DP-750 Certification Practice Question #92

Question

A high-ingest Delta table (`sales.bronze_events`) receives thousands of small files per hour from streaming writes and frequent `MERGE` operations. Query latency has grown and storage cost is rising from obsolete pre-compaction files. You must design a weekly maintenance run that (a) improves read performance by compacting and clustering files, then (b) reclaims storage from files no longer referenced, while preserving the standard 7-day time-travel window. Order the maintenance steps into the correct end-to-end sequence. ```mermaid flowchart LR subgraph Tiles T1["DESCRIBE HISTORY / DESCRIBE DETAIL
(baseline file count and state)"] T2["OPTIMIZE sales.bronze_events
(bin-pack small files; cluster layout)"] T3["VACUUM sales.bronze_events
(default 7-day / 168-hour retention)"] T4["DESCRIBE DETAIL
(verify fewer, larger files)"] end subgraph Sequence Slot1["Step 1"] Slot2["Step 2"] Slot3["Step 3"] Slot4["Step 4"] end T1 -.-> Slot1 T2 -.-> Slot2 T3 -.-> Slot3 T4 -.-> Slot4 ``` Place each tile into the correct step slot (Step 1 → Step 4).

Accepted Answer

The correct lifecycle is baseline → compact → reclaim → verify. You first capture file counts with `DESCRIBE HISTORY`/`DESCRIBE DETAIL`, then run `OPTIMIZE` to bin-pack the many small files (and apply clustering) into fewer large files, which improves read performance. `OPTIMIZE` leaves the old files unreferenced, so `VACUUM` runs after it to delete those obsolete files and reclaim storage; using the default 168-hour (7-day) retention preserves the standard time-travel window. A final `DESCRIBE DETAIL` confirms the file count dropped. Reversing OPTIMIZE and VACUUM, or shortening retention below 7 days, would defeat the goal.

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