Snowflake ADA-C01 SnowPro Advanced Administrator Exam Practice Test

According to the AT | BEFORE documentation, the AT or BEFORE clause is used for Snowflake Time Travel, which allows you to query historical data from a table based on a specific point in the past. The clause can use one of the following parameters to pinpoint the exact historical data you wish to access:

•TIMESTAMP: Specifies an exact date and time to use for Time Travel.

•OFFSET: Specifies the difference in seconds from the current time to use for Time Travel.

•STATEMENT: Specifies the query ID of a statement to use as the reference point for Time Travel.

Therefore, the queries that will allow the user to view the historical data that was in the table before the query was executed are:

•B. SELECT * FROM my_table AT (TIMESTAMP => ‘2021-01-01 07:00:00’ :: timestamp); This query uses the TIMESTAMP parameter to specify a point in time that is before the query execution time of 07:01.

•D. SELECT * FROM my table PRIOR TO STATEMENT ‘8e5d0ca9-005e-44e6-b858-a8f5b37c5726’; This query uses the PRIOR TO STATEMENT keyword and the STATEMENT parameter to specify a point in time that is immediately preceding the query execution time of 07:01.

•F. SELECT * FROM my_table BEFORE (STATEMENT => ‘8e5d0ca9-005e-44e6-b858-a8f5b37c5726’); This query uses the BEFORE keyword and the STATEMENT parameter to specify a point in time that is immediately preceding the query execution time of 07:01.

The other queries are incorrect because:

•A. SELECT * FROM my table WITH TIME_TRAVEL (OFFSET => -60*30); This query uses the OFFSET parameter to specify a point in time that is 30 minutes before the current time, which is 07:30. This is after the query execution time of 07:01, so it will not show the historical data before the query was executed.

•C. SELECT * FROM TIME_TRAVEL (‘MY_TABLE’, 2021-01-01 07:00:00); This query is not valid syntax for Time Travel. The TIME_TRAVEL function does not exist in Snowflake. The correct syntax is to use the AT or BEFORE clause after the table name in the FROM clause.

•E. SELECT * FROM my_table AT (OFFSET => -60*30); This query uses the AT keyword and the OFFSET parameter to specify a point in time that is 30 minutes before the current time, which is 07:30. This is equal to the query execution time of 07:01, so it will not show the historical data before the query was executed. The AT keyword specifies that the request is inclusive of any changes made by a statement or transaction with timestamp equal to the specified parameter. To exclude the changes made by the query, the BEFORE keyword should be used instead.

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 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.

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.

According to the Snowflake documentation1, resource monitors are a feature that helps you manage and control Snowflake costs by monitoring and setting limits on your compute resources. Resource monitors do not consume any credits or add any load to the virtual warehouses they monitor1. Resource monitors can also have multiple triggers that specify different actions (such as suspending or notifying) when certain percentages of the credit quota are reached2. Resource monitors can be applied to either the entire account or a specific set of individual warehouses1. The other options are not benefits of resource monitors. The cost of running a resource monitor is negligible, not 10% of a credit3. Multiple resource monitors cannot be applied to a single virtual warehouse; only one resource monitor can be assigned to a warehouse at a time2. Resource monitor governance is not tightly controlled; account administrators can enable users with other roles to view and modify resource monitors using SQL2.

According to the Snowflake documentation1, organizations are a feature that allows linking the accounts owned by a business entity, simplifying account management and billing, replication and failover, data sharing, and other account administration tasks. Some of the benefits of using organizations are:

•Administrators can monitor and understand usage across all accounts in the organization using the ORGANIZATION_USAGE schema, which provides historical usage data for all accounts in the organization via views in a shared database named SNOWFLAKE2. This can help to optimize costs and performance across the organization.

•Administrators have the ability to create accounts in any available cloud provider or region using the CREATE ACCOUNT command, which allows specifying the cloud platform and region for the new account3. This can help to meet the business needs and compliance requirements of the organization.

Option A is incorrect because administrators cannot change Snowflake account editions on-demand based on need, but rather have to contact Snowflake Support to request an edition change4. Option C is incorrect because administrators cannot simplify data movement across all accounts within the organization, but rather have to enable account database replication for both the source and target accounts, and use the ALTER DATABASE … ENABLE REPLICATION TO ACCOUNTS command to promote a local database to serve as the primary database and enable replication to the target accounts5. Option D is incorrect because user administration is not simplified across all accounts within the organization, but rather requires creating and managing users, roles, and privileges for each account separately, unless using a federated authentication method such as SSO or SCIM.

