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Snowflake SPS-C01 Exam Syllabus Topics:

Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

Question 1

You have a Snowpark DataFrame containing sales data with columns 'region' , and 'sales_amount'. You need to calculate the total sales amount for each region and then filter the results to only include regions where the total sales amount is greater than 10000. Which of the following Snowpark code snippets correctly implements this logic?

A.

B.

C.

D.

E.

Question 2

You have a Snowflake table named 'CUSTOMER DATA' with columns 'CUSTOMER ID', 'NAME, 'CITY , and 'ORDER COUNT. You want to create a Snowpark DataFrame named 'customer_df containing only customers from 'New York' with an 'ORDER COUNT greater than 10. Which of the following code snippets is the MOST efficient and correct way to achieve this, minimizing data transfer and maximizing pushdown optimization to Snowflake?

A.

B.

C.

D.

E.

Question 3

Consider the following Snowpark code snippet designed to process data from an event table and calculate a rolling average:

After deploying this code, you observe that the rolling average calculation is significantly slower than expected, even though the virtual warehouse is adequately sized. What is the MOST effective way to optimize the performance of this rolling average calculation in Snowpark, considering the size of window_size ?

A. Materialize the intermediate 'event_df DataFrame into a temporary table using before applying the window function.
B. Use the SNOWFLAKML.FORECAST package in the Snowpark DataFrame to forecast the rolling average of events in each of the user session.
C. Use the 'rangeBetween' method instead of 'rowsBetween' in the 'Windows specification, as it is generally more efficient for numerical data.
D. Utilize Snowflake's APPROX AVG aggregate function in conjunction with a custom UDF to estimate the rolling average, trading off accuracy for performance.
E. Reduce the 'window_size' parameter. Smaller windows inherently require less computation.

Question 4

You are developing a Snowpark application to process customer reviews. You need to use a third-party sentiment analysis library, 'SentimentAnalyzer', which is NOT available in the Anaconda repository. You have the library JAR file stored in an internal artifact repository accessible via HTTP. Which of the following steps are necessary to make this library available to your Snowpark session?

A. Configure the Snowflake account-level parameter to point to the HTTP location of the JAR file. Then use session.add_import' to use it.
B. Use 'session.add_dependency('/path/to/SentimentAnalyzer.jar')' in your Snowpark Python code after uploading the JAR to an internal stage.
C. Upload the JAR file toa Snowflake stage and register it as a Java UDF using CREATE FUNCTION.
D. Upload the JAR file to a Snowflake stage. Then use 'session.add_import' to make the file available in your Snowpark session.
E. Create a conda environment that includes the JAR, upload it to a stage, and use the environment in Snowpark.

Question 5

You have a Snowpark DataFrame containing sensor data'. You need to write this data to a Snowflake stage 'sensor_stage' , creating a new set of files every hour based on the 'timestamp' column (data type: Timestamp). You also want to ensure that the file names include the hour of the timestamp and are written in Avro format with Zstandard compression. The directory structure on the stage should reflect the hourly partitioning. Which of the following approaches offers the most efficient and scalable way to achieve this, while minimizing the number of files written per hour?

A. Create a new DataFrame by adding an 'hour' column extracted from the 'timestamp' column. Then use 'df.write.partitionBy('hour').format('avro').option('compression', 'zstd').mode('append').save('@sensor_stage/')'.
B. Define a stored procedure that iterates through hourly intervals, filters the DataFrame based on the current hour, and writes the filtered DataFrame to the stage using 'df.write.format('avro').option('compression', 'zstd').mode('append').save(f'@sensor_stage/hour={current_hour}/')'
C. Create a view on top of the data and schedule a task which create file in avro with zstd compression by running the select statment with group by hour.
D. Using scala user defined function (UDF) for write dataframe into stage in avro file format partitioned by Hour and calling it in snowpark dataframe.
E. Write a Python script that connects to Snowflake, retrieves the entire DataFrame, iterates through each row, determines the hour from the 'timestamp', and writes each row to a separate Avro file named after the hour in the 'sensor_stage'

Solutions: