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Snowflake SnowPro Advanced Administrator Sample Questions (Q76-Q81):

NEW QUESTION # 76
Which masking policy will mask a column whenever it is queried through a view owned by a role named MASKED_VIEW_ROLE?

A. create or replace masking policy maskString as (val string) returns string -> case when current_role() in ('MASKED_VIEW_ROLE') then ' ********* ' else val end;
B. create or replace masking policy maskstring as (val string) returns string -> case when is_role_in_session ('MASKED_VIEW_ROLE') then ' ** else val end;
*,
C. create or replace masking policy maskstring as (val string) returns string -> case when invoker_role() in ('MASKED_VIEW_ROLE') then else val end;
' **
D. create or replace masking policy maskString as (val string) returns string -> case when array_contains ('MASKED_VIEW_ROLE' :: variant, parse_json (current_available_roles ())) then
'*
else val
end;
** '

Answer: B

Explanation:
Explanation
A masking policy is a SQL expression that transforms the data in a column based on the role that queries the column1. The is_role_in_session function returns true if the specified role is in the current session2. Therefore, the masking policy in option A will mask the column data with asterisks whenever it is queried through a view owned by the MASKED_VIEW_ROLE3. The other options use different functions that do not check the ownership of the view, but rather the current role, the invoker role, or the available roles in the session45.
These functions may not return the desired result if the role that owns the view is different from the role that queries the view.

NEW QUESTION # 77
An Administrator is evaluating a complex query using the EXPLAIN command. The Globalstats operation indicates 500 partitionsAssigned.
The Administrator then runs the query to completion and opens the Query Profile. They notice that the partitions scanned value is 429.
Why might the actual partitions scanned be lower than the estimate from the EXPLAIN output?

A. Runtime optimizations such as join pruning can reduce the number of partitions and bytes scanned during query execution.
B. The EXPLAIN results always include a 10-15% safety factor in order to provide conservative estimates.
C. In-flight data compression will result in fewer micro-partitions being scanned at the virtual warehouse layer than were identified at the storage layer.
D. The GlobalStats partition assignment includes the micro-partitions that will be assigned for preservation of the query results.

Answer: A

Explanation:
Explanation
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.

NEW QUESTION # 78
An Administrator has a user who needs to be able to suspend and resume a task based on the current virtual warehouse load, but this user should not be able to modify the task or start a new run.
What privileges should be granted to the user to meet these requirements? (Select TWO).

A. OWNERSHIP on the database and schema containing the task
B. EXECUTE TASK on the task
C. OPERATE on the task
D. USAGE on the database and schema containing the task
E. OWNERSHIP on the task

Answer: C,D

Explanation:
Explanation
The user needs the OPERATE privilege on the task to suspend and resume it, and the USAGE privilege on the database and schema containing the task to access it1. The EXECUTE TASK privilege is not required for suspending and resuming a task, only for triggering a new run1. The OWNERSHIP privilege on the task or the database and schema would allow the user to modify or drop the task, which is not desired.

NEW QUESTION # 79
A Snowflake Administrator is investigating why a query is not re-using the persisted result cache.
The Administrator found the two relevant queries from the SNOWFLAKE. ACCOUNT_USAGE. QUERY_HISTORY view:

Why is the second query re-scanning micro-partitions instead of using the first query's persisted result cache?

A. The second query includes a CURRENT_DATE () function.
B. The second query includes a CURRENT_TIMESTAMP () function.
C. The queries are executed with two different roles.
D. The queries are executed with two different virtual warehouses.

Answer: B

Explanation:
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.

NEW QUESTION # 80
MY_TABLE is a table that has not been updated or modified for several days. On 01 January 2021 at 07:01, a user executed a query to update this table. The query ID is
'8e5d0ca9-005e-44e6-b858-a8f5b37c5726'. It is now 07:30 on the same day.
Which queries will allow the user to view the historical data that was in the table before this query was executed? (Select THREE).

A. SELECT * FROM my table PRIOR TO STATEMENT '8e5d0ca9-005e-44e6-b858-a8f5b37c5726';
B. SELECT * FROM TIME_TRAVEL ('MY_TABLE', 2021-01-01 07:00:00);
C. SELECT * FROM my table WITH TIME_TRAVEL (OFFSET => -60*30);
D. SELECT * FROM my_table BEFORE (STATEMENT => '8e5d0ca9-005e-44e6-b858-a8f5b37c5726');
E. SELECT * FROM my_table AT (OFFSET => -60*30);
F. SELECT * FROM my_table AT (TIMESTAMP => '2021-01-01 07:00:00' :: timestamp);

Answer: A,D,F

Explanation:
Explanation
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.

NEW QUESTION # 81
......

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