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Snowflake SnowPro Advanced: Data Analyst Certification Sample Questions:

1. A Data Analyst executes a query in a Snowflake worksheet that returns the total number of daily sales, and the total amount for each sale. How can the Analyst check the distribution of the total amount, without running the query again?

A) Call the WIDTH_BUCKET function.
B) Go to Chart and select a histogram that includes the two variables.
C) Click on the column header in the results and review the histogram.
D) Go to Chart and select a bar chart that contains the two variables.

2. A Data Analyst has been asked to predict sales revenue through the end of the year. Which function will provide this information?

A) CORR
B) COVAR_SAMP
C) REGR_SLOPE
D) VARIANCE

3. This command was executed:
SQL
SELECT seq4(), uniform(1, 10, RANDOM(12))
FROM TABLE(GENERATOR(TIMELIMIT => NULL))
ORDER BY 1;
How many rows will be generated?

A) 0
B) An infinite number
C) 12
D) 10

4. A company is looking for new headquarters and wants to minimize the distances employees have to commute.
The company has geographic data on employees' residences. Through the Snowflake Marketplace, the company obtained geographic data for possible locations of the new headquarters. How can the distance between an employee's residence and potential headquarters locations be calculated in meters with the LEAST operational overhead?

A) ST_DISTANCE
B) ST_LENGTH
C) ST_HAUSDORFFDISTANCE
D) HAVERSINE

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, SUM(ACTIVE_DRIVERS), AVG(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, 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: