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Snowflake SPS-C01 Exam Syllabus Topics:

Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

Question #1
You are developing a data pipeline using Snowpark and want to optimize the execution of multiple DataFrame transformations. Which of the following strategies or techniques can you employ to improve performance and reduce execution time? (Select all that apply)

A. Eagerly evaluating all DataFrame transformations using 'df.collect()' after each transformation to materialize the intermediate results.
B. Leveraging Snowflake's caching mechanisms by using the 'CACHE RESULT clause after complex or frequently used queries.
C. Using on intermediate DataFrames that are reused multiple times in subsequent transformations.
D. Using pushdown optimization by writing UDFs in Scala and ensuring filter operations are applied as early as possible in the data processing pipeline.
E. Using to explicitly define the order in which DataFrames should be processed.

Question #2
You have a Python UDTF that calculates a running average from a stream of numerical data'. The UDTF's 'process' method maintains state (the running sum and count) between calls. You need to ensure that the UDTF's state is properly initialized for each new group of data processed within a Snowpark DataFrame. What are the requirements?

A. The UDTF class must define a 'reset' method. This method will be called by Snowpark at the beginning of processing each group of rows.
B. The UDTF class must define a 'end_partition' method to finalize processing and avoid memory leaks.
C. The UDTF class must have an '__init____' method to initialize the state variables. This '___init____' method will be called once per UDTF instance.
D. The UDTF class must define a '___init___' method to initialize the state variables and also 'reset method. This '___init____' and 'reset methods will be called once per UDTF instance.
E. The UDTF class must define a '___del___' method. This method will be called by Snowpark at the beginning of processing each group of rows.

Question #3
You are building a Snowpark application that uses a Python UDF to perform sentiment analysis on customer reviews. The UDF relies on a large pre-trained machine learning model loaded from a file. During execution, you encounter 'Out of Memory' errors within the UDF. Considering the constraints of the Snowpark execution environment and the need to optimize resource usage, which of the following steps is the MOST effective in addressing this issue and ensuring the application's stability and performance?

A. Optimize the model itself by reducing it's size through quantization or distillation, and re-upload the smaller model to the Snowflake stage for the UDF to use.
B. Increase the overall size of the Snowflake warehouse to provide more memory for UDF execution. The Snowflake environment will automatically allocate more memory to UDFs when available.
C. Implement lazy loading of the machine learning model within the UDF, ensuring that the model is loaded only when it's first needed, and then cached for subsequent calls within the same UDF invocation.
D. Use Snowpark's 'sproc' to register the UDF as a stored procedure instead ofa UDF, as stored procedures typically have more memory allocated to them.
E. Break down the customer reviews into smaller chunks and process them in batches within the UDF, clearing the model from memory after each batch to reduce overall memory consumption.

Question #4
You have a Snowpark DataFrame with columns 'product_id', 'customer_id', and 'sale_amount'. Some values are negative, indicating returns, and others are null. You need to replace negative values with 0 and fill null values with the average 'sale_amount' for each 'product_id'. Which of the following approaches is the MOST efficient and correct way to achieve this using Snowpark?

A.

B.

C.

D.

E.

Question #5
You are tasked with processing a large number of PDF files stored in an external stage named Each PDF contains scanned receipts, and you need to extract the total amount from each receipt. You plan to use Snowpark Python, SnowflakeFile object, and an OCR (Optical Character Recognition) library for text extraction. Assuming you have already set up the connection and session, what is the most efficient and secure way to read and process these PDF files using Snowpark and SnowflakeFile, minimizing data transfer and maximizing parallelism?

A. List all files in the stage using 'session.sql('LlST iterate through the results, create a 'SnowflakeFile' object for each file path, download each file using 'SnowflakeFile.get' into the client machine, perform OCR, extract the amount, and create a Snowpark DataFrame with the extracted amounts.
B. Read all PDF files into a Pandas DataFrame, then iterate through the DataFrame, downloading each file using 'SnowflakeFile.gef , perform OCR, extract the amount, and finally create a Snowpark DataFrame with the extracted amounts.
C. Create a UDF that accepts a 'SnowflakeFile' object as input, performs OCR and amount extraction inside the UDF, and then call this UDF on a Snowpark DataFrame representing the files in the stage. Use 'session.read.option('PATTERN', ' to generate the initial DataFrame.
D. Use 'session.read.option('PATTERN', ' to load the PDF files as binary data into a Snowpark DataFrame. Then, define a UDF that takes the binary data as input, converts it to a 'SnowflakeFile' object within the UDF, performs OCR, and extracts the amount.
E. Define an external function that takes the path to a file on the stage as input, performs OCR using a cloud-based OCR service and stores the result in an intermediate database table, then create a Snowpark DataFrame to query extracted amounts. Use 'session-read-option('PATTERN', ' to generate the initial DataFrame.

Solutions: