Detect Anomalies in Data | Dataford Interview Questions

How do you identify anomalies in large-scale datasets at ByteDance?

Using event_logs and platform_thresholds, identify platform dates whose daily event count falls below the minimum or exceeds the maximum configured threshold. Exclude events without a recognized platform and return only anomalous platform dates.

Output

One row per anomalous platform and date.
Columns: surface_name, event_date, event_count, min_daily_events, max_daily_events, and anomaly_type.
Sort by event_date ascending, then surface_name ascending.

Schema

event_logs

Column	Type	Description
event_idPK	VARCHAR(40)	Unique event identifier
platform_id	INT	Referenced platform identifier
occurred_at	TIMESTAMP	Timestamp when the event occurred

platform_thresholds

Column	Type	Description
platform_idPK	INT	Unique platform identifier
surface_name	VARCHAR(80)	ByteDance product surface name
min_daily_events	INT	Lowest expected event count for a day
max_daily_events	INT	Highest expected event count for a day

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