Explain how an AIOps platform performs automated root-cause analysis across metrics, logs, and traces.
Quick Answer
It combines a service dependency/topology map with time-correlated anomalies across all three signal types, then ranks candidate root causes by which upstream anomaly best explains the timing and shape of downstream symptoms — typically surfaced as a ranked list of likely causes with supporting evidence, not a single definitive answer.
Detailed Answer
Root-cause analysis starts from "what changed and where" rather than any single signal. The platform maintains (or infers) a service topology — which services call which, and their deploy/config change history. When anomalies fire across many services roughly simultaneously, it looks for one or a small number of anomalies upstream in the dependency graph whose onset time precedes and plausibly explains the downstream anomalies (e.g., a database's latency spike explains five services timing out afterward, but not the other way around).
Logs and traces add causal detail that metrics alone can't: a trace can show a specific downstream call failing or timing out inside a request, and log clustering (grouping similar error messages) can surface a new error signature that started at the same moment as the metric anomaly — often pointing straight at a recent deploy or config change. The system typically also cross-references recent change events (deployments, config pushes, feature flag flips) since a large share of real incidents correlate directly with a recent change. The output is a ranked set of candidate causes with the supporting timeline and evidence attached, and a human confirms and acts — treating it as a definitive automated verdict is a common design mistake.
Code Example
# Simplified causal ranking: does upstream anomaly A explain downstream symptom B?
def causal_score(candidate, symptom, topology):
if not topology.has_path(candidate.service, symptom.service):
return 0.0
timing_score = 1.0 if candidate.onset <= symptom.onset else 0.0
proximity = max(0, 1 - (symptom.onset - candidate.onset) / MAX_LAG_SECONDS)
return timing_score * proximity * candidate.anomaly_severityInterview Tip
Stress that RCA output should be a ranked, evidence-backed list rather than a single confident answer — overselling automated certainty is a red flag interviewers listen for.