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

0 practiced

Design an adaptive thresholding algorithm that adjusts per-metric thresholds over time using estimates of seasonality, trend, and recent variance. Provide formulas or pseudocode for computing thresholds that adapt (e.g., quantile-based or mean+factor*std) and explain safeguards such as min/max bounds and cooldown periods to avoid oscillation.

MediumTechnical

0 practiced

You observe anomalies across multiple purchase-funnel metrics in several countries. Describe a framework to estimate business impact (e.g., revenue or DAU loss) to prioritize which anomalies to investigate first. List what additional data you'd request and how you'd present a prioritized list to stakeholders.

MediumTechnical

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Metrics pipelines often face late-arriving events and missing days. Describe strategies for ETL and detection pipelines to handle delayed data, backfills, and to avoid spurious alerts during a backfill. Include watermarking strategies, alert suppression, and reconciliation jobs.

HardTechnical

0 practiced

A daily PySpark job computes anomaly scores for 200M data points and currently takes 6 hours. Propose concrete optimizations (repartitioning, avoiding wide shuffles, vectorized UDF alternatives, caching, adaptive query execution, tuning memory/executor configs) and show example Spark config changes or code snippets that could reduce runtime toward under 1 hour.

MediumBehavioral

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Describe a situation where you reduced alert fatigue from noisy anomaly alerts. What changes did you propose and implement (e.g., dynamic thresholds, grouping, suppression rules), how did you measure improvement, and what trade-offs or pushback did you face from stakeholders?

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