Snowflake Certified SnowPro Specialty - Snowpark : SPS-C01

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Snowflake SPS-C01 Exam Syllabus Topics:

Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. You are developing a Snowpark application to perform complex data transformations on a large dataset stored in Snowflake. You need to optimize the application's performance. Which of the following strategies are MOST effective for improving performance within Snowpark?

A) Leveraging Snowpark's optimized functions and operations whenever possible, even if it requires rewriting some Python code.
B) Materializing intermediate DataFrames aggressively to reduce memory usage.
C) Using user-defined functions (UDFs) written in Python for all transformations, regardless of complexity.
D) Using the 'collect()' method frequently to retrieve small subsets of data to the client for processing.
E) Taking advantage of Snowpark's lazy evaluation by chaining transformations together before triggering execution with an action like 'collect()' or 'write()'.

2. You are building a Snowpark application that processes a large number of PDF files stored in a Snowflake stage. You need to extract text from each PDF file using a Python UDF and store the extracted text in a Snowflake table. You are considering different approaches for loading the PDF files into the UDE Which of the following approaches would provide the BEST performance and scalability, while minimizing network traffic and memory usage?

A) Load the PDF files into a pandas DataFrame within the Snowpark application, then pass the DataFrame to the UDF. This way the PDF can be available to all workers in the dataframe.
B) Download all PDF files from the Snowflake stage to a local directory on the machine running the Snowpark application, then load the files from the local directory into the UDF.
C) Use the 'snowflake.snowpark.functions.read' function in Python to read the PDF files directly from the stage within the UDF. This loads the file contents into a variable available for processing.
D) Pass the file path of each PDF file in the Snowflake stage to the UDF as a string. Within the UDF, use the 'snowflake.snowpark.files.SnowflakeFile' class to open and read the file. This will allow efficient access to the file directly from the stage.
E) Use the 'GET_OBJECT Snowflake SQL command to retrieve each file's contents and then pass the results as arguments into the UDF for processing. This allows use of pure SQL statements to access the files.

3. You're working with Snowpark and have a DataFrame 'df containing a column 'json_data' with JSON strings. Some of these JSON strings are invalid. You need to parse the valid JSON strings and extract a field named 'product_id' from them. Invalid JSON strings should result in a 'NULL' value for the extracted 'product_id'. Which of the following approaches is the MOST robust and efficient way to achieve this?

A)

B)

C)

D)

E)

4. You need to perform a set difference operation between two DataFrames in Snowpark Python. 'dfl' contains customer IDs from a marketing campaign, and 'df2 contains customer IDs from a recent purchase event. You want to identify customers who were targeted in the campaign but did not make a recent purchase. Both DataFrames have a column named 'customer id'. Which of the following approaches provides the most efficient way to accomplish this task in Snowpark?

A)

B)

C)

D)

E)

5. You are optimizing a Snowpark application that performs complex data transformations on a large dataset. The transformation involves multiple joins and aggregations. You notice that the query execution time is excessive. Which of the following techniques would be MOST effective in improving the performance of this application, assuming you have the appropriate Snowflake role and privileges?

A) Increase the size of the Snowflake warehouse used for the computation. A larger warehouse provides more resources for parallel processing.
B) Disable result caching for the Snowflake session. This ensures that the most up-to-date data is always used, even if it slows down performance.
C) Reduce the overall complexity of the query, but increase the number of queries being executed. This allows for a more incremental approach.
D) Rewrite the Snowpark code to use more UDFs (User-Defined Functions). UDFs are always more efficient than built-in functions.
E) Use the ' DataFrame.explain(Y method to analyze the query plan and identify potential bottlenecks. Optimize the code based on the query plan analysis, focusing on reducing data shuffling and improving join strategies.

Solutions: