Managing and provisioning a cloud solution infrastructure

A logistics company is deploying a Cloud Bigtable cluster to store real-time vehicle tracking events. Each record includes a vehicle ID, a timestamp, and GPS coordinates. During load testing with millions of concurrent writes, engineers observe that most traffic is hitting a single node while others remain nearly idle, causing high write latency. The current row key is a monotonically increasing timestamp prefix. What is the most appropriate change to resolve the throughput bottleneck?

A media streaming company serves premium video segments (thousands of small HLS chunk files per title) from a Cloud Storage bucket fronted by an external HTTP(S) load balancer with Cloud CDN enabled. Only authenticated, paying subscribers may access these files, and each subscriber's session grants access to many files under a common URL path prefix. The security team wants to avoid generating and managing a separate token for every individual file request. Which access-control mechanism should the architect configure?

A financial services company runs a production trading platform in Google Cloud and requires a private, high-throughput connection to its on-premises data center. The network team demands a connectivity architecture that qualifies for the 99.99% availability SLA from Google. They plan to use Dedicated Interconnect. Which configuration must the architect implement to meet the 99.99% SLA requirement?

A financial analytics company runs hundreds of Compute Engine instances across multiple subnets in a Shared VPC. These instances have no external IP addresses but must make frequent outbound HTTPS calls to a third-party market-data provider's public API. During peak trading hours, engineers observe connection failures and log entries indicating dropped outbound connections, while off-peak traffic works fine. Investigation shows the instances are hitting a limit on simultaneous outbound connections to the same destination endpoint. What is the most appropriate action to resolve this?

A logistics company runs a globally distributed inventory system on Cloud Spanner. During regional promotional events, read and write throughput spikes unpredictably, and engineers currently adjust the instance's node count manually. This has led to over-provisioning during quiet periods and latency violations when they react too slowly to spikes. They want the database capacity to adjust to load automatically while keeping utilization within target thresholds. What should the architect recommend?

A retail company runs a Compute Engine application tier in a custom-mode VPC. They are provisioning a new Cloud SQL for PostgreSQL instance and require that all traffic between the application VMs and the database stay on Google's internal network, with no public IP exposure on the database. The application VMs must connect using a private IP address. What must the architect configure to enable this connectivity?

A retail analytics company runs a reporting application backed by a single Cloud SQL for PostgreSQL instance in us-central1. The primary instance handles all transactional writes, but a growing team of analysts in Europe runs heavy read-only dashboards that are now causing CPU contention and slow queries on the primary. Leadership wants to reduce read load on the primary and improve dashboard response times for the European users, while keeping operational overhead low and avoiding application rewrites. What should the architect do?

A visual effects studio is building a rendering farm on Compute Engine consisting of 300 VMs that must simultaneously read and write to a shared POSIX-compliant file system. The workload requires sustained aggregate throughput of several GB/s and low-latency access to a 50 TB working set. The team wants a fully managed service and prefers not to operate their own file server cluster. Which storage option best meets these requirements?

A subscription media company wants to predict customer churn using two years of historical account data stored in BigQuery. The data science team is small and lacks deep expertise in building and tuning custom neural network architectures, but they need an accurate classification model deployed as an online prediction endpoint within a few weeks. They want to minimize the amount of custom modeling code they write while still leveraging their structured tabular data. Which approach on the Gemini Enterprise Agent / Vertex AI platform best meets these requirements?

A legal services company wants to build an internal application where staff upload contract PDFs and receive concise summaries plus answers to natural-language questions about each document. The team has no ML engineers and wants to minimize custom model development and infrastructure management while grounding responses in the uploaded documents. Which approach best meets these requirements?

A retail company collects thousands of free-text customer product reviews daily in multiple languages. The product team wants to automatically detect the overall sentiment (positive, negative, neutral) and extract mentioned entities (product names, brands) from each review, feeding results into BigQuery for dashboards. The team has no ML engineers and wants the fastest path with minimal operational overhead. Which approach should the architect recommend?

A telecommunications company runs a call center that records thousands of customer support calls daily in various languages. The architecture team wants to build an end-to-end pipeline that transcribes each recorded call, detects the sentiment of the conversation, and stores structured results for downstream analytics. The team has no data scientists and wants to avoid training or hosting custom models. Which approach using the Gemini Enterprise Agent Platform and Google Cloud prebuilt APIs best meets these requirements?

A global customer-support platform receives incoming support tickets in dozens of languages. The engineering team wants to automatically detect each ticket's language and translate the body into English before routing it to agents. They have no ML expertise and no labeled training data, and they need production-grade quality with minimal operational overhead. Which approach best meets these requirements?

A retail company runs applications on Compute Engine instances in a VPC connected to their on-premises data center via Cloud VPN. The Compute Engine workloads must resolve private hostnames for internal on-premises services (e.g., ldap.corp.internal), and on-premises servers must resolve the private DNS names of Google Cloud resources managed in a Cloud DNS private zone. What should the architect configure to enable bidirectional private DNS resolution?

A logistics company stores 500 TB of shipment scan images in a Cloud Storage bucket. Analysis shows that images are accessed frequently for the first 30 days, occasionally between 30 and 90 days, and essentially never after 1 year (though they must be retained for 7 years for compliance). The architect wants to minimize storage cost while ensuring the rare retrievals within the first year remain reasonably quick and no manual intervention is required. What should the architect configure?

A retail company runs a stateless web application on a managed instance group behind a global load balancer. User session state is currently stored in the application memory, which causes users to be logged out whenever instances are scaled in or replaced. The team wants a fully managed, low-latency shared session store. They require automatic failover with minimal downtime if the primary node fails, but they do not need cross-region replication. Which Google Cloud solution should the architect recommend?

An insurance company wants to automatically extract structured fields (policy number, claimant name, dates, and dollar amounts) from millions of scanned claim forms and unstructured PDFs. The team has no ML engineers and needs a solution that requires no model training, returns structured key-value pairs, and can scale to process large batches. Which Google Cloud approach best meets these requirements with the least operational effort?

A financial services company runs a fleet of Compute Engine VMs in a subnet with no external IP addresses for security compliance. These VMs must upload processed reports to Cloud Storage and pull secrets from Secret Manager, but all traffic to Google APIs must stay off the public internet and no Cloud NAT egress is permitted. What should the architect configure to enable this access?

A financial services company runs a stateful licensed application on a single Compute Engine VM in the us-central1-a zone. The application stores critical data on a zonal persistent disk. The team needs to ensure that if the entire zone becomes unavailable, they can bring the application back up quickly in another zone within the same region without restoring from backups, while keeping infrastructure changes minimal. What should they configure for the storage layer?

A large enterprise has separate teams managing distinct application projects, but a central network team must retain full control over all subnets, firewall rules, and routing. Application teams should be able to deploy Compute Engine VMs into predefined subnets without the ability to modify network configuration. Which Google Cloud networking design best meets these requirements?