When a data source becomes unavailable, what should you do?

When a data source becomes unavailable, what should you do?

• A. Do not log to avoid noise.
• B. Log the incident to monitor and diagnose and inform future improvements.
• C. Log only after multiple failures.
• D. Notify only after user complaints.

Answer: (B)

When a data source becomes unavailable, you want visibility into what happened and when, so you can respond quickly and prevent recurrence. Logging the incident creates a trace that supports real-time monitoring, helps diagnose the root cause, and feeds post-incident analysis to drive future improvements. This practice makes outages visible, measurable, and actionable, which is essential for preserving reliability and user trust. Not logging to avoid noise misses critical information and leaves you without a record to analyze after the fact. Proper logging can be paired with structured severity levels and filtering to keep noise down while preserving valuable data. Waiting to log until there are multiple failures delays detection and fixes, compounding impact and making it harder to understand what went wrong or when it began. Waiting for user complaints is reactive and can harm user experience. Proactive detection and logging enable faster containment and proactive improvements before users are affected.

When a data source becomes unavailable, you want visibility into what happened and when, so you can respond quickly and prevent recurrence. Logging the incident creates a trace that supports real-time monitoring, helps diagnose the root cause, and feeds post-incident analysis to drive future improvements. This practice makes outages visible, measurable, and actionable, which is essential for preserving reliability and user trust.

Not logging to avoid noise misses critical information and leaves you without a record to analyze after the fact. Proper logging can be paired with structured severity levels and filtering to keep noise down while preserving valuable data.

Waiting to log until there are multiple failures delays detection and fixes, compounding impact and making it harder to understand what went wrong or when it began.

Waiting for user complaints is reactive and can harm user experience. Proactive detection and logging enable faster containment and proactive improvements before users are affected.