Snowflake ADA-C01 Practice Test Questions Answers

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.

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.

This command will generate a sample of 20% of the table by using the SYSTEM (or BLOCK) sampling method, which selects each block of rows with a probability of 20/100. This method is suitable for taking data by blocks of rows, as the question requires. According to the Snowflake documentation, “SYSTEM (or BLOCK): Includes each block of rows with a probability of p/100. Similar to flipping a weighted coin for each block of rows. This method does not support fixed-size sampling.” The other options are either incorrect or do not meet the requirements of the question. Option A uses the BERNOULLI (or ROW) sampling method, which selects each row with a probability of 20/100, but does not take data by blocks of rows. Option C uses the BLOCK sampling method, but specifies a fixed number of rows (20) instead of a percentage (20%). Option D uses the SYSTEM sampling method, but specifies a seed value (1), which makes the sampling repeatable, contrary to the question.

According to the Introduction to External Tables documentation, an external table is a Snowflake feature that allows you to query data stored in an external stage as if the data were inside a table in Snowflake. The external stage is not part of Snowflake, so Snowflake does not store or manage the stage. This simplifies the data pipeline by eliminating the need to load the data into Snowflake before querying it. External tables can access data stored in any format that the COPY INTO <title> command supports, such as CSV, JSON, AVRO, ORC, or PARQUET. The other scenarios do not involve external tables, but rather require data loading, unloading, or federation.

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 incorrectbecause it grants the REFERENCE_USAGE privilege on the wrong database.