ADA-C01 SnowPro Advanced: Administrator Certification Exam Questions and Answers

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.

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 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.

According to the Snowflake documentation1, when a role is dropped, ownership of all objects owned by the dropped role is transferred to the role that is directly above the dropped role in the role hierarchy. This is to ensure that there is always a single owner for each object in the system.

1: Drop Role | Snowflake Documentation

Option B is the correct answer because it follows the steps described in the Snowflake documentation for sharing data from multiple databases using secure views. The data provider needs to grant the REFERENCE_USAGE privilege on each database that contains objects referenced by the secure view, and the USAGE privilege only on the database where the secure view is created. Option A is incorrect because it grants the USAGE privilege instead of the REFERENCE_USAGE privilege. Option C is incorrect because it grants the REFERENCE_USAGE privilege to a database role, which is not supported. Option D is incorrect because it grants the REFERENCE_USAGE privilege on the wrong database.

According to the Snowflake documentation1, network policies are a feature that allows restricting access to your account based on user IP address. A network policy can be applied to an account, a user, or a security integration, and can specify a list of allowed IP addresses and a list of blocked IP addresses. If there are network policies applied to more than one of these, the most specific network policy overrides more general network policies. In this case, the user1 has a network policy (bar_policy) applied to them, which overrides the account-level network policy (foo_policy). The bar_policy allows access only from the IP range 3.3.3.0/24, and blocks access from the IP address 3.3.3.10. Therefore, the user1 will not be able to log in to Snowflake from IP address 3.3.3.10, as it is found in the BLOCKED_IP_LIST of bar_policy. Option A is incorrect because the ALLOWED_IP_LIST of bar_policy does not override the BLOCKED_IP_LIST of bar_policy. Option C is incorrect because the ALLOWED_IP_LIST of foo_policy does not apply to user1, as it is overridden by the user-level network policy. Option D is incorrect because the ALLOWED_IP_LIST of foo_policy does not matter, as it is overridden by the user-level network policy.

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 command can be executed by the ACCOUNTADMIN role, as well as any role that has been granted the ORGADMIN role1.

To request and get data from a Data Exchange, the role in Account 123 must have the USAGE privilege on the Data Exchange object. This privilege allows the role to view the listings and request access to the data. According to the Snowflake documentation, “To view the listings in a data exchange, a role must have the USAGE privilege on the data exchange object. To request access to a listing, a role must have the USAGE privilege on the data exchange object and the IMPORT SHARE privilege on the account.” The other options are either incorrect or not sufficient to request and get data from a Data Exchange. Option A is incorrect, as the ACCOUNTADMIN role is not the only role that can request and get data, as long as other roles have the necessary privileges. Option C is incorrect, as the IMPORT SHARE and CREATE DATABASE privileges are not required to request and get data, but only to create a database from a share after the access is granted. Option D is incorrect, as the listing provider does not designate the authorized roles in Account 123, but only approves or denies the requests from Account 123.

According to the Snowflake documentation1, stages without credentials are a way to create external stages that use storage integrations to access data files in cloud storage without providing any credentials to Snowflake. Storage integrations are objects that define a trust relationship between Snowflake and a cloud provider, allowing Snowflake to authenticate and authorize access to the cloud storage. To limit data exfiltration after a storage integration and associated stages are created, the following account-level parameters can be set:

•REQUIRE_STORAGE_INTEGRATION_FOR_STAGE_CREATION: This parameter enforces that all external stages must be created using a storage integration. This prevents users from creating external stages with inline credentials or URLs that point to unauthorized locations.

•REQUIRE_STORAGE_INTEGRATION_FOR_STAGE_OPERATION: This parameter enforces that all operations on external stages, such as PUT, GET, COPY, and LIST, must use a storage integration. This prevents users from performing operations on external stages with inline credentials or URLs that point to unauthorized locations.

•PREVENT_UNLOAD_TO_INLINE_URL: This parameter prevents users from unloading data from Snowflake tables to inline URLs that do not use a storage integration. This prevents users from exporting data to unauthorized locations.

Therefore, the correct answer is option D, which sets all these parameters to true. Option A is incorrect because it sets PREVENT_UNLOAD_TO_INLINE_URL to false, which allows users to unload data to inline URLs that do not use a storage integration. Option B is incorrect because it sets both REQUIRE_STORAGE_INTEGRATION_FOR_STAGE_CREATION and REQUIRE_STORAGE_INTEGRATION_FOR_STAGE_OPERATION to false, which allows users to create and operate on external stages without using a storage integration. Option C is incorrect because it sets all the parameters to false, which does not enforce any restrictions on data exfiltration.

