Apply Clustering and Materialized Views ARA-C01 Practice Questions (Page 2)

Questions 6–10

1. 6application · medium

   A table's clustering depth has increased from 4 to 12 over the past week due to heavy DML activity. Queries are now scanning more micro-partitions than necessary. You want to restore pruning efficiency with minimal manual intervention. What should you do?

   • AIncrease the warehouse size for all queries
   • BManually run ALTER TABLE ... RECLUSTER ON every hour
   • CDrop and recreate the table with the same clustering key
   • DEnable automatic clustering on the table
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2. 7application · medium

   A table with automatic clustering enabled has a clustering depth that remains high despite frequent DML. You suspect the clustering key is not optimal. What should you do first?

   • AReview the query patterns and adjust the clustering key
   • BDisable automatic clustering and manually recluster
   • CDrop and recreate the table
   • DIncrease the frequency of automatic clustering
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4. 8foundation · easy

   Which characteristic of a column makes it a good candidate for a clustering key?

   • ALow cardinality and frequent use in SELECT list
   • BHigh cardinality but rarely used in queries
   • CHigh cardinality and frequent use in filter predicates
   • DLow cardinality and used in ORDER BY clauses
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5. 9foundation · easy

   What does the clustering depth metric indicate about a table?

   • AThe total number of micro-partitions in the table
   • BThe number of overlapping micro-partitions for a given clustering key
   • CThe amount of storage used by the table's clustering metadata
   • DThe time required to recluster the table automatically
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6. 10foundation · easy

   How does clustering metadata help improve query performance?

   • AIt creates indexes on the clustering key columns
   • BIt caches query results in the virtual warehouse
   • CIt compresses the data in each micro-partition to reduce storage
   • DIt allows Snowflake to skip micro-partitions that do not contain relevant data
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