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.
MediumTechnical
0 practiced
Design a CI and runtime guardrail to detect and prevent accidental high-cardinality metric labels introduced by code changes in multiple languages. Describe static checks, unit tests, synthetic runs, thresholds for new series creation, feedback to developers, and how to gracefully allow legitimate high-cardinality use cases.
MediumSystem Design
0 practiced
Design a cost-optimized retention and aggregation strategy for high-cardinality user-level metrics that are needed for weekly business analysis but not necessarily at full resolution. Propose sampling, cohort rollups, TTLs, and downsampling techniques, and explain how you would preserve necessary percentile or distributional information for business queries.
HardSystem Design
0 practiced
Your Prometheus federation-based architecture struggles with slow cross-cluster dashboards. Propose an improved architecture to deliver low-latency global queries for SLOs and dashboards. Compare federation tuning, remote_write to central TSDB (Thanos/Cortex), query federation layers, and caching or precomputation, evaluating cost, freshness, latency, and operational complexity.
HardTechnical
0 practiced
Design a method to compute accurate long-term percentiles (p95/p99) when raw per-request latency samples are only available for 7 days and 1-year retention is stored as downsampled histograms or sketches. Discuss candidate algorithms (TDigest, reservoir sampling, histogram merging), expected error bounds, how to merge sketches across instances, and strategies to validate percentile accuracy over time.
EasyTechnical
0 practiced
Describe counters, gauges, histograms, and summaries in the context of a metrics system (e.g., Prometheus). For each type: define it, provide a typical use case (for example CPU usage, request count, or latency distribution), and explain how aggregation across instances and label cardinality impact storage and query semantics.
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