SnowPro-Core Exam Dumps - SnowPro Core Certification Exam

Snowflake’s Data Exchange feature provides a data hub for secure data collaboration. It allows providers to publish data that can be discovered and accessed by a selected group of invited members, facilitating secure and controlled data sharing within a collaborative environment3. References: [COF-C02] SnowPro Core Certification Exam Study Guide

Unstructured data stored in Snowflake can be retrieved by using SQL functions to generate URLs that point to the data. These URLs can then be used to download the data directly to a client

The MIN_DATA_RETENTION_TIME_IN_DAYS parameter is set at the account level. This parameter determines the minimum number of days Snowflake retains historical data for Time Travel operations

Snowflake stores metadata for rows in micro-partitions, including the range of values for each column and the number of distinct values1.

 The Access History feature in Snowflake allows administrators to track data access patterns and identify unused data. This information can be used to make decisions about archiving or deleting data to optimize storage and reduce costs. 

Snowflake external tables are read-only, which means data manipulation language (DML) operations like INSERT, RENAME, or ALTER cannot be performed on them. However, external tables can be used for query and join operations3.

References: [COF-C02] SnowPro Core Certification Exam Study Guide

 For optimizing the performance of queries that run on a very large table, it is recommended to choose one high cardinality column as the clustering key. This helps to co-locate similar rows in the same micro-partitions, improving scan efficiency in queries by skipping data that does not match filtering predicates4.

References: [COF-C02] SnowPro Core Certification Exam Study Guide

Micro-partitioning is a Snowflake function that is maintained separately from the data and supports features such as Time Travel, Secure Data Sharing, and pruning. It allows Snowflake to efficiently manage and query large datasets by organizing them into micro-partitions1.