A is correct because BigQuery is the fully managed cloud data warehouse for analytics and supports the analytics requirement.
B is not correct because Cloud SOL does not support the expected 100 TB Additionally, Cloud SQL is a relational database and not the best fit for time-series log data formats C Is not correct because Stackdriver is optimized for monitoring, error reporting, and debugging instead of analytics queries.
D is not correct because Cloud Bigtable is optimized for read-write latency and analytics throughput not analytics querying and reporting.
E is correct because Cloud Storage provides the Coldline storage class to support long-term storage with infrequent access, which would support the long-term disaster recovery backup requirement.

https://cloud.google.com/storage/docs/request-rate
Use a naming convention that distributes load evenly across key ranges
Auto-scaling of an index range can be slowed when using sequential names, such as object keys based on a sequence of numbers or timestamp. This occurs because requests are constantly shifting to a new index range, making redistributing the load harder and less effective.
In order to maintain a high request rate, avoid using sequential names. Using completely random object names will give you the best load distribution.

The framework illustrated in the preceding diagram has four phases:
- Assess. In this phase, you assess your source environment, assess the workloads that you want to migrate to Google Cloud, and assess which VMs support each workload.
- Plan. In this phase, you create the basic infrastructure for Migrate for Compute Engine, such as provisioning the resource hierarchy and setting up network access.
- Deploy. In this phase, you migrate the VMs from the source environment to Compute Engine.
- Optimize. In this phase, you begin to take advantage of the cloud technologies and capabilities.
Reference: https://cloud.google.com/architecture/migrating-vms-migrate-for-compute-engine- getting-started

Google Cloud Dataproc is a fast, easy-to-use, low-cost and fully managed service that lets you run the Apache Spark and Apache Hadoop ecosystem on Google Cloud Platform. Cloud Dataproc provisions big or small clusters rapidly, supports many popular job types, and is integrated with other Google Cloud Platform services, such as Google Cloud Storage and Stackdriver Logging, thus helping you reduce TCO.
References: https://cloud.google.com/dataproc/docs/resources/faq

Data studio cannot be used with BigTable.
https://datastudio.google.com/data