Question 11

Explanation

Option A is CORRECT. BigQuery with partitioning is ideal for petabyte-scale log storage and forensic analysis. BigQuery can handle massive datasets, scales automatically, and partitioning (e.g., by date) optimizes both storage costs and query performance for occasional analysis. When investigating specific incidents, you can query only relevant partitions, making BigQuery cost-effective for infrequent but complex analytical queries across large log datasets.

Option B is INCORRECT. While Cloud Storage is cost-effective for storing large volumes of data, you would need to process the logs with Dataflow each time you wanted to analyze them. This adds complexity and latency to forensic investigations compared to having the data already loaded and queryable in BigQuery.

Option C is INCORRECT. Bigtable is optimized for high-throughput, low-latency workloads, not for occasional complex analytical queries across petabytes of data. It would be inefficient for the forensic analysis use case described, as it lacks the SQL querying capabilities that make BigQuery well-suited for ad-hoc investigation.

Option D is INCORRECT. Cloud SQL is a relational database with significant limitations on database size (typically terabytes, not petabytes) and query performance for very large datasets. It would not scale to handle petabytes of log data and would perform poorly for complex forensic queries across large volumes of data.