You support an application that stores product information in cached memory. For every cache miss, an entry is logged in Stackdriver Logging. You want to visualize how often a cache miss happens…

Question

Options

• ALink Stackdriver Logging as a source in Google Data Studio. Filter the logs on the cache misses.
• BConfigure Stackdriver Profiler to identify and visualize when the cache misses occur based on the
• CCreate a logs-based metric in Stackdriver Logging and a dashboard for that metric in Stackdriver
• DConfigure BigQuery as a sink for Stackdriver Logging. Create a scheduled query to filter the

• A
  14% (3)
• B
  5% (1)
• C
  77% (17)
• D
  5% (1)

Explanation

Creating a logs-based metric in Cloud Logging (Stackdriver Logging) lets you define a filter that matches cache miss log entries and automatically increments a counter metric each time a matching log line is written. That metric can then be added to a Cloud Monitoring (Stackdriver Monitoring) dashboard to visualize cache miss frequency over time - all within the same Google Cloud observability stack with minimal setup. Option A (Data Studio) is more complex and better suited for BI reporting, not operational dashboards. Option B (Cloud Profiler) is a CPU/memory performance profiler, not a log analysis tool. Option D (BigQuery sink + scheduled queries) is a valid analytics approach but far more complex than necessary for a simple time-series visualization.

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