The correct answer is Data lakes hold raw unprocessed data while data warehouses hold structured curated datasets prepared for reporting.

Data lakes store data in its original form so teams can ingest a wide variety of formats and apply a schema when they read or process the data. Data warehouses store cleaned, modeled and optimized tables with a predefined schema so reporting and business intelligence queries run efficiently.

Data lakes are commonly used for flexible exploration and machine learning because they retain raw and semi structured or unstructured inputs. Data warehouses are commonly used for governed analytics and fast SQL reporting because they enforce schema and performance optimizations.

Google Cloud Storage and BigQuery is incorrect because it lists specific Google services rather than describing the fundamental conceptual difference between a lake and a warehouse. Both services can be part of an analytics architecture but they do not define the distinction.

Data lakes are optimized for real time analytics and data warehouses handle only batch workloads is incorrect because both architectures can support streaming and batch patterns depending on design and tooling. Modern warehouses can ingest streaming data and lakes can be tuned for low latency access for certain workloads.

Data lakes store only unstructured files while data warehouses accept only tabular structured data is incorrect because lakes can contain structured and semi structured data as well as unstructured files. Warehouses also support semi structured and nested types so the split is not strictly about file type or structure.

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