What are the three data pillars that support observability?

• A. Profiles, metrics, and charts
• B. Metrics, logs, and traces
• C. Users, sessions, events
• D. Events, dashboards, alarms

Answer: (B)

Three data types define observability: metrics, logs, and traces. Metrics are numeric, time-series measurements such as request rate, latency, error rate, and resource usage. They give a quick, at-a-glance view of how the system behaves over time and help you spot anomalies or trends. Logs capture detailed, time-stamped events and messages that provide context about what happened, when, and under what conditions, which is crucial for deep debugging and understanding specific incidents. Traces map the path of a single request as it travels across services, with each span showing how long different components take and where delays occur, making it possible to see the end-to-end flow in a distributed system.

Together, these three pillars let you monitor overall health (metrics), investigate specifics (logs), and diagnose distributed performance issues (traces). Other options mix in visualizations or non-observability data (like profiling data or user analytics) that aren’t the foundational data types used to observe and understand system behavior.