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

• 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 correct way to securely share data with a vendor using a Snowflake account on a different cloud platform and region is to create a share, add objects to the share, and add a consumer account to the share for the vendor to access. This way, the company can control what data is shared, who can access it, and how long the share is valid. The vendor can then query the shared data without copying or moving it to their own account. The other options are either incorrect or inefficient, as they involve creating unnecessary reader accounts, users, roles, or database replication.

The correct answer is option B. This query is designed to assess the need for a multi-cluster warehouse by examining the queuing time (AVG_QUEUED_LOAD) on different days and virtual warehouses. When the AVG_QUEUED_LOAD is greater than zero, it suggests that queries are waiting for resources, which can be an indicator that performance might be improved by using a multi-cluster warehouse to handle the workload more efficiently. By grouping by date and warehouse name and filtering on the sum of the average queued load being greater than zero, the query identifies specific days and warehouses where the workload exceeded the available compute resources. This information is valuable when considering scaling out warehouses to multi-cluster configurations for improved performance.