Apply Liquid Clustering to Improve Query Speed When Filtering Large Tables on Specific Columns. DATA-ANALYST-ASSOCIATE Practice Questions (Page 5)

Questions 21–24

1. 21expert · hard

   A team has a Delta table that is updated with large batch writes every night. They enabled Liquid Clustering on `store_id` and `sale_date`. They are concerned about the maintenance overhead during the batch write. What is the best way to manage this?

   • ADisable Liquid Clustering during the batch write and re-enable it afterward.
   • BUse partitioning instead of clustering to avoid maintenance overhead.
   • CRun `OPTIMIZE` after the batch write to reorganize the data and maintain the clustering layout.
   • DAccept the overhead and do nothing, because clustering maintenance is automatic and cannot be tuned.
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2. 22application · medium

   A team has enabled Liquid Clustering on a Delta table. They notice that after many small writes, the table has many small files and query performance has degraded. What should they do?

   • ARun `VACUUM` to remove old files.
   • BDisable Liquid Clustering and use partitioning instead.
   • CRecreate the table from scratch.
   • DRun `OPTIMIZE` to consolidate small files and reapply the clustering layout.
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4. 23application · medium

   A team has an existing Delta table that they want to cluster on `category` and `price`. They run `ALTER TABLE products CLUSTER BY (category, price);`. What is the immediate effect of this command?

   • AThe data is immediately reorganized according to the new clustering columns.
   • BThe command fails because clustering cannot be added to an existing table.
   • CThe table metadata is updated to define the clustering columns, but the data is not physically reorganized until `OPTIMIZE` is run.
   • DThe table is immediately partitioned by `category` and `price`.
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5. 24application · medium

   A team is considering enabling Liquid Clustering on a frequently updated Delta table. They are concerned about the impact on write performance. What should they expect?

   • AWrite performance may be slightly impacted due to automatic maintenance, but the overhead is managed by Databricks and can be tuned.
   • BWrite performance will degrade significantly because every write triggers a full table rewrite.
   • CWrite performance will improve because clustered tables are smaller.
   • DWrite performance will be identical to a non-clustered table.
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