Data Lake: Store raw data in any format. Schema on read. Cheap. Use for: raw logs, ML training, exploratory analytics. Examples: S3 + Athena, Databricks.
Data Warehouse: Structured, schema on write. Optimized for queries. Use for: business intelligence, dashboards, reports. Examples: Snowflake, BigQuery, Redshift.
Many companies use both. Raw data lands in the lake. Transformed data moves to the warehouse. In interviews, specify which you need based on the use case.