You recently developed an application that will be hosted on Cloud Run. You need to conduct a load test. You want to analyze the load test logs second by second to understand your Cloud Run service's

Question

Options

• AUse estimation to extrapolate performance from summary monitoring charts.
• BAnalyze the log data in BigQuery by configuring a BigQuery log sink with the appropriate inclusion
• CUse Cloud Monitoring's default log console for analysis.
• DAnalyze the log data in Cloud SQL for PostgreSQL by pushing logs to a Pub/Sub topic. Use

• A
  8% (3)
• B
  77% (30)
• C
  13% (5)
• D
  3% (1)

Explanation

Exporting Cloud Run logs to BigQuery via a log sink with an inclusion filter enables precise, second-by-second SQL analysis of log data at scale. BigQuery supports timestamp-based queries and aggregations, making it ideal for correlating traffic spikes with latency, error rates, and instance behavior over time. The inclusion filter ensures only relevant logs are exported, keeping costs manageable. Option C (Cloud Monitoring's default log console) has limited querying and aggregation capabilities and is not designed for deep analytical workloads. Option A (estimation from summary charts) is not precise enough for second-by-second analysis of rapid traffic spikes. Option D (Cloud SQL via Pub/Sub) is unnecessarily complex - Cloud SQL is a transactional relational database not optimized for log analytics at the scale Cloud Run can generate.

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