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Snowflake SnowPro Advanced: Data Analyst Certification Sample Questions:

1. A Data Analyst needs to write a query that will return all projects from a project table and all employees from an employee table. What type of join should be used in this query?

A) Inner join
B) Left outer join
C) Full outer join
D) Cross join

2. What functionalities are available when a Snowflake worksheet is shared with other users? (Select TWO).

A) Users with edit permissions can view past versions of the worksheet.
B) If multiple users edit and run a shared worksheet at the same time, each run of the worksheet will create a new version.
C) If the worksheet is being edited, the collaborators will be able to see these edits in real-time.
D) Whenever a user with permissions runs a worksheet, the existing version history of the worksheet will be overwritten.
E) Collaborators can share the worksheet across Snowflake accounts.

3. Which Snowflake feature allows users to encapsulate a series of SQL statements into a reusable database object, facilitating modular code development?

A) Materialized view
B) Common Table Expressions (CTE)
C) User-Defined Function (UDF)
D) Stored procedure

4. A Data Analyst wants to use pandas code they have previously written to process a column of text and return multiple rows of parsed output for each input value. The Analyst wants to be able to join these results with other tables in a single transaction. Which type of extensibility feature should the Analyst use?

A) Vectorized User-Defined Table Function (UDTF)
B) External function
C) User-Defined Function (UDF)
D) Stored procedure

5. A Data Analyst for a ride-sharing company needs to assess the relationship between the number of active drivers in a city, and the average waiting time for passengers. Which query will determine if an increase in the number of active drivers is associated with a decrease in the average waiting time?

A) SELECT CITY, VARIANCE(ACTIVE_DRIVERS, AVERAGE_WAITING_TIME) FROM
RIDE_DATA GROUP BY CITY;
B) SELECT CITY, CORR(ACTIVE_DRIVERS, AVERAGE_WAITING_TIME) FROM RIDE_DATA GROUP BY CITY;
C) SELECT CITY, SUM(ACTIVE_DRIVERS), VARIANCE(AVERAGE_WAITING_TIME) FROM RIDE_DATA GROUP BY CITY;
D) SELECT CITY, SUM(ACTIVE_DRIVERS), AVG(AVERAGE_WAITING_TIME) FROM
RIDE_DATA GROUP BY CITY;

Solutions: