Trend Analysis and Anomaly Detection Questions

Covers methods for detecting and interpreting deviations in metric behavior over time and determining whether changes reflect real product or user behavior versus noise. Topics include baseline establishment, seasonality and holiday effects, time series decomposition, smoothing and aggregation choices, statistical detection techniques such as control charts, z scores, EWMA and CUSUM, thresholding strategies, and modern algorithmic approaches like isolation forest or LSTM-based detectors. Also covers visualization and dashboarding practices for communicating trends, setting sensible alerting rules, triage workflows for investigating anomalies, and assessing business impact to prioritize fixes or rollbacks.

HardTechnical

39 practiced

Compare classical statistical detectors (z-score, CUSUM, EWMA) with ML-based detectors (isolation forest, autoencoders, LSTM). For the dimensions of explainability, training-data requirement, compute cost, latency, and maintenance overhead, state which approach is preferable and why, giving concrete examples.

HardSystem Design

51 practiced

Design an integrated monitoring-to-incident flow: anomaly detection -> alert enrichment (context, recent deploys, experiments) -> automated triage -> incident creation -> runbook execution -> human escalation. For each stage explain automation points, data passed between systems, retry/backoff strategies, and safety checks to prevent auto-mitigation of critical incidents.

MediumTechnical

39 practiced

Implement in Python a class SlidingMedian(window_size) with methods add(value: float) and median() that maintains the median of the last 'window_size' values in O(log n) time per update using two heaps and supports deletion of the oldest element. Explain memory and performance trade-offs and how you'd adapt this for high-throughput streaming.

MediumTechnical

79 practiced

How would you design anomaly detection that is robust to active A/B experiments so experiment-driven metric changes do not trigger false alarms? Discuss metadata tagging, alert suppression rules, and any statistical approaches to isolate experiment effects.

MediumTechnical

55 practiced

You're on-call and receive an alert: a 40% drop in weekly active users for a key cohort. Walk through an end-to-end triage workflow you would follow within the first 30 minutes: quick checks, dashboards and logs to consult, how to rule out instrumentation errors, sampling raw events, and who to escalate to.

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