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Snowflake SnowPro Advanced: Data Analyst Certification Sample Questions:

1. A Data Analyst created a model called modelX using SNOWFLAKE.ML.FORECAST. The Analyst needs to predict the next few values and save the result directly into tableX. What step does the Analyst need to take after calling the modelX!FORECAST function?

A) List the cache content, then use the data saved in the RESULT_SCAN for tableX.
B) Pass the new table as a function argument.
C) Create the table by querying the RESULT_SCAN.
D) Load the function call results directly INTO tableX.

2. 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) Cross join
D) Full outer join

3. A scorecard tile on a Snowsight dashboard shows a comparison between the industry average employee age (which is 34) and a company's average employee age (which is 38). The scorecard tile looks like this:
Comparison with industry average

How should this tile be interpreted?

A) 38 is the value and 12% is the percent difference from the comparison.
B) 38 is the comparison and 12% is the projected industry growth.
C) 38 is the value and 12% is the standard deviation.
D) 38 is the comparison and 12% is the average age change in the last year.

4. A single variant data column table RAW_SOURCE has the following JSON records:

A Data Analyst needs to get the value of the "f" field and have it in a consumable, tabular format. Which query should be used to meet this requirement?

A) select value:f::number from raw_source, lateral flatten( input => data );
B) select src.events:f::number from raw_source src;
C) select value:f::number from raw_source, lateral flatten( input => data:events );
D) select data:events:f::number from raw_source;

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

Solutions: