Designing and planning a cloud solution architecture

A media company runs a data analytics platform on Google Cloud with a stable baseline of Compute Engine instances that operate 24/7 for at least the next three years. During monthly reporting periods, the workload spikes with additional short-lived instances that run for a few hours. The finance team wants to minimize total compute cost while keeping the architecture simple and avoiding upfront lump-sum payments. Which pricing strategy should the architect recommend?

A logistics company is building a new fleet-management backend on Google Cloud. The application must store structured shipment records with a well-defined relational schema, support ACID transactions across multiple tables, and serve a moderate but steady workload from a single region. The engineering team wants a fully managed database and expects data to grow to a few hundred gigabytes, with no requirement for global distribution or horizontal write scaling beyond a single primary. Which storage service best meets these requirements at the lowest operational and licensing overhead?

A genomics research startup runs nightly batch pipelines that process large datasets on Compute Engine. The jobs are fault-tolerant, can be checkpointed, and can restart if interrupted. They currently run on 200 standard on-demand n2 instances and take about 6 hours. Leadership wants to reduce compute cost as much as possible without missing the 10-hour nightly processing window, and the workload has no strict per-instance uptime requirement. Which compute design best meets these business and technical requirements?

A logistics company runs a nightly analytics pipeline composed of several dependent steps: extracting data from Cloud SQL, transforming it with a Dataflow job, loading results into BigQuery, and finally triggering a report-generation Cloud Run service. The steps must execute in strict order, with automatic retries on transient failures and clear visibility into which step failed. The team currently chains these steps with brittle cron jobs and shell scripts that offer no dependency management or observability. As the cloud architect, which Google Cloud service should you recommend to orchestrate this multi-step, dependency-aware workflow?

A pharmaceutical company is designing a data platform for clinical trial results. Regulators require that all raw trial data be retained for 15 years and remain retrievable within a few hours if audited. Only the most recent 90 days of data is queried frequently by analysts; older data is accessed at most once or twice a year. The company wants to minimize total storage cost while satisfying the retention and retrievability requirements. Which storage design should the architect recommend?

A European financial services company is building a new customer-facing platform on Google Cloud. Under GDPR and their national banking regulator's rules, all personally identifiable customer data must remain physically stored within the European Union, and the company must be able to demonstrate this to auditors. The application also requires low-latency access for users concentrated in Germany and France. As the cloud architect, which design approach best satisfies these business and compliance requirements while minimizing latency?

A financial services company is migrating a customer-records application to Google Cloud. Their regulator requires that the company retain sole control over the encryption keys protecting customer data at rest, including the ability to revoke Google's access to decrypt the data at any time, while still using Google-managed storage services. The security team also wants to avoid operating and patching their own on-premises key infrastructure. Which encryption key management approach best satisfies these requirements?

A media startup runs an internal-facing content approval portal that receives no traffic overnight and on weekends, then bursts to a few hundred concurrent editors during weekday business hours. The application is a stateless containerized web service with unpredictable request patterns. Leadership wants to minimize idle cost while ensuring the service scales rapidly during peaks, and the team wants to avoid managing node infrastructure. Which compute platform best meets these requirements?

A financial services company is designing a real-time fraud detection service on Google Cloud. The model is a deep neural network that requires GPU acceleration to meet a strict 50 ms p99 inference latency target. Traffic is highly variable: near-zero overnight and peaking at 30,000 requests per second during business hours. The architecture team must minimize cost while meeting the latency SLA and avoiding manual capacity management. Which compute design should the architect recommend?

An automotive company collects sensor readings from a fleet of connected vehicles. Each vehicle emits several thousand timestamped metrics per second, and analysts need to run range queries over time windows (for example, all engine temperature readings for a vehicle over the past 30 days) with single-digit millisecond read latency at massive scale. The dataset grows to petabytes and writes are extremely high-throughput. Which Google Cloud storage service best meets these requirements?

A software company is designing the CI/CD foundation for a greenfield migration to Google Cloud. Their build pipeline produces both Docker container images and language-specific packages (Maven and npm). Compliance requires that every artifact be scanned for known vulnerabilities before deployment and that deployments be blocked if an image contains a critical CVE. They want a single Google-managed service that stores all artifact types and integrates natively with vulnerability scanning and admission control. Which design should the architect recommend?

A software company runs about 60 non-production (development and QA) VM-based environments on Compute Engine. Developers work standard business hours (roughly 9 AM to 6 PM, Monday to Friday) in a single time zone, but the environments currently run 24/7, driving up costs. Management wants to significantly reduce spend on these environments without requiring developers to change how they access or provision the machines, and the environments must retain their disk state between sessions. Which approach best meets these requirements?

A logistics company runs a nightly ETL job on a self-managed Hadoop cluster hosted on Compute Engine VMs. The cluster is only fully utilized for about 3 hours each night but runs 24/7, and the operations team spends significant time patching and tuning it. Leadership wants to cut both infrastructure spend and operational overhead while keeping the existing Spark and Hive jobs running with minimal code changes. Which approach best meets these business requirements?

A financial services company runs its core banking application on Compute Engine in the us-central1 region. Regulators require the ability to recover from a full regional outage with a Recovery Time Objective (RTO) of 30 minutes and a Recovery Point Objective (RPO) of 5 minutes. The company wants to minimize ongoing costs while still meeting these targets. Which disaster recovery strategy should the architect design?

A financial services company runs a nightly batch job that consolidates transactions and must complete within a strict 4-hour window to meet regulatory reporting deadlines. The job is fault-tolerant and can checkpoint and resume from partial results. Leadership wants to minimize compute cost while guaranteeing the deadline is never missed. Which compute strategy on Google Cloud best meets these business and technical requirements?

A financial services company runs a fraud-detection application on Compute Engine. Data scientists manually export transaction logs to a VM, run Python scripts to train models weekly, and copy the resulting model files to a bucket for the serving application to load. Leadership asks the architect to envision future improvements that reduce operational toil, enable automated retraining as new data arrives, and provide reproducible, versioned model deployments — while minimizing the team's infrastructure management burden. Which evolution of the architecture best meets these goals?

A logistics company runs a monolithic Java shipment-tracking application on a fleet of Compute Engine VMs. The application works but is difficult to update, and leadership wants a roadmap for incrementally modernizing it toward a microservices architecture without a risky big-bang rewrite. New features must be delivered as independent services while the existing monolith continues to serve production traffic. As the architect, which approach should you recommend for the modernization roadmap?

A gaming company is designing a new multiplayer backend on Google Cloud. Players are distributed across North America, Europe, and Asia. The game maintains a global leaderboard and player inventory that must remain strongly consistent worldwide, tolerate regional outages, and scale to millions of writes per second. Latency for gameplay reads should be minimized in each region. Which managed data service best meets these business and technical requirements?

A global e-commerce company is designing the backend for a new order-processing platform. Business requirements state that the platform must remain available for writes even if an entire Google Cloud region becomes unavailable, provide strong transactional consistency for financial data, and scale horizontally to handle unpredictable global traffic spikes during promotional events. The team wants a fully managed database service to minimize operational overhead. Which storage solution should the architect choose?

A media startup is designing a new video-on-demand platform on Google Cloud. Their finance team is concerned about network egress costs, since most of the traffic is streaming static video files to consumers across North America, Europe, and Asia. The architect must design a network and content-delivery approach that keeps monthly egress costs as low as possible while maintaining low latency for global viewers. Which design should the architect recommend?