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.

MediumTechnical

0 practiced

Explain STL (Seasonal and Trend decomposition using Loess) versus classical additive decomposition for time series. Describe when you'd choose STL over simpler decomposition for production anomaly detection pipelines and the operational costs of using STL.

EasyBehavioral

0 practiced

Tell me about a time when you investigated a production metric spike or drop. Use the STAR method: describe the Situation, Task, Actions you took to triage (data validations, log checks, cohorting), the Result, and what you learned or changed in the monitoring pipeline afterward.

MediumTechnical

0 practiced

Describe methods to detect feature drift and label drift for anomaly detection models in production. Include specific metrics you would compute (e.g., PSI, KL divergence), sampling frequency, and automated actions to take when drift exceeds thresholds.

EasyTechnical

0 practiced

Discuss the trade-offs when choosing aggregation frequency and smoothing parameters (e.g., hourly vs daily aggregation, window size for moving averages) for anomaly detection on user engagement metrics. Address sensitivity, detection latency, signal-to-noise ratio, and compute/resource cost.

HardTechnical

0 practiced

Derive the statistical power of a z-score based detector for detecting a shift in mean of size delta (expressed in units of baseline sigma) given sample size n and detection threshold t. Show the formula for power (1 - beta) assuming normality, explain how power scales with n and delta, and discuss implications for very small n.

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