ARA-R01 Exam Dumps : SnowPro Advanced: Architect Recertification Exam

The requirement is for the data to be accessible as quickly as possible after it arrives in the external stage with minimal coding effort.

Option A: Snowpipe with auto-ingest is a service that continuously loads data as it arrives in the stage. With auto-ingest, Snowpipe automatically detects new files as they arrive in a cloud stage and loads the data into the specified Snowflake table with minimal delay and no intervention required. This is an ideal low-maintenance solution for the given scenario where files are arriving at a very high frequency.

Option E: Using a combination of a task and a stream allows for real-time change data capture in Snowflake. A stream records changes (inserts, updates, and deletes) made to a table, and a task can be scheduled to trigger on a very short interval, ensuring that changes are processed into the dashboard tables as they occur.

• The architecture in the image shows a Snowflake data platform with two databases, DB1 and DB2, and two schemas, SH1 and SH2. DB1 contains a table TBL1 and a stage STAGE1. DB2 contains a table TBL2. The image also shows a snippet of code written in SQL language that copies data from STAGE1 to TBL2 using a file format FF PIPE 1.
• To copy data from DB1 to TBL2, there are two possible options among the choices given:

SQLAI-generated code. Review and use carefully. More info on FAQ.

use database DB2;

use schema SH2;

create stage EXT_STAGE1

url = @DB1.SH1.STAGE1;

• uk.co.certification.simulator.questionpool.PList@18276980

SQLAI-generated code. Review and use carefully. More info on FAQ.

copy into TBL2

from @EXT_STAGE1

file format = (format name = DB1.SH1.FF PIPE 1);

• uk.co.certification.simulator.questionpool.PList@18276cf0

SQLAI-generated code. Review and use carefully. More info on FAQ.

use database DB2;

use schema SH2;

insert into TBL2

select * from DB1.SH1.TBL1;

• The other options are not valid because:

References:

• 1: CREATE STAGE | Snowflake Documentation
• 2: COPY INTO table | Snowflake Documentation
• 3: Cross-database Queries | Snowflake Documentation

The Snowflake system functions used to view and monitor the clustering metadata for a table are:

• SYSTEM$CLUSTERING_INFORMATION
• SYSTEM$CLUSTERING_DEPTH

Comprehensive But Short Explanation:

• The SYSTEM$CLUSTERING_INFORMATION function in Snowflake returns a variety of clustering information for a specified table. This information includes the average clustering depth, total number of micro-partitions, total constant partition count, average overlaps, average depth, and a partition depth histogram. This function allows you to specify either one or multiple columns for which the clustering information is returned, and it returns this data in JSON format.
• The SYSTEM$CLUSTERING_DEPTH function computes the average depth of a table based on specified columns or the clustering key defined for the table. A lower average depth indicates that the table is better clustered with respect to the specified columns. This function also allows specifying columns to calculate the depth, and the values need to be enclosed in single quotes.

References:

• SYSTEM$CLUSTERING_INFORMATION: Snowflake Documentation
• SYSTEM$CLUSTERING_DEPTH: Snowflake Documentation