Databricks Certified Data Engineer - Professional

Explanation

Stream-static join is the correct answer because:

• Stateless tables in streaming contexts don't maintain state information across processing windows
• Stream-static join involves joining a streaming dataset with a static (non-streaming) reference table
• This operation doesn't require maintaining state about the streaming data since the static table is fixed and doesn't change during processing
• The state dimension remains limited because only the static table data needs to be loaded, not historical streaming data

Why other options are incorrect:

• A. Stream-stream join: Requires maintaining state for both streaming sources to handle late-arriving data and ensure correct joins
• C. Stateful aggregation: By definition maintains state (counts, sums, etc.) across processing windows
• D. Drop duplicates: Requires maintaining state to track which records have already been seen to identify duplicates

Stream-static joins are commonly used for enrichment operations where streaming data is joined with reference/lookup tables, making them suitable for stateless processing scenarios where state maintenance needs to be minimized.