming pipelines, and store in BigQuery for real-time analytics.
C) Use Firestore to store clickstream events and process with Cloud Functions hourly.
D) Store data in Cloud SQL and query on demand for personalization.

Answer: B) Stream events to Pub/Sub, process using Dataflow streaming pipelines, and store in BigQuery for real-time analytics.

Explanation:

Real-time personalization requires ingesting clickstream events at scale with low latency. Pub/Sub handles high-throughput messaging, Dataflow processes events in streaming mode, and BigQuery allows near real-time analytics. Options A, C, and D either introduce latency or cannot scale efficiently. Security and monitoring ensure safe handling of user data, and the architecture supports auto-scaling, serverless operation, and efficient resource usage.

Question 24:

A company wants to deploy a global API service with high availability, low latency, and automatic failover. Which architecture should the Cloud Architect recommend?

A) Deploy API servers on Compute Engine in a single region with regional load balancing.
B) Use App Engine Standard Environment for the API and global Cloud Load Balancing.
C) Deploy API servers in multiple zones of a single region with manual failover.
D) Use Cloud Functions in a single region without load balancing.

Answer: B) Use App Engine Standard Environment for the API and global Cloud Load Balancing.

Explanation:

App Engine Standard Environment provides fully managed, auto-scaling, and highly available application hosting. Global Cloud Load Balancing routes requests to the nearest healthy region and automatically handles failover, ensuring low latency and high availability. Option A is limited to a single region, Option C requires manual failover, and Option D does not provide multi-region high availability. Security, monitoring, and IAM ensure operational safety.

Question 25:

A company wants to implement a secure, serverless workflow for processing sensitive financial documents uploaded by clients. The solution must ensure compliance, scalability, and auditability. Which architecture should the Cloud Architect recommend?

A) Use Cloud Functions triggered by Cloud Storage uploads, process documents, and store them in Cloud Storage with versioning and audit logging.
B) Deploy Compute Engine VMs to poll Cloud Storage and process documents manually.
C) Store files in Firestore and process with Cloud Functions hourly.
D) Use Cloud Storage with daily batch scripts on Compute Engine for processing.

Answer: A) Use Cloud Functions triggered by Cloud Storage uploads, process documents, and store them in Cloud Storage with versioning and audit logging.

Explanation:

Cloud Functions enables serverless event-driven processing, scaling automatically with workload. Cloud Storage ensures durable, versioned storage with fine-grained IAM control. Audit logging tracks every action for compliance. Option B is operationally heavy, Option C introduces delays, and Option D lacks real-time processing and scalability. This architecture supports compliance, auditability, and serverless processing best practices.

Question 26:

A company wants to implement a secure data pipeline that transfers sensitive data from on-premises databases to Google Cloud, ensuring encryption in transit and at rest, and minimal downtime. Which approach should the Cloud Architect recommend?

A) Export data to CSV, upload to Cloud Storage daily, and load into BigQuery.
B) Use Database Migration Service (DMS) for near real-time replication to Cloud SQL with TLS and CMEK encryption.
C) Set up Compute Engine instances to copy data manually every hour.
D) Use Cloud Functions triggered by CSV uploads to process data.

Answer: B) Use Database Migration Service (DMS) for near real-time replication to Cloud SQL with TLS and CMEK encryption.

Explanation:

DMS supports near real-time replication of relational data from on-premises to Cloud SQL, ensuring minimal downtime. TLS ensures encryption in transit, and CMEK encryption ensures data at rest is secure. Options A, C, and D involve batch processing, manual operations, or introduce latency, failing to meet minimal downtime and security requirements. IAM, VPC Service Controls, and audit logging further strengthen security compliance.

Question 27:

A company wants to implement predictive maintenance for its industrial machinery using IoT sensors. They require near real-time data ingestion, processing, and ML inference. Which architecture should the Cloud Architect recommend?

A) Store sensor data in Cloud Storage and run nightly batch ML predictions.
B) Stream data to Pub/Sub, process with Dataflow, and send to Vertex AI for online predictions.
C) Use Compute Engine VMs to run ML models hourly on accumulated data.
D) Store data in Firestore and process predictions with Cloud Functions daily.

