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Snowflake DAA-C01 Exam Syllabus Topics:

Snowflake SnowPro Advanced: Data Analyst Certification Sample Questions:

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

2. A Data Analyst needs to generate a graphic that will identify any natural clusters in a data set containing details about completed orders. Which Snowsight chart should be used?

A) Heat grid
B) Scatter plot
C) Bar chart
D) Line chart

3. How can a Data Analyst automatically create a table structure for loading a Parquet file?

A) Use INFER_SCHEMA together with the CREATE TABLE USING TEMPLATE command.
B) Use the INFER_SCHEMA together with the CREATE TABLE LIKE command.
C) Use the GENERATE_COLUMN_DESCRIPTION with the CREATE TABLE LIKE command.
D) Use the GENERATE_COLUMN_DESCRIPTION with the CREATE TABLE USING TEMPLATE command.

4. A Data Analyst has a very large table with columns that contain country and city names. Which query will provide a very quick estimate of the total number of different values of these two columns?

A) SELECT DISTINCT COUNT(country, city) FROM TABLE1;
B) SELECT COUNT(DISTINCT country, city) FROM TABLE1;
C) SELECT HLL(country, city) FROM TABLE1;
D) SELECT COUNT(country, city) FROM TABLE1;

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

Solutions: