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.
HardSystem Design
0 practiced
A newly adopted instrumentation library accidentally started adding a dynamic label 'request_id' to metrics across many services, causing cardinality explosion and storage overload. Propose an automated detection and enforcement system to catch such regressions early, sanitize metrics at runtime or ingest-time if needed, integrate with CI and dev workflows for prevention, and outline safe rollback/remediation steps.
MediumTechnical
0 practiced
Explain the differences between Prometheus histograms and summaries for measuring latency. Discuss how each affects percentile computation, how aggregation across instances works (or doesn't), exemplar support, and provide best-practice guidance for choosing between them in a microservices environment.
HardSystem Design
0 practiced
Design a global label/tagging schema for a microservices platform that balances flexible slicing (service, region, environment, cluster, version) with cardinality constraints. Specify naming conventions, which labels are dynamic vs static, guidance for avoiding labels that cause explosions (user_id, request_id), and enforcement mechanisms (linters, CI checks, runtime scrubbing).
EasyTechnical
0 practiced
What is metric cardinality? Provide an example of a harmful high-cardinality label (for example, request_id or user_id) and explain three practical strategies to reduce cardinality while retaining actionable signal: e.g., label bucketing, sampling, and using low-cardinality derived fields. Describe a simple rule you would give developers to avoid cardinality explosions.
HardSystem Design
0 practiced
Design an automated incident playbook that uses metric signals to trigger a safe rollback of a Kubernetes deployment when reliability degrades. Specify which metrics and thresholds should be considered (e.g., p99 latency, 5xx rate, pod restarts), how to avoid false positives during rolling deploys, safety checks before rollback (canary checks, cooldowns), testing strategy, integration with on-call workflows, and how rollback actions affect SLO/error-budget calculations.
Unlock Full Question Bank
Get access to hundreds of Metrics Analysis and Monitoring Fundamentals interview questions and detailed answers.
Sign in to ContinueJoin thousands of developers preparing for their dream job.