Pass the Snowflake SnowPro Advanced: Administrator ADA-C01 Questions and answers with ValidTests

According to the Using Persisted Query Results documentation, the RESULT_SCAN function allows you to query the result set of a previous command as if it were a table. The LAST_QUERY_ID function returns the query ID of the most recent statement executed in the current session. Therefore, the combination of these two functions can be used to access the output of the SHOW WAREHOUSES command, which returns the configurations of all the virtual warehouses in the account. However, to persist the warehouse data in JSON format in the table VWH_META, the OBJECT_CONSTRUCT function is needed to convert the output of the SHOW WAREHOUSES command into a VARIANT column. The OBJECT_CONSTRUCT function takes a list of key-value pairs and returns a single JSON object. Therefore, the correct commands to execute are:

1.SHOW WAREHOUSES;

2.INSERT INTO VWH_META SELECT CURRENT_TIMESTAMP (), OBJECT_CONSTRUCT (*) FROM TABLE (RESULT_SCAN (LAST_QUERY_ID ()));

The other options are incorrect because:

•A. This option does not use the OBJECT_CONSTRUCT function, so it will not persist the warehouse data in JSON format. Also, it is missing the * symbol in the SELECT clause, so it will not select any columns from the result set of the SHOW WAREHOUSES command.

•B. This option does not use the OBJECT_CONSTRUCT function, so it will not persist the warehouse data in JSON format. It will also try to insert multiple columns into a single VARIANT column, which will cause a type mismatch error.

•D. This option does not use the OBJECT_CONSTRUCT function, so it will not persist the warehouse data in JSON format. It will also try to use the RESULT_SCAN function on a subquery, which is not supported. The RESULT_SCAN function can only be used on a query ID or a table name.

According to the Snowflake documentation1, database replication across accounts within the same organization requires the following steps:

•Link the accounts in the organization using the ORGADMIN role.

•Enable account database replication for both the source and target accounts using the SYSTEM$GLOBAL_ACCOUNT_SET_PARAMETER function.

•Promote a local database to serve as the primary database and enable replication to the target accounts using the ALTER DATABASE … ENABLE REPLICATION TO ACCOUNTS command.

•Create a secondary database in the target account using the CREATE DATABASE … FROM SHARE command.

•Refresh the secondary database periodically using the ALTER DATABASE … REFRESH command.

Option A is incorrect because it does not include the step of creating a secondary database in the target account. Option C is incorrect because replicating databases across accounts within the same organization is not enabled by default, but requires enabling account database replication for both the source and target accounts. Option D is incorrect because it is possible to replicate a database from an Enterprise edition Snowflake account to a Standard edition Snowflake account, as long as the IGNORE EDITION CHECK option is used in the ALTER DATABASE … ENABLE REPLICATION TO ACCOUNTS command2. Option B is correct because it includes all the necessary configuration steps in ACCOUNT1, except for creating a secondary database in ACCOUNT2, which can be done after the replication is enabled.

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

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.

❗Scenario:

Resource Monitor MONTHLY_FINANCE_LIMIT has acredit quota of 1000.

800 credits have been used and warehouses arealready suspended.

According to monitor configuration:

At80%, warehouses aresuspended.

At100%, warehouses would besuspended immediately.

Warehouses cannot resume untilthe monitor is reset or the quota is increased.

✅E. SET CREDIT_QUOTA = 1500

Increases themonthly credit limitto 1500.

Since current usage is 800 < 1500, this puts usage below 80%.

This allowsresumption of warehouses.

✅F. RESET

sql

CopyEdit

ALTER RESOURCE MONITOR MONTHLY_FINANCE_LIMIT RESET;

Resets usage to zerofor the current period.

Allows warehouses to resume immediately — same effect as a fresh cycle.

❌Why Other Options Are Incorrect:

A. / B. ALTER WAREHOUSE ... RESUME

Won’t work while the resource monitor is actively suspending the warehouses due to limits.

C. / D. UNSET RESOURCE_MONITOR

Youcan’t remove a resource monitorfrom a warehouse while it iscurrently suspendedby that same monitor.

You must firstreset or adjust the monitor.

G. UNSET RESOURCE_MONITORS

Invalid syntax — there’s no RESOURCE_MONITORS plural keyword.

????SnowPro Administrator References:

Resource Monitors Overview

ALTER RESOURCE MONITOR

Best Practices for Controlling Warehouse Credit Usage

According to the [GRANT MODIFY] documentation, the MODIFY privilege on a data exchange allows a role to perform administrative tasks on the data exchange, such as inviting members, approving profiles, and reviewing listings. This privilege can be granted by the ACCOUNTADMIN role or a role that already has the MODIFY privilege on the data exchange. Therefore, to delegate the administration of a company’s data exchange to users who do not have access to the ACCOUNTADMIN role, the best option is to grant the MODIFY privilege on the data exchange to a role that the users can assume. The other options are incorrect because:

•A. There is no such privilege as IMPORTED PRIVILEGES in Snowflake. The correct privilege name is IMPORT SHARE, which allows a role to create a database from a share. This privilege is not related to the administration of a data exchange, but to the consumption of shared data.

•C. There is no such privilege as OWNERSHIP in Snowflake. The correct privilege name is OWNED BY, which indicates the role that owns an object and has full control over it. However, this privilege cannot be granted or revoked, but only transferred by the current owner to another role using the GRANT OWNERSHIP command. Therefore, this option is not feasible for delegating the administration of a data exchange.