Answer: B) Stream data to Pub/Sub, process with Dataflow, and send to Vertex AI for online predictions.

Explanation:

Near real-time predictive maintenance requires streaming ingestion, low-latency processing, and online ML predictions. Pub/Sub provides high-throughput message ingestion, Dataflow processes and aggregates data, and Vertex AI serves ML models for online predictions. Options A, C, and D introduce delays and are not suitable for real-time ML inference. Security, monitoring, and auto-scaling best practices are applied.

Question 28:

A company wants to implement a hybrid cloud solution where some workloads run on-premises and others in Google Cloud, sharing the same network securely. Which architecture should the Cloud Architect recommend?

A) Use a public internet connection with VPN between on-premises and Cloud.
B) Use Cloud Interconnect or Cloud VPN with VPC peering and IAM policies.
C) Move all workloads to the Cloud to avoid a hybrid architecture.
D) Use Firestore to replicate on-premises data to the Cloud.

Answer: B) Use Cloud Interconnect or Cloud VPN with VPC peering and IAM policies.

Explanation:

Cloud Interconnect or VPN provides secure, low-latency connectivity between on-premises and GCP. VPC peering allows resource sharing across VPCs while IAM policies enforce access control. Option A is less secure and unreliable for enterprise workloads. Option C ignores hybrid requirements. Option D does not provide network-level integration. Security, monitoring, and SLA compliance are ensured.

Question 29:

A company wants to deploy a microservices-based application globally with low latency and fault tolerance. Which architecture should the Cloud Architect recommend?

A) Deploy microservices on App Engine in a single region.
B) Deploy microservices in GKE with regional clusters, global Cloud Load Balancing, and Cloud SQL with multi-region replicas.
C) Deploy microservices on Compute Engine in a single zone.
D) Deploy microservices on Cloud Functions without load balancing.

Answer: B) Deploy microservices in GKE with regional clusters, global Cloud Load Balancing, and Cloud SQL with multi-region replicas.

Explanation:

Regional GKE clusters ensure high availability and fault tolerance. Global Cloud Load Balancing directs traffic to the nearest healthy region. Multi-region Cloud SQL replicas provide HA for stateful services. Options A, C, and D cannot meet global low-latency, multi-region, fault-tolerant requirements. Security and monitoring follow best practices.

Question 30:

A company wants to build a cost-optimized analytics solution that queries terabytes of historical data occasionally. Which architecture should the Cloud Architect recommend?

A) Store data in BigQuery and pay for on-demand queries.
B) Store data in BigQuery with partitioned tables and use long-term storage pricing.
C) Store data in Cloud SQL and query daily.
D) Use Firestore for historical analytics.

Answer: B) Store data in BigQuery with partitioned tables and use long-term storage pricing.

Explanation:

Partitioning reduces query costs and improves performance. Long-term storage pricing lowers costs for rarely accessed data. Option A is costlier for large datasets. Option C (Cloud SQL) cannot handle terabytes efficiently. Option D (Firestore) is not suitable for historical analytics. Security and monitoring follow best practices.

Question 31:

A company wants to implement a secure, high-performance content delivery platform for dynamic and static assets globally. Which services should the Cloud Architect use?

A) Cloud Storage for static content, App Engine for dynamic content, Cloud CDN, and Cloud Load Balancing.
B) Compute Engine for all assets with manual scaling.
C) Firestore for dynamic content and Cloud Functions for static assets.
D) BigQuery to serve dynamic and static content.

Answer: A) Cloud Storage for static content, App Engine for dynamic content, Cloud CDN, and Cloud Load Balancing.

Explanation:

Cloud Storage efficiently stores static assets, App Engine serves dynamic content serverlessly, Cloud CDN caches content globally, and Cloud Load Balancing provides high availability and low latency. Options B, C, and D are operationally intensive or unsuitable for large-scale content delivery. Security, monitoring, and IAM policies ensure safe and scalable delivery.

Question 32:

A company wants to migrate its on-premises MySQL database to Google Cloud while ensuring zero downtime. Which approach should the Cloud Architect recommend?

