Your company wants to try out the cloud with low risk. They want to archive approximately 100 TB of their log data to the cloud and test the analytics features available to them there, while also retaining that data as a long-term disaster recovery backup. Which two steps should you take? (Choose two.)

Uploading log files into Google Cloud Storage (GCS) is an excellent first step for archiving large amounts of data due to its low cost, high durability, and various storage classes suitable for long-term retention and disaster recovery. GCS can store 100 TB efficiently. Loading logs into Google BigQuery allows for powerful analytics directly on the archived data. BigQuery is a serverless, highly scalable, and cost-effective data warehouse designed for analyzing petabytes of data, making it ideal for testing analytics features on the log data. Google Cloud SQL is a relational database service, not suitable for archiving 100 TB of unstructured log data or for large-scale analytics on such data. Google Stackdriver (now Google Cloud Logging and Monitoring) is primarily for operational logging and monitoring of cloud resources, not for archiving 100 TB of historical logs or performing complex analytics on them. Google Cloud Bigtable is a NoSQL wide-column database, optimized for high-throughput, low-latency workloads, not primarily for cost-effective archiving or general-purpose SQL-based analytics.