•D. The USAGE privilege on a data exchange allows a role to access the data exchange and view the available data listings. This privilege does not allow a role to perform administrative tasks on the data exchange, such as inviting members, approving profiles, and reviewing listings. Therefore, this option is not sufficient for delegating the administration of a data exchange.

According to the Snowflake documentation1, Snowflake supports SCIM 2.0 to integrate Snowflake with Okta and Microsoft Azure AD, which both function as identity providers. Snowflake also supports identity providers that are neither Okta nor Microsoft Azure (i.e. Custom). Therefore, the SCIM integration types that are supported in Snowflake are Okta, Custom, and Azure AD. Option A is incorrect because Amazon Web Services (AWS) is not a SCIM identity provider. Option B is incorrect because Google Cloud Platform (GCP) is not a SCIM identity provider. Option F is incorrect because Duo Security Provisioning Connector is not a SCIM identity provider.

According to the Snowflake documentation1, when an object in a schema is cloned, any future grants defined for this object type in the schema are applied to the cloned object unless the COPY GRANTS option is specified in the CREATE statement for the clone operation. However, some objects may still reference the source object or external objects after cloning, which may cause issues with access or functionality. These objects include:

•Sequences: If a table column references a sequence that generates default values, the cloned table may reference the source or cloned sequence, depending on where the sequence is defined. To avoid conflicts, the sequence reference should be re-pointed to the desired sequence using the ALTER TABLE command2.

•Storage integrations: If a stage or a table references a storage integration, the cloned object may still reference the source storage integration, which may not be accessible or valid in the new environment. To avoid errors, the storage integration reference should be re-pointed to the desired storage integration using the ALTER STAGE or ALTER TABLE command34.

•Views, secure views, and materialized views: If a view references another view or table, the cloned view may still reference the source object, which may not be accessible or valid in the new environment. To avoid errors, the view reference should be re-pointed to the desired object using the CREATE OR REPLACE VIEW command5.

1: Cloning Considerations | Snowflake Documentation 2: [ALTER TABLE | Snowflake Documentation] 3: [ALTER STAGE | Snowflake Documentation] 4: [ALTER TABLE | Snowflake Documentation] 5: [CREATE VIEW | Snowflake Documentation]

According to the Understanding & Using Time Travel documentation, Time Travel is a feature that allows you to query, clone, and restore historical data in tables, schemas, and databases for up to 90 days. To meet the requirements of the scenario, the following best practices should be followed:

•The fact and dimension tables should have the same DATA_RETENTION_TIME_IN_DAYS. This parameter specifies the number of days for which the historical data is preserved and can be accessed by Time Travel. To ensure that the fact and dimension tables can be reverted to a consistent state in case of any anomalies in the latest load, they should have the same retention period. Otherwise, some tables may lose their historical data before others, resulting in data inconsistency and quality issues.

•The fact and dimension tables should be cloned together using the same Time Travel options to reduce potential referential integrity issues with the restored data. Cloning is a way of creating a copy of an object (table, schema, or database) at a specific point in time using Time Travel. To ensure that the fact and dimension tables are cloned with the same data set, they should be cloned together using the same AT or BEFORE clause. This will avoid any referential integrity issues that may arise from cloning tables at different points in time.

The other options are incorrect because:

•Related data should not be placed together in the same schema. Facts and dimension tables should each have their own schemas. This is not a best practice for Time Travel, as it does not affect the ability to query, clone, or restore historical data. However, it may be a good practice for data modeling and organization, depending on the use case and design principles.

•The DATA_RETENTION_TIME_IN_DAYS should be kept at the account level and neverused for lower level containers (databases and schemas). This is not a best practice for Time Travel, as it limits the flexibility and granularity of setting the retention period for different objects. The retention period can be set at the account, database, schema, or table level, and the most specific setting overrides the more general ones. This allows for customizing the retention period based on the data needs and characteristics of each object.

•Only TRANSIENT tables should be used to ensure referential integrity between the fact and dimension tables. This is not a best practice for Time Travel, as it does not affect the referential integrity between the tables. Transient tables are tables that do not have a Fail-safe period, which means that they cannot be recovered by Snowflake after the retention period ends. However, they still support Time Travel within the retention period, and can be queried, cloned, and restored like permanent tables. The choice of table type depends on the data durability and availability requirements, not on the referential integrity.

According to the Multi-cluster Warehouses documentation, auto-suspend is a feature that allows a warehouse to automatically suspend itself after a specified period of inactivity. For a multi-cluster warehouse, auto-suspend applies to the entire warehouse, not to individual clusters. Therefore, auto-suspend occurs when the minimum number of clusters is running and there is no activity for the specified period of time. The other options are incorrect because:

•A. Auto-suspend does not occur when there has been no activity on any cluster for the specified period of time. This would imply that each cluster has its own auto-suspend timer, which is not the case. The warehouse has a single auto-suspend timer that is reset by any activity on any cluster.

•B. Auto-suspend does not occur after a specified period of time when an additional cluster has started on the maximum number of clusters specified for a warehouse. This would imply that the auto-suspend timer is affected by the number of clusters running, which is not the case. The auto-suspend timer is only affected by the activity on the warehouse, regardless of the number of clusters running.

•D. Auto-suspend does apply for multi-cluster warehouses, as explained above. It is a feature that can be enabled or disabled for any warehouse, regardless of the number of clusters.