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  2. Snowflake Certification
  3. SnowPro-Core Exam
  4. Snowflake.SnowPro-Core.v2024-10-07.q429 Dumps
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Question 381

Which object allows you to limit the number of credits consumed within a Snowflake account?
Select one.

Correct Answer: B
Explanation
The time travel data retention can be overwritten at the table level "When creating a table, schema, or database, the account default can be overridden using the DATA_RETENTION_TIME_IN_DAYS parameter in the command."
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Question 382

Credit Consumption by the Compute Layer (Virtual Warehouses) is based on: (Choose two.)

Correct Answer: B,D
https://docs.snowflake.com/en/user-guide/credits.html#virtual-warehouse-credit-usage "Snowflake credits are charged based on the number of virtual warehouses you use, how long they run, and their size."
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Question 383

How can performance be optimized for a query that returns a small amount of data from a very large base table?

Correct Answer: C
The search optimization service in Snowflake is designed to improve the performance of selective point lookup queries on large tables, which is ideal for scenarios where a query returns a small amount of data from a very large base table1. References: [COF-C02] SnowPro Core Certification Exam Study Guide
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Question 384

Which of the following is a valid source for an external stage when the Snowflake account is located on Microsoft Azure?

Correct Answer: D
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Question 385

The effects of query pruning can be observed by evaluating which statistics? (Select TWO).

Correct Answer: A,C
Query pruning in Snowflake refers to the optimization technique where the system reduces the amount of data scanned by a query based on the query conditions. This typically involves skipping unnecessary data partitions that do not contribute to the query result. The effectiveness of this technique can be observed through:
* Option A: Partitions scanned. This statistic indicates how many data partitions were actually scanned as a result of query pruning, showing the optimization in action.
* Option C: Bytes scanned. This measures the volume of data physically read during query execution, and a reduction in this number indicates effective query pruning, as fewer bytes are read when unnecessary partitions are skipped.
Options B, D, and E do not directly relate to observing the effects of query pruning. "Partitions total" shows the total available, not the impact of pruning, while "Bytes read from result" and "Bytes written" relate to output rather than the efficiency of data scanning.References: Snowflake documentation on performance tuning and query optimization techniques, specifically how query pruning affects data access.
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