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Snowflake SnowPro Advanced Administrator Sample Questions (Q10-Q15):

NEW QUESTION # 10
An Administrator loads data into a staging table every day. Once loaded, users from several different departments perform transformations on the data and load it into different production tables.
How should the staging table be created and used to MINIMIZE storage costs and MAXIMIZE performance?

A. Create it as an external table, which will not incur Time Travel costs.
B. Create it as a transient table with a retention time of 0 days.
C. Create it as a temporary table with a retention time of 0 days.
D. Create it as a permanent table with a retention time of 0 days.

Answer: B

Explanation:
Explanation
According to the Snowflake documentation1, a transient table is a type of table that does not support Time Travel or Fail-safe, which means that it does not incur any storage costs for maintaining historical versions of the data or backups for disaster recovery. A transient table can be dropped at any time, and the data is not recoverable. A transient table can also have a retention time of 0 days, which means that the data is deleted immediately after the table is dropped or truncated. Therefore, creating the staging table as a transient table with a retention time of 0 days can minimize the storage costs and maximize the performance, as the data is only loaded and transformed once, and then deleted after the production tables are populated. Option A is incorrect because creating the staging table as an external table, which references data files stored in a cloud storage location, can incur additional costs and complexity for data transfer and synchronization, and may not provide the best performance for data loading and transformation. Option C is incorrect because creating the staging table as a temporary table, which is automatically dropped when the session ends or the user logs out, can cause data loss or inconsistency if the session is interrupted or terminated before the production tables are populated. Option D is incorrect because creating the staging table as a permanent table, which supports Time Travel and Fail-safe, can incur additional storage costs for maintaining historical versions of the data and backups for disaster recovery, and may not provide the best performance for data loading and transformation.

NEW QUESTION # 11
For Snowflake network policies, what will occur when the account_level and user_level network policies are both defined?

A. A network policy error will be generated with no definitions provided.
B. The account_level policy will override the user_level policy.
C. The user_level network policies will not be supported.
D. The user_level policy will override the account_level policy.

Answer: D

Explanation:
Explanation
According to the Network Policies documentation, a network policy can be applied to an account, a security integration, or a user. If there are network policies applied to more than one of these, the most specific network policy overrides more general network policies. The following summarizes the order of precedence:
*Account: Network policies applied to an account are the most general network policies. They are overridden by network policies applied to a security integration or user.
*Security Integration: Network policies applied to a security integration override network policies applied to the account, but are overridden by a network policy applied to a user.
*User: Network policies applied to a user are the most specific network policies. They override both accounts and security integrations.
Therefore, if both the account_level and user_level network policies are defined, the user_level policy will take effect and the account_level policy will be ignored. The other options are incorrect because:
*The account_level policy will not override the user_level policy, as explained above.
*The user_level network policies will be supported, as they are part of the network policyfeature.
*A network policy error will not be generated, as there is no conflict between the account_level and user_level network policies.

NEW QUESTION # 12
What access control policy will be put into place when future grants are assigned to both database and schema objects?

A. Database privileges will take precedence over schema privileges.
B. An access policy combining both the database object and the schema object will be used, with the most permissive policy taking precedence.
C. An access policy combining both the database object and the schema object will be used, with the most restrictive policy taking precedence.
D. Schema privileges will take precedence over database privileges.

Answer: D

Explanation:
When future grants are defined on the same object type for a database and a schema in the same database, the schema-level grants take precedence over the database level grants, and the database level grants are ignored4. This behavior applies to privileges on future objects granted to one role or different roles4. Future grants allow defining an initial set of privileges to grant on new (i.e. future) objects of a certain type in a database or a schema3. As soon as the new objects are created inside the database or schema, the predefined set of privileges are assigned to the object automatically without any manual intervention3.

NEW QUESTION # 13
What are characteristics of Dynamic Data Masking? (Select TWO).

A. A single masking policy can be applied to columns in different tables.
B. The role that creates the masking policy will always see unmasked data in query results.
C. A masking policy can be applied to the VALUE column of an external table.
D. A single masking policy can be applied to columns with different data types.
E. A masking policy that is currently set on a table can be dropped.

Answer: A,D

Explanation:
According to the Using Dynamic Data Masking documentation, Dynamic Data Masking is a feature that allows you to alter sections of data in table and view columns at query time using a predefined masking strategy. The following are some of the characteristics of Dynamic Data Masking:
* A single masking policy can be applied to columns in different tables. This means that you can write a policy once and have it apply to thousands of columns across databases and schemas.
* A single masking policy can be applied to columns with different data types. This means that you can use the same masking strategy for columns that store different kinds of data, such as strings, numbers, dates, etc.
* A masking policy that is currently set on a table can be dropped. This means that you can remove the masking policy from the table and restore the original data visibility.
* A masking policy can be applied to the VALUE column of an external table. This means that you can mask data that is stored in an external stage and queried through an external table.
* The role that creates the masking policy will always see unmasked data in query results. This is not true, as the masking policy can also apply to the creator role depending on the execution context conditions defined in the policy. For example, if the policy specifies that only users with a certain custom entitlement can see the unmasked data, then the creator role will also need to have that entitlement to see the unmasked data.

NEW QUESTION # 14
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 is_role_in_session ('MASKED_VIEW_ROLE') then ' ** else val end;
*,
B. 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;
** '
C. create or replace masking policy maskString as (val string) returns string -> case when current_role() in ('MASKED_VIEW_ROLE') then ' ********* ' else val end;
D. create or replace masking policy maskstring as (val string) returns string -> case when invoker_role() in ('MASKED_VIEW_ROLE') then else val end;
' **

Answer: A

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 # 15
......

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Latest updated ADA-C01 Latest Test Questions & Latest ADA-C01 Valid Test Guide & Useful New ADA-C01 Braindumps Questions

ADA-C01 Latest Test Questions | 100% Free Trustable SnowPro Advanced Administrator Valid Test Guide

Snowflake SnowPro Advanced Administrator Sample Questions (Q10-Q15):

Pass Guaranteed Quiz 2025 ADA-C01: Perfect SnowPro Advanced Administrator Latest Test Questions

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