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Exam SnowPro Core All Questions

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Exam SnowPro Core topic 1 question 1186 discussion

Actual exam question from Snowflake's SnowPro Core
Question #: 1186
Topic #: 1
[All SnowPro Core Questions]

How does Snowflake optimize queries on tables by leveraging the sorting and storage of data within micro-partitions?

  • A. The data is pruned by column to eliminate unnecessary micro-partitions, then the rows within the remaining micro-partitions are filtered.
  • B. The data is pruned by micro-partitions based on column values, then rows within the remaining micro-partitions are filtered.
  • C. Any micro-partitions that are not needed for the query are pruned, then the data within the remaining micro-partitions is pruned by column.
  • D. The data within micro-partitions is sorted by rows, then the columns are filtered based on the query conditions.
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Suggested Answer: B 🗳️

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Billhardy
2 weeks ago
Selected Answer: B
Snowflake organizes table data into immutable micro‑partitions—small (50–500 MB uncompressed, up to 16 MB compressed) columnar files stored in cloud blob storage—where each partition’s columns are sorted and compressed independently. At query compile time, Snowflake uses per‑partition metadata (min/max values, distinct counts, byte offsets) to prune irrelevant partitions and skip unreferenced columns, drastically reducing I/O and network reads.
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37ceea2
3 months ago
Selected Answer: B
https://docs.snowflake.com/en/user-guide/tables-clustering-micropartitions The micro-partition metadata maintained by Snowflake enables precise pruning of columns in micro-partitions at query run-time, including columns containing semi-structured data. These being said, it seems that pruning is done by column in micro-partitions, which is against all the questions we solved in this examtopics. I shall say that B, but I am not convinced anymore.
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