A) Backup database to Cloud Storage and restore once fully uploaded.
B) Use Database Migration Service for continuous replication to Cloud SQL.
C) Export data to CSV and import into Cloud SQL daily.
D) Manually copy database tables using Compute Engine scripts.

Answer: B) Use Database Migration Service for continuous replication to Cloud SQL.

Explanation:

DMS supports continuous replication from MySQL to Cloud SQL, enabling near-zero downtime during migration. Options A, C, and D involve downtime and are error-prone. Security, monitoring, and encryption practices ensure safe migration. Using Database Migration Service (DMS) for continuous replication to Cloud SQL is the most reliable, efficient, and low-downtime method for modernizing database infrastructure on Google Cloud. This approach is specifically designed for seamless, secure, and automated migration of databases from on-premises or other cloud environments to Cloud SQL. It minimizes manual effort, reduces the risk of data loss, and ensures that applications can transition with near-zero downtime, which is essential for production workloads. By establishing a continuous replication pipeline, DMS keeps the source and destination databases synchronized until the final cutover, allowing organizations to migrate without interrupting day-to-day operations.

One of the greatest advantages of Database Migration Service is its simplicity and automation. Instead of scripting complex migrations or coordinating downtime windows, DMS handles the ingestion, synchronization, and data consistency for you. It supports both homogeneous migrations—such as MySQL-to-MySQL or PostgreSQL-to-PostgreSQL—and heterogeneous migrations through its integration with features like AlloyDB and Cloud SQL. The service encrypts data in transit, automatically provisions connectivity, and validates schema compatibility. It also provides monitoring tools that track replication lag, data throughput, and migration status, allowing teams to detect issues early and resolve them before the cutover.

Continuous replication is particularly powerful for organizations that cannot afford extended downtime. Traditional migration strategies often require long outages while exporting, transferring, and importing databases. DMS avoids this by performing an initial bulk load of the data, then keeping the target Cloud SQL database in sync with ongoing changes. Once replication catches up and the new Cloud SQL instance is fully aligned with the source, teams can schedule a brief cutover—often just a few minutes—to switch production traffic. This ensures a smooth, nearly disruption-free transition to a managed cloud database with improved reliability, scalability, and automatic maintenance.

In contrast, option A—backing up a database to Cloud Storage and restoring it—cannot support continuous replication. This method requires a full backup and restore sequence, which results in significant downtime, and must be repeated manually if updates occur during the migration window. It is only suitable for small databases or environments where downtime is acceptable, and it does not support real-time synchronization or automated cutover.

Option C, exporting data to CSV and importing into Cloud SQL daily, is even more restrictive. CSV exports strip away important relational and type information, require manual maintenance, and cannot preserve transactions, foreign key relationships, or incremental updates. Daily imports also introduce data staleness and break consistency. This approach is prone to errors, slow for large datasets, and unsuitable for production workloads.

Option D, manually copying tables using Compute Engine scripts, is the most labor-intensive and error-prone option. It requires writing and maintaining custom ETL scripts, handling retries, monitoring failures, and managing network and performance issues. Manual scripts fail to preserve consistency during ongoing writes and lack the automated synchronization provided by DMS. This creates a high operational burden and significant risk of data corruption or incomplete migration.

For these reasons, Database Migration Service is the best solution. It delivers a fully managed, secure, and low-downtime migration path to Cloud SQL by enabling automated continuous replication and reliable cutover. It minimizes risk, eliminates manual overhead, and supports production-grade database modernization with confidence and efficiency.

Question 33:

A company wants to implement serverless ETL for structured and semi-structured data uploaded by multiple teams. Which architecture should the Cloud Architect recommend?

A) Use Cloud Functions triggered by Cloud Storage uploads, process data, and load into BigQuery.
B) Use Compute Engine to run nightly ETL scripts.
C) Store data in Firestore and process hourly with Cloud Functions.
D) Use App Engine Standard to process data manually.

Answer: A) Use Cloud Functions triggered by Cloud Storage uploads, process data, and load into BigQuery.

Explanation:

Cloud Functions supports event-driven ETL with automatic scaling. Cloud Storage and BigQuery provide durable storage and analytics. Options B, C, and D lack serverless scaling, efficiency, or real-time processing. Security, monitoring, and IAM follow best practices.

