Snowflake SPS-C01 - Snowflake Certified SnowPro Specialty - Snowpark

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Snowflake SPS-C01 Exam Syllabus Topics:

Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

Question 1

You are developing a Snowpark Python application that reads a large dataset (1 TB) from a Snowflake table 'TRANSACTIONS and performs complex aggregations. The application is experiencing significant performance issues, with query execution taking several hours. You have already verified that the warehouse size is appropriate and caching is enabled. You suspect the issue might be related to data skew and incorrect partitioning. Which of the following strategies would be MOST effective in identifying and mitigating this performance bottleneck?

A. Use to force a broadcast join, assuming the aggregated data is small enough to fit in memory. Monitor query profiles to confirm the broadcast occurs.
B. Analyze the 'TRANSACTIONS' table's data distribution using and histograms on the join keys. Based on the analysis, use with the most skewed column to redistribute the data more evenly. Also, consider using bucketing if appropriate.
C. Use partition_expression=sf.rand())' to randomly repartition the DataFrame into 100 partitions, regardless of the data distribution in the ' TRANSACTIONS table.
D. Increase the Snowflake warehouse size to the largest available option (e.g., X6-Large) to provide more resources for query execution, without analyzing data distribution.
E. Implement caching using after reading the data from the 'TRANSACTIONS' table and before performing any aggregations.

Question 2

You have a Snowpark DataFrame named containing customer data. You want to calculate the average order value for each customer and rank them based on this average. The 'customer_df contains columns 'customer_id' and 'order_value'. Which of the following Snowpark code snippets correctly performs this task, returning a new DataFrame with 'customer_id', 'average_order_value', and 'rank' columns?

A.

B.

C.

D.

E.

Question 3

You have a Snowpark DataFrame named 'sales df that contains daily sales data'. You need to calculate the weekly sales for each product and store the results in a new DataFrame. The calculation of weekly sales involves a window function that is computationally expensive. To optimize performance, you decide to cache the DataFrame after applying the window function. However, after implementing the caching, you notice that the performance is not improved as expected. What could be the reason for this and how can you fix it?

A. The call is placed before the window function. Move the call after applying the window function.
B. Snowflake automatically optimizes window function calculations, rendering explicit caching unnecessary.
C. The DataFrame is being evicted from the cache due to memory pressure. Increase the warehouse size or reduce the data being processed.
D. The DataFrame is too small. Caching only benefits large DataFrames.
E. The window function is not cacheable. Window functions cannot be cached using 'cache_result()'.

Question 4

You are developing a Snowpark application to ingest a large dataset into Snowflake. You have a DataFrame with a schema that matches the target table 'TARGET TABLE. Due to network constraints, you need to optimize the insertion process to minimize the number of API calls. Which of the following approaches would provide the MOST efficient way to insert the data?

A. Load the DataFrame into chunks of 1000 records into target table by using insert_into() function iteratively
B. Convert 'data_df to a Pandas DataFrame using and then use to insert the data.
C. Iterate through the rows of 'data_df , constructing and executing a separate INSERT statement for each row using 'session.sql()'.
D. Stage 'data_df to an internal stage, then load the data from stage to the table using COPY INTO command.
E. Directly use the method, without any intermediate transformations.

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 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.
B. 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/')'.
C. 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'
D. 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}/')'
E. Using scala user defined function (UDF) for write dataframe into stage in avro file format partitioned by Hour and calling it in snowpark dataframe.

Solutions: