ARA-C01 Exam Dumps - Snowflake SnowPro Advanced: Architect Questions and Answers

In Snowflake, the change tracking metadata for a table is utilized by the MERGE command and the STREAM object. The MERGE command uses change tracking to determine how to apply updates and inserts efficiently based on differences between source and target tables. STREAM objects, on the other hand, specifically capture and store change data, enabling incremental processing based on changes made to a table since the last stream offset was committed.References: Snowflake Documentation on MERGE and STREAM Objects.

According to the Snowflake documentation, these two system functions are provided by Snowflake to monitor clustering information within a table. A system function is a type of function that allows executing actions or returning information about the system. A clustering key is a feature that allows organizing data across micro-partitions based on one or more columns in the table. Clustering can improve query performance by reducing the number of files to scan.

• SYSTEM$CLUSTERING_INFORMATION is a system function that returns clustering information, including average clustering depth, for a table based on one or more columns in the table. The function takes a table name and an optional column name or expression as arguments, and returns a JSON string with the clustering information. The clustering information includes the cluster by keys, the total partition count, the total constant partition count, the average overlaps, and the average depth1.
• SYSTEM$CLUSTERING_DEPTH is a system function that returns the clustering depth for a table based on one or more columns in the table. The function takes a table name and an optional column name or expression as arguments, and returns an integer value with the clustering depth. The clustering depth is the maximum number of overlapping micro-partitions for any micro-partition in the table. A lower clustering depth indicates a better clustering2.

References:

• SYSTEM$CLUSTERING_INFORMATION | Snowflake Documentation
• SYSTEM$CLUSTERING_DEPTH | Snowflake Documentation

 The SIMULATED_DATA_SHARING_CONSUMER session parameter allows a data provider to simulate the data access of a consumer account without creating a reader account or logging in with the consumer credentials. This parameter can be used to validate the data accessibility by the consumers in a data share, especially when using secure views or secure UDFs that filter data based on the current account or role. By setting this parameter to the name of a consumer account, the data provider can see the same data as the consumer would see when querying the shared database. This is a convenient and efficient way to test the data sharing functionality and ensure that only the intended data is visible to the consumers.

References:

• Using the SIMULATED_DATA_SHARING_CONSUMER Session Parameter
• SnowPro Advanced: Architect Exam Study Guide

The error encountered during the cloning attempt arises because the schema STAGING as it existed on June 1st, 2021, is not within the Time Travel retention period. According to the schema history, STAGING was recreated on June 2nd, 2021, after being dropped on the same day. The requested timestamp of '2021-06-01 08:00:00' is prior to this recreation, hence not available. The STAGING schema from before June 2nd was dropped and exceeded the Time Travel period for retrieval by the time of the cloning attempt. Therefore, cloning STAGING as it looked on June 1st, 2021, cannot be achieved because the data from that time is no longer available within the allowed Time Travel window.References: Snowflake documentation on Time Travel and data cloning, which is covered under the SnowPro Advanced: Architect certification resources.