Metrics Analysis and Monitoring Fundamentals Questions
Fundamental concepts for metrics, basic monitoring, and interpreting telemetry. Includes types of metrics to track (system, application, business), metric collection and aggregation basics, common analysis frameworks and methods such as RED and USE, metric cardinality and retention tradeoffs, anomaly detection approaches, and how to read dashboards and alerts to triage issues. Emphasis is on the practical skills to analyze signals and correlate metrics with logs and traces.
EasyTechnical
0 practiced
Design a practical alerting rule for sustained high CPU usage on Kubernetes nodes. Specify the metric(s) you would use (node CPU usage, system load, container throttling), a PromQL-like alert expression, threshold and duration (for example > 90% for 5 minutes), grouping strategy (per-node vs cluster), and how you'd suppress alerts during planned maintenance windows or autoscaling events.
MediumTechnical
0 practiced
Compare threshold-based detection, moving-average plus standard deviation (z-score), seasonal decomposition (e.g., STL), and ML-based detection (e.g., isolation forest) for anomaly detection on product metrics with strong daily seasonality. Discuss detection latency, false positive/negative behavior, maintenance cost, and when each approach is most appropriate.
HardTechnical
0 practiced
Design an A/B experiment to compare two metric aggregation algorithms (for example: per-instance sum of counts vs average of per-instance rates) to determine which correlates better with user-facing errors. Define a clear hypothesis, treatment assignment, telemetry collection, experiment metrics (primary and secondary), required sample size considerations, statistical tests, and how to interpret results to change production aggregation.
HardSystem Design
0 practiced
Architect a metrics collection and storage pipeline for a global SaaS product that must ingest 1,000,000 distinct series and sustain 100k samples/second, support multi-region reads, retain 2 years of downsampled data and 7 days of raw data, and compute global SLOs. Describe the collector, TSDB choice, sharding and replication, downsampling strategy, cardinality control, HA design, and cost optimization techniques and explain trade-offs.
EasySystem Design
0 practiced
List the core principles for designing an effective reliability dashboard for a service team. Include layout considerations (which panels go top-left), metric selection that supports fast triage (which RED/USE metrics to show), use of thresholds and annotations (deploys, incidents), and links to runbooks and logs to support on-call decision making.
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