Free Snowflake ARA-C01 Exam Dumps Questions & Answers

Which SQL ALTER command will MAXIMIZE memory and compute resources for a Snowpark stored procedure when executed on the snowpark_opt_wh warehouse?

• A.ALTER WAREHOUSE snowpark_opt_wh SET MAX_CONCURRENCY_LEVEL = 1;
• B.ALTER WAREHOUSE snowpark_opt_wh SET MAX_CONCURRENCY_LEVEL = 16;
• C.ALTER WAREHOUSE snowpark_opt_wh SET MAX_CONCURRENCY_LEVEL = 8;
• D.ALTER WAREHOUSE snowpark_opt_wh SET MAX_CONCURRENCY_LEVEL = 2;

A new user user_01 is created within Snowflake. The following two commands are executed:
Command 1# SHOW GRANTS TO USER user_01;
Command 2# SHOW GRANTS ON USER user_01;
What inferences can be made about these commands?

• A.Command 1 defines all the grants which are given to user_01Command 2 defines which role owns user_01
• B.Command 1 defines which user owns user_01Command 2 defines all the grants which have been given to user_01
• C.Command 1 defines which role owns user_01Command 2 defines all the grants which have been given to user_01
• D.Command 1 defines all the grants which are given to user_01Command 2 defines which user owns user_01

An Architect is implementing a CI/CD process. When attempting to clone a table from a production to a development environment, the cloning operation fails.
What could be causing this to happen?

• A.The table has a masking policy.
• B.The retention time for the table is set to zero.
• C.The table is transient.
• D.Tables cannot be cloned from a higher environment to a lower environment.

An Architect has a design where files arrive every 10 minutes and are loaded into a primary database table using Snowpipe. A secondary database is refreshed every hour with the latest data from the primary database.
Based on this scenario, what Time Travel query options are available on the secondary database?

• A.Using Time Travel, secondary database users can query every iterative version within each hour (the individual Snowpipe loads) and outside the retention window.
• B.Using Time Travel, secondary database users can query every iterative version within each hour (the individual Snowpipe loads) in the retention window.
• C.A query using Time Travel in the secondary database is available for every hourly table version within the retention window.
• D.A query using Time Travel in the secondary database is available for every hourly table version within and outside the retention window.

An Architect has a table called leader_follower that contains a single column named JSON. The table has one row with the following structure:
{
"activities": [
{ "activityNumber": 1, "winner": 5 },
{ "activityNumber": 2, "winner": 4 }
],
"follower": {
"name": { "default": "Matt" },
"number": 4
},
"leader": {
"name": { "default": "Adam" },
"number": 5
}
}
Which query will produce the following results?
ACTIVITY_NUMBER
WINNER_NAME
1
Adam
2
Matt

• A.SELECT lf.json:activities.activityNumber AS activity_number,
  IFF(
  lf.json:activities.activityNumber = lf.json:leader.number,
  lf.json:leader.name.default,
  lf.json:follower.name.default
  )::VARCHAR
  FROM leader_follower lf;
• B.value:winner = lf.json:leader.number,
  lf.json:leader.name.default,
  lf.json:follower.name.default
  )::VARCHAR AS winner_name
  FROM leader_follower lf,
  LATERAL FLATTEN(input => json:activities, outer => TRUE) p;
• C.value:activityNumber AS activity_number,
  IFF(
• D.value:activityNumber AS activity_number,
  IFF(
• E.value:winner = lf.json:leader.number,
  lf.json:leader,
  lf.json:follower
  )::VARCHAR AS winner_name
  FROM leader_follower lf,
  LATERAL FLATTEN(input => json:activities) p;
• F.SELECT
• G.value:winner = lf.json:leader.number,
  lf.json:leader.name.default,
  lf.json:follower.name.default
  )::VARCHAR AS winner_name
  FROM leader_follower lf,
  LATERAL FLATTEN(input => json:activities) p;
• H.value:activityNumber AS activity_number,
  IFF(
• I.SELECT
• J.SELECT