Apply Liquid Clustering to Improve Query Speed When Filtering Large Tables on Specific Columns. DATA-ANALYST-ASSOCIATE Practice Questions (Page 3)

Questions 11–15

1. 11expert · hard

   A team has a Delta table with a column `status` that has only 3 distinct values ('active', 'inactive', 'pending'). Queries frequently filter on `status`. They also have a column `created_at` that is frequently used in date-range filters. They are considering clustering on `status` and `created_at`. What is the best recommendation?

   • ADo not cluster on `status` because it has too few distinct values to be useful.
   • BCluster on `created_at` only, because high-cardinality columns are more effective.
   • CCluster on both `status` and `created_at`.
   • DCluster on `status` only, because low-cardinality columns are ideal for clustering.
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2. 12foundation · easy

   A Delta table is frequently filtered on `customer_id` and `order_date`. Which clustering column selection is most appropriate?

   • ACluster on both `customer_id` and `order_date`.
   • BCluster on a synthetic column that combines both values.
   • CCluster on a column that is never used in queries, such as `internal_note`.
   • DCluster only on `order_date` because it has lower cardinality.
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4. 13application · medium

   A team has a Delta table with columns `event_type` (10 distinct values), `event_timestamp` (high cardinality), and `user_id` (high cardinality). Queries frequently filter on `event_type` and `event_timestamp` together. Which clustering columns should they choose?

   • A`event_type` and `event_timestamp`
   • B`event_timestamp` only
   • C`user_id` and `event_timestamp`
   • D`event_type` only
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5. 14expert · hard

   A team has a Delta table with columns `user_id` (high cardinality), `session_id` (high cardinality), and `event_time` (high cardinality). Queries frequently filter on `user_id` and `event_time` together. They are considering clustering on `user_id` and `event_time`. What is the primary benefit of this choice?

   • AIt eliminates the need for `OPTIMIZE` maintenance.
   • BIt allows the query engine to prune files for queries that filter on both `user_id` and `event_time`.
   • CIt automatically creates an index on `user_id` and `event_time`.
   • DIt reduces the storage size of the table by compressing the data.
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6. 15application · medium

   A company has a large Delta table clustered on `region` and `product_id`. A query filters on `region = 'West'` and `product_id = 12345`. How does Liquid Clustering help this query run faster?

   • AIt compresses the data files so that fewer bytes are read from disk.
   • BIt reduces the amount of data scanned by allowing the query engine to skip files that do not contain matching values for the clustering columns.
   • CIt automatically creates an index on the clustering columns.
   • DIt caches the query results so subsequent runs of the same query are faster.
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