To evaluate cloud analytics with low risk, archive about 100 TB of logs to the cloud for analysis and long‑term disaster recovery. Which two steps should you take? (Choose two.)

For archiving 100 TB of logs and preparing them for analysis, uploading to Google Cloud Storage is a cost-effective and scalable solution for long-term storage and disaster recovery. Google Cloud Storage offers various storage classes to optimize costs based on access frequency. Loading logs into Google BigQuery allows for powerful, serverless analytics directly on the archived data. BigQuery is designed for petabyte-scale data analysis, making it ideal for querying large log datasets without managing infrastructure. Google Cloud SQL is a relational database and not suitable for 100 TB of log data. Stackdriver Logging (now Cloud Logging) is for operational logs, not for archiving and analyzing 100 TB of historical logs. Google Cloud Bigtable is a NoSQL wide-column store, better suited for high-throughput, low-latency operational data, not typically for batch analytics of archived logs.