Mohan, Data engineer works with ALTUSO Company, wants to programmatically check the status of the query. He needs query id to identify each query executed by Snowflake & using Snowflake Connector for Python to execute a query, how he will be able to meet this requirements. Select the best options you will suggest?

• A.He can access the query ID through the sfqid attribute in the Cursor object.
  1.cur = con.cursor()
  2.cur.execute("SELECT * FROM snowtable")
  3.print(cur.sfqid)
• B.When he used the Snowflake Connector for Python to execute a query, he can access the query ID through the pyqueryid attribute in the Cursor object.
• C.He needs to query history views to get the queryID as best practices.
• D.Using python connector, snowflake does not support queryID retrieval for both syn-chronous & asynchronous query.

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Which query will show a list of the 20 most recent executions of a specified task kttask, that have been scheduled within the last hour that have ended or are stillrunning's.

• A.
• B.
• C.
• D.

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Within a Snowflake account permissions have been defined with custom roles and role hierarchies.
To set up column-level masking using a role in the hierarchy of the current user, what command would be used?

• A.CORRECT_ROLE
• B.IKVOKER_ROLE
• C.IS_RCLE_IN_SESSION
• D.IS_GRANTED_TO_INVOKER_ROLE

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1.+--------------------------------------------------------------+
2.| SYSTEM$CLUSTERING_INFORMATION('SF_DATA', '(COL1, COL3)') |
3.|--------------------------------------------------------------|
4.| { |
5.| "cluster_by_keys" : "(COL1, COL3)", |
6.| "total_partition_count" : 1156, |
7.| "total_constant_partition_count" : 0, |
8.| "average_overlaps" : 117.5484, |
9.| "average_depth" : 64.0701, |
10.| "partition_depth_histogram" : { |
11.| "00000" : 0, |
12.| "00001" : 0, |
13.| "00002" : 3, |
14.| "00003" : 3, |
15.| "00004" : 4, |
16.| "00005" : 6, |
17.| "00006" : 3, |
18.| "00007" : 5, |
19.| "00008" : 10, |
20.| "00009" : 5, |
21.| "00010" : 7, |
22.| "00011" : 6, |
23.| "00012" : 8, |
24.| "00013" : 8, |
25.| "00014" : 9, |
26.| "00015" : 8, |
27.| "00016" : 6, |
28.| "00032" : 98, |
29.| "00064" : 269, |
30.| "00128" : 698 |
31.| } |
32.| } |
33.+--------------------------------------------------------------+
The Above example indicates that the SF_DATA table is not well-clustered for which of following valid reasons?

• A.High average of overlapping micro-partitions.
• B.High average of overlap depth across micro-partitions.
• C.ALL of the above
• D.Most of the micro-partitions are grouped at the lower-end of the histogram, with the majority of micro-partitions having an overlap depth between 64 and 128.
• E.Zero (0) constant micro-partitions out of 1156 total micro-partitions.

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A Data Engineer is working on a continuous data pipeline which receives data from Amazon Kinesis Firehose and loads the data into a staging table which will later be used in the data transformation process The average file size is 300-500 MB.
The Engineer needs to ensure that Snowpipe is performant while minimizing costs.
How can this be achieved?

• A.Increase the size of the virtual warehouse used by Snowpipe.
• B.Split the files before loading them andset the SIZE_LIMIT option to 250 MB.
• C.Change the file compression size and increase the frequency of the Snowpipe loads
• D.Decrease the buffer size to trigger delivery of files sized between 100 to 250 MB in Kinesis Firehose

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