Question 34:

A company wants to implement a scalable, secure, and low-latency backend for a mobile gaming app. Which architecture should the Cloud Architect recommend?

A) Deploy backend services on GKE regional clusters with Cloud Load Balancing and Memorystore for caching.
B) Use Compute Engine with a fixed number of VMs.
C) Deploy Cloud Functions in a single region.
D) Use Firestore for all backend processing.

Answer: A) Deploy backend services on GKE regional clusters with Cloud Load Balancing and Memorystore for caching.

Explanation:

GKE provides scalable microservices deployment. Cloud Load Balancing routes traffic globally, and  Memorystore provides low-latency caching. Options B, C, and D cannot handle high-concurrency, global traffic efficiently. Security and monitoring are applied. Deploying backend services on GKE (Google Kubernetes Engine) regional clusters with Cloud Load Balancing and Memorystore for caching is by far the most scalable, fault-tolerant, and production-grade architecture among the options presented. This design supports high availability, low latency, efficient resource utilization, and automated management—core requirements for modern backend systems that must handle unpredictable traffic patterns and deliver consistent performance. Each component in this architecture plays a crucial role in ensuring reliability and elasticity while minimizing operational overhead.

GKE regional clusters provide a highly resilient Kubernetes environment by distributing nodes and control plane components across multiple zones within a region. This enables your services to remain available even if a zone fails, a significant advantage over zonal clusters or manually managed VM setups. Kubernetes automates container orchestration, scaling, deployment rollouts, rollback strategies, resource allocation, and health checks. As a result, applications can scale up or down automatically, responding to traffic spikes or reducing usage during quieter periods to control costs. GKE’s integration with Google Cloud’s IAM, networking, observability, and security systems creates a cohesive and secure environment without the complexity of managing Kubernetes from scratch.

Cloud Load Balancing integrates seamlessly with GKE, providing global or regional load balancing across backend services. It supports autoscaling, distributing traffic evenly across instances and zones while maintaining high availability. It also supports advanced routing features, SSL termination, and defense against DDoS attacks through Cloud Armor. This ensures that users always connect to the nearest available instance with minimal latency. By combining Cloud Load Balancing with a regional GKE setup, you create an infrastructure that is both resilient and optimized for performance.

Memorystore adds an essential caching layer to the architecture. Built on Redis or Memcached, Memorystore provides extremely low-latency in-memory caching for frequently accessed data such as session information, API responses, user profiles, product lists, or precomputed results. Caching significantly reduces backend load, speeds up response times, and improves the overall user experience. Because Memorystore is fully managed, developers do not need to worry about patching, failover, or cluster management. When connected to GKE applications, it helps maintain high throughput and consistent performance even under heavy load.

In contrast, option B—using Compute Engine with a fixed number of VMs—lacks elasticity and requires manual scaling and maintenance. Fixed VM fleets cannot keep up with sudden demand spikes, leading to downtime or degraded performance. It also creates an unnecessary operational burden, as administrators must patch systems, manage autoscaling logic, and handle failover manually.

Option C, deploying Cloud Functions in a single region, is insufficient for backend services that require consistent low latency, multi-zone redundancy, or complex stateful workloads. Cloud Functions are excellent for small, event-driven tasks, but they are not designed to run complex backend APIs, long-running processes, or containerized microservices. Running them in a single region introduces a single point of failure.

Option D, using Firestore for all backend processing, is fundamentally misaligned with backend architecture needs. Firestore is a NoSQL database, not a compute platform. It cannot replace application servers, business logic execution, or orchestration mechanisms required for robust backend processing. Relying on it alone severely limits flexibility and scalability.

Therefore, option A stands out as the most resilient, scalable, and cloud-native solution, capable of supporting demanding production workloads with high availability and superior performance.

Question 35:

A company wants to implement event-driven architecture for processing orders with minimal operational overhead and automatic scaling. Which services should the Cloud Architect use?

A) Cloud Functions triggered by Pub/Sub messages, store processed data in Cloud SQL.
B) Compute Engine instances polling Cloud SQL for orders.
C) Firestore with scheduled batch processing via Cloud Functions.
D) Cloud Storage with nightly ETL scripts on Compute Engine.

