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Snowflake SnowPro Advanced: Data Analyst Certification Sample Questions:

1. A Data Analyst needs a sample of 10 rows from a table FCT_SALES that has billions of rows. Which commands can be used to accomplish this? (Select TWO).

A) SELECT * FROM FCT_SALES SAMPLE (10 ROWS);
B) SELECT * FROM FCT_SALES SAMPLE 10;
C) SELECT TOP 10 ROWS * FROM FCT_SALES;
D) SELECT TOP (10 ROWS) * FROM FCT_SALES;
E) SELECT TOP 10 * FROM FCT_SALES;

2. There are two similarly-structured and sized tables, Table_a and Table_b, in a schema with data populated in both tables. A Data Analyst is running queries as part of a preliminary analysis of the data to check the MAX value of a numeric column named num which is present in both the tables:
* Query 1: SELECT MAX(num) FROM Table_a;
* Query 2: SELECT MAX(num) FROM Table_b;
After running the queries, the Analyst observed that Query 2 ran significantly slower than Query 1. Why is this occurring?

A) Table_b has more rows than Table_a.
B) A multi-cluster warehouse was used to run Query 1.
C) The USE_CACHED_RESULT was set to FALSE before running Query 2.
D) Table_b has a row-access policy defined.

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

4. The following code is run:

Then this statement is executed:

What will be the output of this statement?

A)

B)

C)

D)

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

Solutions: