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Your IoT monitoring platform ingests sensor data from 100,000 devices, generating 2 million writes per second during peak hours and 500,000 writes per second during off-peak hours. Each record is 1KB with a row key format: deviceId#timestamp. Your current 12-node Bigtable cluster experiences hotspotting on specific device ranges, with three nodes handling majority ofthe traffic while others remain underutilized. Read queries typically access the last 24 hours of data for specific devices. Storage costs are becoming a concern as you retain two years of historical data (500TB total). You need to optimize performance, eliminate hotspotting, and reduce storage costs while maintaining read latency under 10ms. What should you do-

• A. Implement row key sting wth hash prefix, configure automsted sealing with an optimal min max threshold setting, and migrate data older than 6 months to Cloud Storage.
• B. Increase cluster size to 20 nodes, implement row key salting with hash prefix, and enable compression on all column families.
• C. Change the row key format to deviceId#timestamp, implement automated scaling with an optimal min max threshold setting based on CPU utilization, and migrate data older than 6 months to Cloud Storage
• D. Split the table by device type, use separate Bigtable instances for different geographic regions, and implement read replicas for query optimization.

You recently launched a new product to the US market. You currently have two Bigtable clusters in one US region to serve all the traffic. Your marketing team is planning an immediate expansion to APAC. You need to roll out the regional expansion while implementing high availability according to Google-recommended practices. What should you do?

• A. Maintain a target of 23% CPU utilization by locating:
  cluster-a in zone us-central1-a
  cluster-b in zone europe-west1-d
  cluster-c in zone asia-east1-b
• B. Maintain a target of 35% CPU utilization by locating:
  cluster-a in zone us-central1-a
  cluster-b in zone us-central2-a
  cluster-c in zone asia-northeast1-b
  cluster-d in zone asia-east1-b
• C. Maintain a target of 23% CPU utilization by locating:
  cluster-a in zone us-central1-a
  cluster-b in zone us-central1-b
  cluster-c in zone us-east1-a
• D. Maintain a target of 35% CPU utilization by locating:
  cluster-a in zone us-central1-a
  cluster-b in zone australia-southeast1-a
  cluster-c in zone europe-west1-d
  cluster-d in zone asia-east1-b

You are using Memorystore for Redis to cache frequently accessed data and improve your application's performance. Recently, your application is experiencing sudden spikes in latency when interacting with the Memorystore for Redis instance. Upon checking the logs, you discover a high number of "evicted keys" messages. You want to reduce the occurrences of latency spikes and their impact on the application. What should you do-

• A. Scale the Memorystore instance to a larger memory size.
• B. Increase the time to live (TTL) value of all the keys within the cache.
• C. Enable read replicas. Deploy additional read replica instances to distribute read workloads.
• D. Redeploy your application to the same zone as the Memorystore instance.

HelioBank is deploying Cloud SQL for MySQL in europe-west1 to support payment processing for example.com. The compliance team requires that data at rest be encrypted with a key that they own and manage in a specific European location, and they must control the key lifecycle including rotation and disablement. They also need all client connections to be encrypted from end to end.
What should they configure?

• A. Create the Cloud SQL for MySQL instance using customer managed encryption keys and place it behind Cloud VPN for all client connectivity
• B. Create the Cloud SQL for MySQL instance with Google managed encryption keys and require SSL or TLS
• C. Create the Cloud SQL for MySQL instance using customer managed encryption keys from Cloud KMS in the required region and require SSL or TLS for client connections
• D. Create the Cloud SQL for MySQL instance with Google managed encryption keys and secure access using VPC Service Controls

BrightTrail Outfitters is moving an order processing platform from its data center to Google Cloud.
The application depends on a MySQL backend that must remain highly available because it supports mission critical transactions. The business requires a recovery time objective and a recovery point objective of no more than 12 minutes during a regional incident. You will use a Google managed database service. What should you implement to achieve the highest possible uptime for this workload?

• A. Migrate the database to multi region Cloud Spanner and refactor the schema and application so that a regional outage does not interrupt service
• B. Set up Cloud SQL for MySQL with regional high availability and rely on scheduled backups to restore a new instance in a different region when a disaster occurs
• C. Deploy Cloud SQL for MySQL as a single zone instance and add a read replica in another zone and a second replica in a distant region for disaster recovery
• D. Provision Cloud SQL for MySQL with regional high availability and create a cross region read replica that you can promote to primary if the region fails