Answer: A) Cloud Functions triggered by Pub/Sub messages, store processed data in Cloud SQL.

Explanation:

Pub/Sub ensures scalable event ingestion. Cloud Functions handle serverless, auto-scaling processing. Cloud SQL stores transactional data reliably. Options B, C, and D are operationally heavy or introduce latency. Security, IAM, and monitoring best practices are applied. Using Cloud Functions triggered by Pub/Sub messages and storing the processed data in Cloud SQL is the most efficient, scalable, and event-driven architecture among the provided options. This approach leverages Google Cloud’s serverless ecosystem to create a responsive workflow that processes orders or events as soon as they occur, ensuring low latency and high reliability. It also reduces operational overhead by eliminating the need to manage servers or continuously running compute systems, while still maintaining the structured and relational integrity of Cloud SQL for transactional data storage.

The pipeline begins with Pub/Sub, which is an ideal entry point for order messages or similar event-driven workloads. As a globally distributed messaging service, Pub/Sub can handle extremely high throughput, scaling automatically to manage bursts in traffic. Rather than polling or batch-processing data, events flow directly into Pub/Sub the moment they are generated. This removes latency and avoids wasted compute cycles, allowing the system to react instantly to new orders. Pub/Sub also provides durable storage for messages until they are acknowledged, ensuring that no orders are lost even if downstream systems experience temporary delays.

Cloud Functions plays a central role in making the pipeline serverless and reactive. Functions are automatically triggered each time a Pub/Sub message arrives, meaning they operate only when needed. This event-driven model ensures that compute resources are provisioned on demand and released immediately after processing, optimizing cost and operational efficiency. Cloud Functions can parse the incoming order data, validate fields, enrich the message with additional information, and apply business logic before writing the final results to Cloud SQL. Because Cloud Functions scales automatically with traffic, it can handle both small and large workloads without manual intervention.

Cloud SQL serves as the final destination for processed order data. As a fully managed relational database, Cloud SQL supports transactional integrity, strong consistency, and SQL querying—all essential features for systems that manage orders, customers, inventory, or payments. Cloud SQL’s compatibility with MySQL, PostgreSQL, and SQL Server makes it easy for teams to adopt without learning new database paradigms. It also integrates smoothly with reporting tools and application backends. By storing processed data in Cloud SQL, the system ensures that order data remains structured, consistent, and accessible for real-time lookups, dashboards, or downstream reporting.

The alternative options are far less suitable. Option B suggests using Compute Engine instances to poll Cloud SQL for orders, which is highly inefficient. Polling introduces unnecessary delays, burns compute resources continuously, and creates unnecessary operational complexity around managing virtual machines, scaling policies, OS updates, and failure handling. Option C proposes Firestore with scheduled batch processing via Cloud Functions, which is not appropriate for real-time order processing. Firestore is optimized for document storage, not relational transactions, and scheduled batch processing adds latency that might delay critical operations. Option D uses Cloud Storage with nightly ETL scripts on Compute Engine, an approach that is too slow for time-sensitive operations and requires heavy maintenance, manual scheduling, and server management.

In contrast, the combination of Pub/Sub, Cloud Functions, and Cloud SQL offers the right balance of real-time performance, scalability, and efficient data management. It creates a responsive, low-latency, and maintainable system that aligns with modern cloud-native best practices, making option A the best choice.

Question 36:

A company wants to implement real-time analytics on user behavior across mobile apps. Which architecture should the Cloud Architect recommend?

A) Stream events to Pub/Sub, process with Dataflow, store results in BigQuery, and visualize with Looker Studio.
B) Store events in Cloud Storage and analyze weekly.
C) Use Compute Engine to process logs hourly.
D) Store events in Firestore and process with Cloud Functions daily.

Answer: A) Stream events to Pub/Sub, process with Dataflow, store results in BigQuery, and visualize with Looker Studio.

