Snowflake SPS-C01 real exam prep : Snowflake Certified SnowPro Specialty - Snowpark

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Snowflake SPS-C01 Exam Syllabus Topics:

Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. 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) 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.
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) 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.
D) 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.
E) 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.

2. You have a Snowpark DataFrame named 'employee_df with columns 'employee_id', 'department', and 'salary'. You want to calculate the average salary for each department and add it as a new column named 'avg_department_salary' to the original DataFrame. Additionally, you want to sort the resulting DataFrame by department and then by salary in descending order. Which of the following Snowpark code snippets correctly implements this requirement?

A)

B)

C)

D)

E)

3. You are tasked with creating a Snowpark stored procedure that needs to access a secret stored in Snowflake's Secret Managen The secret contains credentials required to connect to an external API. Which of the following steps are necessary to correctly and securely access and use the secret within your Snowpark stored procedure? (Select all that apply)

A) Ensure that the stored procedure is created with the 'EXECUTE AS CALLER clause.
B) Use the method within the stored procedure to retrieve the secret value.
C) Create a UDF that exposes the secret and call that UDF in the stored procedure.
D) Store the secret value directly in the stored procedure's code as a global variable.
E) Grant the USAGE privilege on the secret to the role that will execute the stored procedure.

4. You've transformed a large Snowpark DataFrame and want to persist it to a Snowflake stage for downstream applications. Your requirements are: 1. The data must be written in CSV format. 2. The files must be GZIP compressed. 3. A header row should be included in each file. 4. The files should be stored in a stage named 'customer_stage' in your Snowflake database. Which of the following code snippets correctly implements this, ensuring optimal performance and resource utilization?

A) Option A
B) Option C
C) Option D
D) Option E
E) Option B

5. You are developing a Snowpark Python application that performs advanced machine learning model training on a large dataset stored in Snowflake. You observe that the application is memory-intensive, causing frequent spilling to disk and slowing down the training process.
Which of the following strategies, when implemented in conjunction, is MOST likely to improve the performance of your Snowpark application in this memory-constrained scenario?

A) Increase the 'MAX CONCURRENCY LEVEL' parameter at the session level and rewrite the model training code in SQL using stored procedures.
B) Reduce the batch size for data loading, utilize Snowpark's optimized data types for columns with small value ranges, and use memory profiling tools to identify memory leaks in your Python code and optimize the application.
C) Increase the size of the Snowpark-optimized warehouse with 'MEMORY OPTIMIZED server type, use appropriate data types to minimize memory footprint, and optimize UDFs to minimize intermediate data creation.
D) Enable caching for intermediate results and switch to a Snowpark-optimized warehouse with a smaller size to force early spilling to disk.
E) Switch to a larger Snowflake warehouse size and increase the parameter to ' True'.

Solutions: