According to the GRANT OWNERSHIP documentation, the ownership privilege grants full control over the table and can only be held by one role at a time. However, the current owner can also grant the ownership privilege to another role, which transfers the ownership to the new role. In this case, the SECURITYADMIN role granted the ownership privilege on future tables in the PROD.WORKING schema to the PROD_WORKING_OWNER role. This means that any table created in that schema after the grant statement will be owned by the PROD_WORKING_OWNER role. Therefore, the PROD_WORKING_OWNER role can alter or drop table XYZ, which was created by the ACCOUNTADMIN role in the PROD.WORKING schema. Additionally, the ACCOUNTADMIN role can also alter or drop table XYZ, because it is the top-level role that has all privileges on all objects in the account. Furthermore, the SYSADMIN role can also alter or drop table XYZ, because it was granted the PROD_WORKING_OWNER role by the SECURITYADMIN role. The SYSADMIN role can activate the PROD_WORKING_OWNER role and inherit its privileges, including the ownership privilege on table XYZ. The SECURITYADMIN role cannot alter or drop table XYZ, because it does not have the ownership privilege on the table, nor does it have the PROD_WORKING_OWNER role.

According to the Snowflake documentation1, a standard listing is a type of listing that provides free access to the full data product, with no payment required. A standard listing can be added and queried immediately by the consumer, as long as they accept the terms and conditions of the listing. A monetized listing is a type of listing that charges for access to the data product, using the pricing models offered by Snowflake. A monetized listing requires the consumer to provide payment information and agree to the billing terms before accessing the data product. A regional listing is not a type of listing, but a way to specify the regions where the listing is available. A personalized listing is a type of listing that provides limited trial access to the data product, with unlimited access to the full data product available upon request. A personalized listing requires the consumer to request access from the provider and wait for the provider to grant access before accessing the data product. Therefore, the only type of listing that can be added and queried immediately is the standard listing.

• Option A is correct because the ALTER DATABASE … ENABLE REPLICATION TO ACCOUNTS command must be issued from the primary account that owns the database to be replicated1.

• Option B is incorrect because users must be granted REPLICATIONGRANTER privileges in order to enable replication1.

• Option C is incorrect because to start replication, the ALTER DATABASE … REFRESH command must be run on the primary database, not the secondary database1.

• Option D is incorrect because replication can occur across different cloud providers, as well as across regions2.

• Option E is correct because databases created from shares can be replicated, as long as the share is active and the database is not dropped or altered1.

• Option F is correct because users can have unlimited primary databases and they can be replicated to an unlimited number of accounts if all accounts are within the same organization1.

According to the Snowflake documentation1, pre-signed URLs are required to access external files in a share. A secure view can be used to generate pre-signed URLs for the audio files stored in an external stage and expose them to the consumer account. Option A is incorrect because file URLs alone are not sufficient to access external files in a share. Option C is incorrect because METADATA$FILENAME only returns the file name, not the full path or URL. Option D is incorrect because the stage name and file path are not enough to generate pre-signed URLs.

According to the Snowflake documentation1, Snowflake uses a multi-version concurrency control (MVCC) model, which means that each transaction operates on a consistent snapshot of the database at a point in time. This allows queries and DML statements to run concurrently without blocking each other, as they do not modify the same data. Therefore, a deadlock, which occurs when concurrent transactions are waiting on resources that are locked by each other, cannot happen in Snowflake. Option B is incorrect because queries and DML statements do not block each other in Snowflake, unless they are explicitly started transactions and multiple statements in each transaction2. Option C is incorrect because transaction locking in Snowflake is enforced at the partition level, not the row or table level3. Option D is incorrect because queries executed within a given transaction do not see that transaction’s uncommitted changes, but only the committed changes that occurred before the transaction started1.

To retrieve a list of schemas deleted within the last 2 days from the DB1 database, you need a metadata view that includes historical data , including dropped (deleted) objects .

Let’s review the options:

✅ B. SNOWFLAKE.ACCOUNT_USAGE.SCHEMATA

This is the correct choice because:

It includes metadata for all schemas , even deleted ones , within the retention period .

It contains a DELETED column and a DELETED_ON timestamp column.

You can filter rows with:

sql

CopyEdit

SELECT *

FROM SNOWFLAKE.ACCOUNT_USAGE.SCHEMATA

WHERE DELETED IS TRUE

AND DELETED_ON > = DATEADD(DAY, -2, CURRENT_TIMESTAMP())

AND CATALOG_NAME = 'DB1';

❌ A. SHOW SCHEMAS IN DATABASE DB1;

Only shows current (active) schemas — does not include deleted schemas.

❌ C. DB1.INFORMATION_SCHEMA.SCHEMATA

Like option A, this view only includes active schemas in the current database.

No info on deleted schemas is retained.

❌ D. SNOWFLAKE.ACCOUNT_USAGE.DATABASES

This metadata view tracks databases , not individual schemas .

???? SnowPro Administrator References:

SNOWFLAKE.ACCOUNT_USAGE.SCHEMATA documentation

Metadata includes both active and deleted schemas (within retention window).

According to the web search results from my predefined tool search_web, one of the factors that affects the reuse of persisted query results is the exact match of the query syntax1. If the query contains functions that return different values for successive runs, such as CURRENT_TIMESTAMP, then the query will not match the previous query and will not benefit from the cache. To avoid this, the query should use functions that return consistent values for the same day, such as CURRENT_DATE, which returns the current date without the time component2. Option A is incorrect because wrapping the query in a UDF does not guarantee the syntax match, as the UDF may also contain dynamic functions. Option B is incorrect because the virtual warehouse does not affect the persisted query results, which are stored at the account level1. Option C is incorrect because the security role does not affect the persisted query results, as long as the role has the necessary privileges to access the tables and views used in the query1.

1: Using Persisted Query Results | Snowflake Documentation 2: Date and Time Functions | Snowflake Documentation

According to the Snowflake documentation1, clustering keys are most effective when the data is evenly distributed across the key values. If the data is skewed, such as in this case where most sales come from only 20 stores out of 1000, then the micro-partitions will not be well-clustered and the pruning will be poor. This means that more bytes will be scanned by queries, even if they filter by STORE_ID. Option A is incorrect because the data type of the clustering key does not affect the pruning. Option B is incorrect because the table is large enough to benefit from clustering, if the data was more balanced. Option D is incorrect because the cardinality of the clustering key is not relevant for pruning, as long as the key values are distinct.

1: Considerations for Choosing Clustering for a Table | Snowflake Documentation

According to the Snowflake documentation1, the ORGADMIN role is a special system role that is responsible for managing operations at the organization level, such as creating and viewing accounts, enabling database replication, and setting global account parameters. The ACCOUNTADMIN role is a system role that is responsible for managing operations at the account level, such as creating and managing users, roles, warehouses, databases, and shares. Therefore, the commands that can be performed by the ORGADMIN role but not the ACCOUNTADMIN role are:

• SHOW ORGANIZATION ACCOUNTS: This command lists all the accounts in the organization and their properties, such as region, edition, and status2. The ACCOUNTADMIN role can only show the current account and its properties using the SHOW ACCOUNTS command3.

• SELECT SYSTEM$GLOBAL_ACCOUNT_SET_PARAMETER: This function sets a global account parameter for an account in the organization, such as enabling account database replication4. The ACCOUNTADMIN role can only set local account parameters using the ALTER ACCOUNT command.

Option A is incorrect because the SHOW REGIONS command can be executed by any role, not just the ORGADMIN role. Option B is incorrect because the SHOW USERS command can be executed by the ACCOUNTADMIN role, as well as any role that has been granted the MONITOR privilege on the account. Option D is incorrect because the GRANT ROLE ORGADMIN TO USER < username > command can be executed by the ACCOUNTADMIN role, as well as any role that has been granted the ORGADMIN role1.

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.