Explanation:

Pub/Sub supports real-time event ingestion. Dataflow provides streaming ETL. BigQuery allows fast analytics, and Looker Studio provides dashboards. Options B, C, and D are slow or unscalable. Security, monitoring, and IAM best practices are appliedUsing Pub/Sub for event ingestion, Dataflow for processing, BigQuery for storage, and Looker Studio for visualization represents the most powerful, scalable, and real-time analytics pipeline on Google Cloud. This architecture is built specifically for high-throughput streaming data and provides immediate insights, automated processing, and serverless scalability—making it ideal for operational dashboards, monitoring systems, IoT analytics, user behavior tracking, and event-driven applications. Each component works seamlessly with the others, forming a modern, cloud-native pipeline capable of handling both present workloads and future growth without major architectural changes.

The process begins with Pub/Sub, Google Cloud’s fully managed, global messaging service that ingests streaming events at massive scale. Whether events come from mobile apps, servers, IoT devices, or application logs, Pub/Sub provides low-latency delivery with guaranteed durability and automatic scaling. Because Pub/Sub is decoupled, producers and consumers operate independently, improving reliability and reducing system complexity. It is designed for real-time systems, ensuring that events flow into the pipeline the moment they occur.

Next, Dataflow processes these incoming events using streaming pipelines. Dataflow is Google’s managed service for executing Apache Beam pipelines and is specifically optimized for both batch and streaming workloads. In a streaming context, Dataflow can handle real-time transformations such as windowing, filtering, joining, enrichment, aggregation, and anomaly detection. One key advantage is its autoscaling capability, which dynamically allocates resources based on incoming traffic. This ensures efficient cost usage while maintaining high performance. Dataflow’s built-in reliability, exactly-once processing guarantees, and checkpointing further support accurate real-time analytics without data duplication or loss.

The processed data is then stored in BigQuery, Google’s serverless, petabyte-scale analytics warehouse. BigQuery excels at real-time data ingestion, allowing data to become queryable within seconds. This makes it perfect for operational dashboards and event-driven insights. BigQuery’s columnar storage and distributed execution engine allow users to run complex analytical SQL queries at high speed without worrying about underlying infrastructure or indexing. Features like partitioning, clustering, and materialized views further improve query performance and reduce cost. Since BigQuery integrates natively with Dataflow, streaming pipelines can write rows continuously with minimal configuration.

Finally, Looker Studio provides visualization and dashboarding. As a lightweight, flexible BI tool, Looker Studio can connect directly to BigQuery using a secure, real-time connector. This enables dashboards to update instantly when new data arrives. Users can create interactive charts, filters, custom calculations, and automated reports without writing code. With BigQuery handling the heavy analytical workload, Looker Studio can serve dashboards to many users simultaneously without performance degradation. This combination supports near–real-time monitoring, allowing organizations to react quickly to trends, anomalies, or operational issues.

The alternative options fall short in key areas. Option B—storing events in Cloud Storage and analyzing weekly—cannot support real-time analytics and introduces long delays. Option C—processing logs hourly on Compute Engine—requires manual server maintenance, scaling, and custom scripts, making it inefficient and fragile. Option D—storing events in Firestore and processing daily with Cloud Functions—relies on services not optimized for high-volume streaming analytics and introduces significant latency.

Therefore, option A is the most robust, efficient, and scalable solution. It embraces the strengths of Google Cloud’s event-driven and analytics ecosystem, ensuring real-time insights, minimal management overhead, and excellent long-term scalability for any organization handling continuous streams of data.

Question 37:

A company wants to implement a secure multi-region backup solution for its database while minimizing cost. Which architecture should the Cloud Architect recommend?

A) Daily snapshots in Cloud SQL are stored in a single region.
B) Enable automated backups in Cloud SQL with cross-region storage using CMEK.
C) Manual backup scripts copying the database to Cloud Storage weekly.
D) Store backups on Compute Engine local disks.

Answer: B) Enable automated backups in Cloud SQL with cross-region storage using CMEK.

Explanation:

Automated backups ensure reliability. Cross-region storage ensures disaster recovery. CMEK provides encryption at rest. Options A, C, and D are either single-region, manual, or not durable. Monitoring and IAM policies enforce security.

Question 38:

A company wants to implement predictive analytics for marketing campaigns using historical customer data. Which architecture should the Cloud Architect recommend?

