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Snowflake DAA-C01 Exam Syllabus Topics:

Snowflake SnowPro Advanced: Data Analyst Certification Sample Questions:

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

2. A Data Analyst is given access to a table of inventory transfers which includes the product details, fulfillment warehouse location, and destination warehouse location. Which type of visualization should be used to illustrate the most active product transfer routes?

A) Line chart
B) Scorecard
C) Scatterplot
D) Heat grid

3. A Data Analyst has created a custom filter called branch_region on a Snowflake dashboard. The filter contains a list of regions where the company has stores. How should the filter be referenced in dashboard queries?

A) ::branch_region
B) %branch_region
C) :branch_region
D) $branch_region

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

5. A large, complicated query is used to generate a data set for a report on the most recent month. It is taking longer than expected. A review of the Query Profile shows excessive spilling. How can the performance of the query be improved WITHOUT increasing costs?

A) Create a materialized view clustered on a date column, on the table that is causing the spilling.
B) Change the source tables into external tables to establish and take advantage of custom partitioning.
C) Run the query against zero-copy clones of the source tables to avoid contention with other queries.
D) Split the query into multiple steps, replacing Common Table Expressions (CTEs) with temporary tables to process the data in smaller batches.

Solutions: