When utilizing logging and monitoring to trigger deployment of services, what is a key consideration to ensure effective scaling?

Question

Options

• AThe frequency of log analysis to trigger scaling
• BThe exclusive use of manual scaling for greater control
• CIgnoring cost implications to prioritize performance
• DRelying solely on CPU and memory usage metrics

• A
  75% (36)
• B
  15% (7)
• C
  6% (3)
• D
  4% (2)

Explanation

Frequent log analysis is the engine that drives responsive auto-scaling: if you analyze logs too infrequently, your system reacts too slowly to traffic spikes or failures, causing degraded performance or outages before scaling kicks in. Option B (manual scaling) defeats the purpose of automated trigger-based deployment and introduces human latency. Option C is a trap - ignoring cost is never a viable strategy, since runaway scaling can result in enormous cloud bills. Option D is incomplete; relying solely on CPU/memory misses application-level signals (error rates, request latency, queue depth) that logs and monitoring capture, which are often better predictors of the need to scale.

Memory tip: Think of log analysis frequency as a heartbeat - the faster it beats, the quicker your system "feels" the pain and responds. Slow heartbeat = slow reaction = bad outcomes.

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