A transactions table has been liquid clustered on the columns product_id, user_id, and event_date.

Delta Lake'sLiquid Clusteringis an advanced feature that improves query performance by dynamically clustering data without requiring costly compaction steps like traditional Z-ordering.

When performing writes to aLiquid Clusteredtable, some write operations automatically maintain clustering, while othersdo not.

Explanation of Each Option:

(A) spark.writestream.format('delta').mode('append') (Correct Answer)

Reason:Streaming writes (writestream) donotsupportLiquid Clusteringbecause streaming data arrives in micro-batches.

Since Liquid Clustering needs efficient global reorganization of files, streaming append operations don't provide sufficient data volume at a time to be effectively clustered.

Delta Lake documentation states that Liquid Clustering is only supported for batch writes.

(B) CTAS and RTAS statements

Reason:CREATE TABLE AS SELECT (CTAS) and REPLACE TABLE AS SELECT (RTAS)are batch operationsand can enforce Liquid Clustering.

These operations create or replace a table based on a query result, and since they are batch-based, Liquid Clustering applies.

(C) INSERT INTO operations

Reason:INSERT INTOis supportedfor Liquid Clustering because it is a batch operation.

While it may not be as efficient as MERGE or COPY INTO, clustering is applied upon execution.

(D) spark.write.format('delta').mode('append')

Reason:Batch append operationsare supportedfor Liquid Clustering.

Unlike streaming append, batch writes allow the optimizer to re-cluster data efficiently.

Conclusion:

Sincestreaming append operations do not support Liquid Clustering, option(A)is the correct answer.

References:

Liquid Clustering in Delta Lake - Databricks Documentation