A) Store data in BigQuery, train ML models using Vertex AI, and deploy for predictions.
B) Store data in Cloud SQL and use scheduled Compute Engine scripts.
C) Use Firestore for historical data and Cloud Functions for predictions.
D) Store data in Cloud Storage and analyze manually offline.

Answer: A) Store data in BigQuery, train ML models using Vertex AI, and deploy for predictions.

Explanation:

BigQuery handles large datasets efficiently. Vertex AI allows training and serving ML models. Options B, C, and D are not scalable or suitable for large-scale predictive analytics. Security, monitoring, and IAM best practices are applied. Using BigQuery to store data, training machine learning models with Vertex AI, and deploying those models for real-time or batch predictions is the most modern, scalable, and efficient approach for building an end-to-end ML pipeline on Google Cloud. This architecture takes full advantage of Google’s serverless analytics and AI platforms, enabling organizations to process massive datasets, develop accurate models, and deliver predictions with minimal operational overhead. It is designed for scenarios where data volume is large, automation is important, and prediction quality must continuously improve as more data becomes available.

BigQuery serves as the ideal foundation for storing training data because it is a fully managed, petabyte-scale analytical warehouse. Its columnar storage, distributed processing, and support for SQL allow data scientists and engineers to quickly explore datasets, run aggregations, perform feature engineering, and join multiple data sources efficiently. BigQuery also integrates seamlessly with Vertex AI, meaning datasets can be exported directly or accessed through BigQuery ML for preprocessing. This tight integration accelerates experimentation and reduces the complexity of moving data between systems. Additionally, BigQuery supports time-partitioned tables, clustering, and materialized views, all of which improve performance and reduce cost—critical features when preparing large training datasets.

Vertex AI provides a unified platform for building, training, tuning, and deploying machine learning models. It supports custom training using frameworks like TensorFlow and PyTorch, as well as AutoML for users who prefer automated feature extraction and model optimization. Vertex AI simplifies the ML lifecycle by providing managed Jupyter notebooks, experiment tracking, hyperparameter tuning, and scalable distributed training. After a model is trained, Vertex AI makes deployment straightforward with options for online real-time predictions or batch predictions. The platform also offers monitoring tools, such as model performance tracking and drift detection, ensuring the model remains accurate over time.

When combined, BigQuery and Vertex AI form a powerful pipeline: data flows into BigQuery, transformations and feature engineering occur using SQL or Dataflow, and Vertex AI trains and deploys the model without requiring complex infrastructure management. This architecture is ideal for use cases such as customer behavior prediction, anomaly detection, forecasting, image recognition, recommendation systems, and more.

In contrast, storing data in Cloud SQL and using scheduled Compute Engine scripts, as proposed in option B, introduces considerable maintenance overhead. Cloud SQL is suitable for transactional workloads, not large-scale analytical processing. Compute Engine scripts require managing virtual machines, patching, scaling, and maintaining custom code pipelines. This approach lacks the automation, scalability, and advanced tooling available in BigQuery and Vertex AI, making it inefficient for enterprise-level ML workflows.

Option C suggests using Firestore for historical data with Cloud Functions for predictions, which is not appropriate for training or large-scale inference. Firestore is optimized for real-time document storage, not analytical queries or batch data processing. Cloud Functions are event-driven and lightweight, but not designed for running ML models or handling large prediction workloads.

Option D, storing data in Cloud Storage and analyzing manually offline, lacks automation, scalability, and repeatability. Manual analysis is slow, error-prone, and unsuitable for production ML systems where continuous retraining and deployment are essential.

Therefore, option A offers the strongest architecture—one that is scalable, automated, cost-effective, and aligned with best practices for cloud-based machine learning.

Question 39:

A company wants to implement a serverless workflow for processing customer support tickets uploaded as files. Which architecture should the Cloud Architect recommend?

A) Cloud Functions triggered by Cloud Storage uploads, process files, and store results in BigQuery.
B) Compute Engine VMs running nightly scripts.
C) Firestore for tickets and Cloud Functions hourly.
D) App Engine Standard for manual processing.

Answer: A) Cloud Functions triggered by Cloud Storage uploads, process files, and store results in BigQuery.

