Snowflake ADA-C01 - SnowPro Advanced Administrator

According to the Snowflake documentation1, a multi-cluster warehouse is a virtual warehouse that consists of multiple clusters of compute resources that can scale up or down automatically to handle the concurrency and performance needs of the queries submitted to the warehouse. A multi-cluster warehouse has a minimum and maximum number of clusters that can be specified by the administrator. Option A is a possible solution to address the issues happening once a month, as it allows the administrator to use a task to increase the cluster size for the time period that the more complex queries are running and another task to reduce the size of the cluster once the complex queries complete. This way, the warehouse can have more resources available to handle the complex queries without reaching the maximum limit of 10 clusters, and then return to the normal cluster size to save costs. Option B is another possible solution to address the issues happening once a month, as it allows the administrator to have the group running the complex monthly queries use a separate appropriately-sized warehouse to support their workload. This way, the warehouse can isolate the complex queries from the standard workload and avoid queue time and resource contention. Option C is not a recommended solution to address the issues happening once a month, as it would increase the costs and complexity of managing the multi-cluster warehouse, and may not solve the underlying problem of inefficient queries. Option D is a good practice to improve the performance of the queries, but it is not a direct solution to address the issues happening once a month, as it requires analyzing and optimizing the complex queries using clustering keys or materialized views, which may not be feasible or effective in all cases. Option E is not a recommended solution to address the issues happening once a month, as it would increase the costs and waste resources by starting more clusters than needed for the standard workload.

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 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 specifya 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.

The inclusion of the CURRENT_TIMESTAMP() function in the second query prevents it from re-using the first query’s persisted result cache because this function makes each execution unique due to the constantly changing timestamp. According to the Snowflake documentation, “The query does not include non-reusable functions, which return different results for successive runs of the same query. UUID_STRING, RANDOM, and RANDSTR are good examples of non-reusable functions.” The CURRENT_TIMESTAMP() function is another example of a non-reusable function, as it returns the current date and time at the start of query execution, which varies for each run. Therefore, the second query is not identical to the first query, and the result cache is not reused. The other options are either incorrect or irrelevant to the question. Option B is incorrect, as the CURRENT_DATE() function is a reusable function, as it returns the same value for all queries executed within the same day. Option C is irrelevant, as the virtual warehouse used to execute the query does not affect the result cache reuse. Option D is also irrelevant, as the role used to execute the query does not affect the result cache reuse, as long as the role has the necessary access privileges for all the tables used in the query.

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 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, 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.

Scenario: You want to rollback changes due to a problematic query (e.g., accidental data modification or corruption). Snowflake provides two powerful tools:

✅B. CLONE ... BEFORE (STATEMENT => 'query_id')

This usesTime Travel + Zero-Copy Cloning.

You canclone a table as it existed before a specific query.

It creates a full copy of the table's state at that momentwithout duplicating storage.

Example:

CREATE TABLE prd_table_bkp CLONE prd_table

BEFORE (STATEMENT => '01a2b3c4-0000-0000-0000-123456789abc');

✅D. ALTER TABLE ... SWAP WITH ...

Once you've cloned the backup, you canswap itwith the live table.

This is afast, atomic operation— ideal for rollback.

Example:

ALTER TABLE prd_table SWAP WITH prd_table_bkp;

❌Why the Other Options Are Incorrect:

A. SELECT SYSTEM$CANCEL_QUERY(...)

Cancels a currently running query — doesn’t help if the query already executed and caused damage.

C. CREATE TABLE ... AS SELECT * FROM RESULT_SCAN(...)

Reconstructsresults, not the original table.

Only captures output rows, not full table state.

Not ideal for rollback.

E. Contact Snowflake Support to retrieve Fail-safe data

Fail-safe is for disaster recovery only, and only accessible by Snowflake support.

It’snotintended for routine rollback or recovery and has a 7-day fixed retention (non-configurable).

📘SnowPro Administrator References:

Zero-Copy Cloning with Time Travel

ALTER TABLE SWAP

System Functions – SYSTEM$CANCEL_QUERY

Fail-safe Overview

A user with the ORGADMIN role can perform the following tasks1:

•Create one or more accounts in the organization.

•View a list of all regions enabled for the organization.

•View usage information for all accounts in the organization.

Option C is incorrect because creating secure views on application tables is not a function of the ORGADMIN role, but rather a function of the roles that have access to the tables and schemas within the accounts. Option E is incorrect because performing zero-copy cloning on account data is not a function of the ORGADMIN role, but rather a function of the roles that have the CLONE privilege on the objects within the accounts. Option F is incorrect because creating a reader account to share data with another organization is not a function of the ORGADMIN role, but rather a function of the roles that have the CREATE SHARE privilege on the objects within the accounts.

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.