Option B is the correct SQL command to create a resource monitor that meets the requirements. It sets the credit quota to 3000, the frequency to yearly, the start timestamp to January 1, 2022, and the triggers to notify and suspend the warehouse at the specified thresholds. Option A is incorrect because it does not specify the frequency. Option C is incorrect because it does not specify the frequency and it uses notify and suspend instead of suspend and suspend_immediate. Option D is incorrect because it does not specify the start timestamp. For more information about resource monitors, see Working with Resource Monitors and CREATE RESOURCE MONITOR.

The MINS_TO_BYPASS_MFA property allows the account administrator to temporarily disable MFA for a user who has lost their phone or changed their phone number1. The user can log in without MFA for the specified number of minutes, and then re-enroll in MFA using their new phone1. This does not revoke their MFA enrollment, unlike the DISABLE_MFA property, which cancels their enrollment and requires them to re-enroll from scratch1. The other options are not valid ways to bypass MFA, as SnowSQL does support MFA authentication2, and there is no such URL parameter as /?mode=mfa_bypass&code= for Snowflake3

This code snippet will create a row access policy that returns true if the current role is ALL_ACCESS_ROLE or if the current role matches the region value in the entitlement_table. This means that the ALL_ACCESS_ROLE can see all rows regardless of region, while the other roles can only see rows for their own regions. According to the Snowflake documentation, the CURRENT_ROLE context function returns the name of the current role for the session. The EXISTS function returns true if the subquery returns any rows. The OR operator returns true if either operand is true. Therefore, this code snippet satisfies the requirements of the question.

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.

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.

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.

The EXPLAIN command returns the logical execution plan for a query, which shows the upper bound estimates for the number of partitions and bytes that might be scanned by the query1. However, these estimates do not account for the runtime optimizations that Snowflake performs to improve the query performance and reduce the resource consumption2. One of these optimizations is join pruning, which eliminates unnecessary partitions from the join inputs based on the join predicates2. This can result in fewer partitions and bytes scanned than the estimates from the EXPLAIN output3. Therefore, the actual partitions scanned value in the Query Profile can be lower than the partitionsAssigned value in the EXPLAIN output4.

According to the Snowflake documentation1, the AUTO_SUSPEND property specifies the number of seconds of inactivity after which a warehouse is automatically suspended. If the property is set to 0 or NULL, the warehouse never suspends automatically. For a warehouse that supports several dashboards, issuing various queries on the same database, setting AUTO_SUSPEND to 0 or NULL can help to keep the data cache warm, which means that the data used by the queries is already loaded into the warehouse memory and does not need to be fetched from the storage layer. This can improve the performance of similar queries that access the same data. Option A is incorrect because setting AUTO_SUSPEND to 0 or NULL does not save costs on warehouse shutdowns and startups, but rather increases the costs by keeping the warehouse running continuously. Option B is incorrect because setting AUTO_SUSPEND to 0 or NULL does not run the warehouse as little as possible, but rather runs the warehouse as much as possible. Option D is incorrect because setting AUTO_SUSPEND to 0 or NULL does not affect the query result cache, which is a separate cache that stores the results of previous queries for a period of time. The query result cache is not dependent on the warehouse state, but on the query criteria2.

Snowflake’s query optimizer can automatically rewrite queries against the base table or regular views to use the materialized view instead, if the query matches the definition of the materialized view1. However, this is not always guaranteed, as the optimizer might decide against using the materialized view based on various factors, such as the freshness of the data, the size of the result set, the complexity of the query, and the availability of the materialized view2. Therefore, the answer is no, because the optimizer might decide against it.

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 never used 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 Snowflake documentation1, creating a user with a default role does not automatically grant that role to the user. The user must be explicitly granted the role by the role owner or a higher-level role. Therefore, the USERADMIN role, which created the DEVELOPER_ROLE, needs to explicitly grant the DEVELOPER_ROLE to the new user PTORRES using the GRANT ROLE command. Otherwise, the user PTORRES will not be able to use the DEVELOPER_ROLE and will see the default role of PUBLIC in the web UI. Option A is incorrect because the DEVELOPER_ROLE does not need to be granted to SYSADMIN before user PTORRES can use the role. Option B is incorrect because the new role can take effect immediately after it is created and granted to the user, and does not depend on the USERADMIN role logging out. Option D is incorrect because the new role will not be affected by the identity provider synchronization, as it is created and managed in Snowflake.

According to the Snowflake documentation1, the authentication process for SCIM provisioning uses an OAuth Bearer token and this token is valid for six months. Customers must keep track of their authentication token and can generate a new token on demand. If the token expires, the SCIM provisioning process will fail. Therefore, the token must be regenerated before it expires. The other options are not required for SCIM provisioning.

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.

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.