Explanation:

Serverless architecture ensures auto-scaling and minimal operational overhead. Cloud Storage triggers Cloud Functions, and BigQuery allows analytics. Options B, C, and D are operationally heavy or introduce latency. Security and monitoring follow best practices.

Question 40:

A company wants to implement multi-tenant analytics dashboards while ensuring data isolation and scalability. Which architecture should the Cloud Architect recommend?

A) Use BigQuery with separate datasets per tenant and Looker Studio for visualization.
B) Store all tenant data in a single dataset with shared access.
C) Use Firestore for analytics data and App Engine for dashboards.
D) Store data in Cloud Storage and use Cloud Functions for visualization.

Answer: A) Use BigQuery with separate datasets per tenant and Looker Studio for visualization.

Explanation:

Separate datasets ensure data isolation and security per tenant. BigQuery allows scalable analytics, and Looker Studio provides dashboards. Options B, C, and D compromise isolation, scalability, or performance. Security, monitoring, and IAM policies enforce tenant-specific access. Using BigQuery with separate datasets per tenant and integrating Looker Studio for visualization is the most secure, scalable, and well-structured approach for a multi-tenant analytics solution on Google Cloud. This architecture directly aligns with industry best practices for modern SaaS analytics platforms, especially where strong data isolation, performance, and real-time insight generation are critical. By assigning each tenant its own dataset in BigQuery, organizations can enforce fine-grained access control while still maintaining a unified analytical environment that performs reliably at scale. This setup supports compliance, strengthens security, and allows each tenant to query only their authorized data without risk of accidental exposure.

BigQuery is built for petabyte-scale analytics, offering near-instant query execution and effortless scaling. In a multi-tenant environment, it becomes crucial to avoid data overlap or misconfigurations that could lead to cross-tenant visibility issues. By creating separate datasets per tenant, administrators can use IAM permissions to restrict dataset access to specific users or groups. This model allows the platform to enforce strict boundaries while still centralizing data storage within a single BigQuery project or multiple pprojectssdepending on organizational needs. Additionally, performance benefits arise because tenant datasets can be tuned, partitioned, or organized independently, preventing the “noisy neighbor” problem that occurs when tenants share the same tables.

Looker Studio provides a flexible, user-friendly visualization layer that integrates seamlessly with BigQuery. It enables tenants to access dashboards without exposing the underlying structure or other tenants’ datasets. Each dashboard can be preconfigured with custom connectors pointing only to that tenant’s dataset. With Looker Studio’s robust features such as filters, interactive visualizations, and scheduled reports, each tenant gains a rich analytical experience. Furthermore, Looker Studio can enforce secure, direct data connections, ensuring real-time query execution without data copying or manual export.

In contrast, the alternatives have clear disadvantages and fail to meet essential multi-tenant analytics requirements. Option B, storing all tenant data in a single dataset with shared access, introduces major security risks. Even with row-level security or filters, misconfigurations can easily expose data across tenants. Performance also degrades because all tenants share the same dataset structure, and query patterns from one tenant can impact others. It lacks the clean separation that enterprise SaaS platforms rely on for compliance and operational reliability.

Option C, using Firestore for analytics data and App Engine for dashboards, is not suitable for analytical workloads. Firestore is optimized for transactional, document-based storage, not complex analytical queries or aggregations. Running analytics on Firestore data is expensive, slow, and inefficient. App Engine dashboards would also require custom implementation and offer far less analytical capability compared to Looker Studio.

Option D proposes storing data in Cloud Storage and using Cloud Functions for visualization, which is fundamentally flawed for analytics. Cloud Storage is built for object storage, not queryable datasets. Extracting analytics from Cloud Storage requires additional processing steps, temporary tables, or batch jobs. Cloud Functions cannot serve as a visualization tool; they would only act as compute triggers and do not provide dashboarding functionality.

In summary, option A—BigQuery with separate datasets per tenant and Looker Studio for visualization—is the superior solution because it ensures strong data isolation, high performance, enterprise-grade security, and rich visualization capabilities. It is designed for multi-tenant SaaS environments, supports large-scale analytics, and enables each tenant to access powerful dashboards without compromising data integrity or privacy.