Snowflake Certification SPS-C01

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Snowflake SPS-C01 Exam Syllabus Topics:

Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

Question #1
You have two Snowpark DataFrames, 'dfl' and 'df2, representing customer data'. 'dfl' contains columns 'CUSTOMER ID', 'NAME, and 'EMAIL', while 'df2 contains 'CUSTOMER ID' and 'PURCHASE AMOUNT'. You need to create a new DataFrame that combines the information from both DataFrames but only includes customers who exist in BOTH 'dfl ' and 'df2 and the resulting DataFrame should have columns from both. Which of the following Snowpark DataFrame operations should you use, and what is the correct way to call it?

A.

B.

C.

D.

E.

Question #2
You are developing a Snowpark application to load data into a Snowflake table named 'SALES DATA. The DataFrame 'sales_df contains new sales records. You need to insert these records into 'SALES DATA. Which of the following Snowpark DataFrame methods will efficiently perform this operation, considering potential data type mismatches between the DataFrame and the target table? Assume no explicit schema definition is necessary.

A. Option C
B. Option B
C. Option E
D. Option A
E. Option D

Question #3
You have a Python function that calculates a complex statistical measure on a given row of a DataFrame. You want to apply this function to each row of a Snowpark DataFrame in a distributed manner. Which of the following is the MOST efficient way to achieve this?

A. Use the 'apply' method on the Snowpark DataFrame, passing the Python function as an argument.
B. Create a Pandas UDF (User-Defined Function) using decorator and apply it to the Snowpark DataFrame.
C. Use the "map' method on the Snowpark DataFrame's underlying RDD (Resilient Distributed Dataset) and pass the Python function as an argument.
D. Iterate through the rows of the Snowpark DataFrame and call the Python function on each row individually.
E. Use Snowpark's 'sprocs feature to create stored procedure and call the Python function.

Question #4
You're using Snowpark in Python and need to execute a complex SQL query. The query involves several joins and aggregations, and you want to optimize its performance. You are using "session.sql(query)' to execute the query. Which of the following strategies, applied before executing 'session.sql(query)' , would likely lead to the most significant performance improvement for a very large dataset?

A. Use the method on the DataFrame returned by 'session.sql(queryy.
B. Ensure that the SQL query includes appropriate comments to improve readability.
C. Create a view of the underlying data source instead of directly querying the table.
D. Convert the SQL query into a series of Snowpark DataFrame operations (e.g., 'groupBy()', 'agg()').
E. Reduce the size of the data by filtering the DataFrame returned by 'session.sql(queryy using 'where()' before executing any further operations.

Question #5
You have a requirement to process a large number of JSON files stored in a Snowflake stage 'json_stage'. These JSON files contain complex nested structures. You need to extract specific fields from these files using Snowpark Python and load them into a Snowflake table. You want to use 'SnowflakeFile' to read the JSON files and minimize the amount of data loaded into memory. Select all that apply from the following options to efficiently accomplish this task:

A. Create a UDTF that accepts a "SnowflakeFile' object, uses 'json.loadS to parse the JSON content incrementally, extracts the desired fields, and yields rows for insertion into the target table. Use 'session.read.option('PATTERN', ' to generate the initial DataFrame.
B. Use to directly load all JSON files into a Snowpark DataFrame and then use 'select' with path expressions (e.g., 'col('fieldl .nested_field')) to extract the required fields.
C. Write a Python script that downloads all JSON files from the stage using 'SnowflakeFile.get' , iterates through the downloaded files, parses each file, extracts the fields, and inserts the data into the Snowflake table using the Snowflake Python connector.
D. Create a UDF that accepts a 'SnowflakeFile' object, opens the file, reads the JSON data using 'json.load', extracts the required fields, and returns a JSON string. Create a Snowpark DataFrame using 'session.read.option('PATTERN', ' then call the UDF with the file path, and finally parse the returned JSON string to load data.
E. Create a UDF that takes a file path as input, constructs a 'SnowflakeFile' object within the UDF, reads the JSON data using 'json.loadS, extracts the required fields, and returns a Row object or a dictionary. Use 'session.sql('SELECT relative_path FROM to get file paths, create Snowpark Dataframe and then call the UDF on each file path.

Solutions: