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Snowflake SnowPro Advanced Administrator Sample Questions (Q38-Q43):

NEW QUESTION # 38
Which Snowflake objects can be managed using SCIM integration? (Select TWO).

A. Roles
B. Stages
C. Warehouses
D. Shares
E. Users

Answer: A,E

Explanation:
Explanation
A SCIM security integration allows the automated management of user identities and groups (i.e. roles) by creating an interface between Snowflake and a third-party Identity Provider (IdP)1. Snowflake supports SCIM integration with Okta, Azure, and custom SCIM clients2. SCIM integration does not support managing other Snowflake objects, such as stages, warehouses, or shares3. Therefore, the answer is B. Users and D. Roles.

NEW QUESTION # 39
A Snowflake customer is experiencing higher costs than anticipated while migrating their data warehouse workloads from on-premises to Snowflake. The migration workloads have been deployed on a single warehouse and are characterized by a large number of small INSERTs rather than bulk loading of large extracts. That single warehouse has been configured as a single cluster, 2XL because there are many parallel INSERTs that are scheduled during nightly loads.
How can the Administrator reduce the costs, while minimizing the overall load times, for migrating data warehouse history?

A. The 2XL warehouse should be changed to 4XL to increase the number of threads available for parallel load queries.
B. There should be another 2XL warehouse deployed to handle a portion of the load queries.
C. The warehouse should be kept as a SMALL or XSMALL and configured as a multi-cluster warehouse to handle the parallel load queries.
D. The INSERTS should be converted to several tables to avoid contention on large tables that slows down query processing.

Answer: C

Explanation:
According to the Snowflake Warehouse Cost Optimization blog post, one of the strategies to reduce the cost of running a warehouse is to use a multi-cluster warehouse with auto-scaling enabled. This allows the warehouse to automatically adjust the number of clusters based on the concurrency demand and the queue size. A multi-cluster warehouse can also be configured with a minimum and maximum number of clusters, as well as a scaling policy to control the scaling behavior. This way, the warehouse can handle the parallel load queries efficiently without wasting resources or credits. The blog post also suggests using a smaller warehouse size, such as SMALL or XSMALL, for loading data, as it can perform better than a larger warehouse size for small INSERTs. Therefore, the best option to reduce the costs while minimizing the overall load times for migrating data warehouse history is to keep the warehouse as a SMALL or XSMALL and configure it as a multi-cluster warehouse to handle the parallel load queries. The other options are incorrect because:
* A. Deploying another 2XL warehouse to handle a portion of the load queries will not reduce the costs, but increase them. It will also introduce complexity and potential inconsistency in managing the data loading process across multiple warehouses.
* B. Changing the 2XL warehouse to 4XL will not reduce the costs, but increase them. It will also provide more compute resources than needed for small INSERTs, which are not CPU-intensive but I/O-intensive.
* D. Converting the INSERTs to several tables will not reduce the costs, but increase them. It will also create unnecessary data duplication and fragmentation, which will affect the query performance and data quality.

NEW QUESTION # 40
Which masking policy will mask a column whenever it is queried through a view owned by a role named MASKED_VIEW_ROLE?

A. create or replace masking policy maskString as (val string) returns string -> case when current_role() in ('MASKED_VIEW_ROLE') then ' ********* ' else val end;
B. create or replace masking policy maskstring as (val string) returns string -> case when is_role_in_session ('MASKED_VIEW_ROLE') then ' ** else val end;
*,
C. create or replace masking policy maskstring as (val string) returns string -> case when invoker_role() in ('MASKED_VIEW_ROLE') then else val end;
' **
D. create or replace masking policy maskString as (val string) returns string -> case when array_contains ('MASKED_VIEW_ROLE' :: variant, parse_json (current_available_roles ())) then
'*
else val
end;
** '

Answer: B

Explanation:
Explanation
A masking policy is a SQL expression that transforms the data in a column based on the role that queries the column1. The is_role_in_session function returns true if the specified role is in the current session2. Therefore, the masking policy in option A will mask the column data with asterisks whenever it is queried through a view owned by the MASKED_VIEW_ROLE3. The other options use different functions that do not check the ownership of the view, but rather the current role, the invoker role, or the available roles in the session45.
These functions may not return the desired result if the role that owns the view is different from the role that queries the view.

NEW QUESTION # 41
Review the output of the SHOW statement below which displays the current grants on the table DB1. S1. T1:

This statement is executed:
USE ROLE ACCOUNTADMIN;
DROP ROLE A;
What will occur?

A. The SELECT privileges for roles B and C will remain.
B. The table object DB1. S1. T1 will be dropped.
C. The OWNERSHIP privilege on table DB1. S1. T1 will be transferred to the ACCOUNTADMIN role.
D. The SELECT privilege on table DB1. S1. T1 to role B will be shown as GRANTED_BY the role ACCOUNTADMIN.

Answer: A

Explanation:
Explanation
Dropping role A does not affect the SELECT privileges granted to roles B and C on the table DB1.S1.T1.
According to the Snowflake documentation, dropping a role revokes all privileges granted to the role, but does not revoke any privileges granted by the role. Therefore, the OWNERSHIP privilege on the table DB1.S1.T1 will be revoked from role A, but the SELECT privileges granted by role A to role B and by role B to role C will remain. The GRANTED_BY column will still show the original grantor of the privilege, not the ACCOUNTADMIN role.

NEW QUESTION # 42
A requirement has been identified to allow members of a corporate Data Product team to bring in data sets from the Snowflake Marketplace. The members of this team use the role DP_TEAM.
What grant statements must the ACCOUNTADMIN execute in order for the DP TEAM role to import and work with data from the Marketplace?

A. grant usage on snowflake_marketplace to role dp_team;
grant create database on account to role dp_team;
B. grant imported privileges on account to role dp_team;
grant create database on account to role dp_team;
C. grant marketplace in account to role dp_team;
grant create database from share to role dp_team;
D. grant import share on account to role dp_team;
grant create database on account to role dp_team;

Answer: D

Explanation:
Option D is the correct answer because it follows the steps described in the Snowflake documentation for importing data from the Snowflake Marketplace. The ACCOUNTADMIN role needs to grant the IMPORT SHARE privilege on the account to the DP_TEAM role, which allows the role to import data from any provider in the marketplace. The ACCOUNTADMIN role also needs to grant the CREATE DATABASE privilege on the account to the DP_TEAM role, which allows the role to create a database from a share. Option A is incorrect because there is no MARKETPLACE privilege in Snowflake. Option B is incorrect because the USAGE privilege on SNOWFLAKE_MARKETPLACE is not sufficient to import data from the marketplace. Option C is incorrect because there is no IMPORTED PRIVILEGES privilege in Snowflake.

NEW QUESTION # 43
......

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