Snowpro Advanced Architect · Free Practice Question Medium

Question 12

Which configuration changes can improve the efficiency of micro-partition pruning during query execution?
  • A Increasing the table’s row count without further action.
  • B Adjusting the clustering key to better reflect query predicates.
  • C Manually altering the table’s internal storage format.
  • D Modifying query predicates to align with micro-partition boundaries.
Reveal correct answers

Correct answers: B, D

Explanation

✅ Adjusting the clustering key to better reflect query predicates.
✅ Modifying query predicates to align with micro-partition boundaries.

Explanation:

Efficient micro-partition pruning in Snowflake helps optimize query performance by reducing the number of scanned partitions. The following configuration changes can improve pruning efficiency:

  • Adjusting the clustering key → Ensures that queries filter on well-clustered columns, allowing Snowflake to eliminate unnecessary partitions.

  • Modifying query predicates → Aligning predicates with micro-partition boundaries improves partition elimination, reducing scan time.


❌ Why the other options are incorrect?

Increasing the table’s row count without further action.
→ Incorrect, because adding more rows does not inherently improve pruning; clustering and query optimization are required.

Manually altering the table’s internal storage format.
→ Incorrect, because Snowflake automatically manages micro-partitions, and users cannot manually modify storage format.

For more details, check the official Snowflake documentation on micro-partition pruning.

For additional insights, check out this guide on recognizing unsatisfactory pruning.

Discussion

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