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, 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.

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.

Snowflake charges for database replication based on two categories: data transfer and compute resources1. Data transfer costs depend on the amount of data that is transferred from the primary database to the secondary database across regions and/or cloud service providers2. Compute resource costs depend on the use of Snowflake-provided compute resources to copy data between accounts across regions1. Both data transfer and compute resource costs are proportional to the frequency and amount of changes to the primary database as a result of data loading or DML operations3. Therefore, the answer is A and B. The other options are not directly related to the replication billing, as the frequency of secondary database refreshes does not affect the amount of data transferred or copied4, and the number and size of warehouses defined in the primary account do not affect the replication process5.

To configure a Snowflake account to use AWS PrivateLink, the Administrator needs to create CNAME records in the DNS that point to the private endpoints provided by Snowflake. This allows the clients to connect to Snowflake using the same URL as before, but with private connectivity. According to the Snowflake documentation, “After you have created the VPC endpoints, Snowflake provides you with a list of private endpoints for your account. You must create CNAME records in your DNS that point to these private endpoints. The CNAME records must use the same hostnames as the original Snowflake URLs for your account.” The other options are either incorrect or not sufficient to configure AWS PrivateLink. Option B is not necessary, as the Administrator can enable AWS PrivateLink using the SYSTEM$AUTHORIZE_PRIVATELINK function1. Option C is not recommended, as it may prevent some data traffic from reaching Snowflake, such as large result sets stored on AWS S32. Option D is not related to AWS PrivateLink, but to Snowflake Connectivity Diagnostic (SnowCD), which is a tool for diagnosing network issues between clients and Snowflake3.

According to the Snowflake documentation1, data sharing is a feature that allows sharing selected objects in a database in one account with other accounts in the same organization, without copying or transferring any data. Data sharing is supported across regions and across cloud platforms, but it requires enabling account database replication for both the source and target accounts2. Data replication is a feature that allows replicating objects from a source account to one or more target accounts in the same organization, providing read-only access for the replicated objects. Data replication is also supported across regions and across cloud platforms, but it incurs additional storage costs for the replicated data2. Therefore, the best way to share data bi-directionally among all offices, while minimizing costs, is to use data sharing among offices in the same region, which does not require replication or additional storage, and use replication among offices across the continents, which provides near real-time access to the shared data. Option A is incorrect because using data replication everywhere would increase the costs associated with additional storage and compute resources for the replicated data. Option B is incorrect because using the PUT command to move files to an Amazon S3 bucket and Azure Blobs, and using an external file management application to move files within the corporate VPC, would not leverage the benefits of Snowflake’s data sharing and replication features, and would also incur additional costs and complexity for data transfer and synchronization. Option C is incorrect because moving all the Snowflake accounts to a single region would violate the PII regulations in some of the European countries, and would also incur additional costs and complexity for data migration and consolidation.

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. This can improve the performance of the second query by avoiding reading the same data from micro-partitions again. The other actions do not provide optimal performance for the second query because:

•Assigning additional clusters to the virtual warehouse does not affect the data access speed, but only the query execution speed. It also increases the cost of the warehouse.

•Increasing the STATEMENT_TIMEOUT_IN_SECONDS parameter in the session does not improve the performance of the query, but only allows it to run longer before timing out. It also increases the risk of resource contention and deadlock.

•Preventing the virtual warehouse from suspending between the running of the first and second queries does not guarantee that the data will be cached in memory, as Snowflake uses a least recently used (LRU) cache eviction policy. It also increases the cost of the warehouse.

https://docs.snowflake.com/en/user-guide/querying-persisted-results

According to the Snowflake documentation1, you can change a managed access schema to a regular schema using the ALTER SCHEMA statement with the DISABLE MANAGED ACCESS keywords. This will disable the managed access feature on the schema and revert the access control to the default behavior. Option B is incorrect because dropping and recreating the schema will also delete all the objects and metadata in the schema, which is not necessary to disable the managed access. Option C is incorrect because revoking the privileges on the future tables from the role is not required to disable the managed access. Option D is incorrect because there is no WITHOUT MANAGED ACCESS option in the CREATE SCHEMA statement.