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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 process sensor data'. You need to define a UDF that converts temperature readings from Celsius to Fahrenheit. However, the conversion formula is computationally intensive and requires access to a pre-trained machine learning model stored as a resource in a stage. Given the following considerations, what is the most efficient and correct way to define this UDF? The model file is named 'temperature_model.pkl'.

A) All of the above options will work.
B)

C)

D)

E)

2. You have a Snowpark DataFrame 'df containing customer data with columns 'customer_id', 'signup_date' (TIMESTAMP NTZ), and 'country'. You need to create a new DataFrame that calculates the number of days since each customer signed up, but only for customers in 'USA' and 'Canada'. Furthermore, you want to filter out records where the signup was more than 365 days ago. Which of the following Snowpark code snippets will achieve this most efficiently?

A)

B)

C)

D)

E)

3. A Snowpark application is designed to process data residing in a Snowflake table called 'ORDERS'. The application needs to create a temporary view named 'TEMP ORDERS VIEW based on a filtered subset of this table. The view should only be accessible within the current Snowpark session and should be automatically dropped when the session ends. What is the MOST efficient and correct Python code snippet using Snowpark to achieve this?

A)

B)

C)

D)

E)

4. You have developed a Snowpark application that uses a Python UDF to perform sentiment analysis on text data extracted from JSON files stored in a Snowflake stage. The UDF relies on a large pre-trained machine learning model that is loaded during the UDF initialization. After deploying the application, you observe that the UDF initialization is taking a significant amount of time, causing slow query performance. What are the three MOST effective strategies to optimize the UDF initialization time in this scenario?

A) Use the 'snowflake.snowpark.files.SnowflakeFile' class to load the model directly from the Snowflake stage within the UDF initializer, but only if the model is smaller than 256M
B) Use the 'streamlit' library and its caching capabilities to cache loaded models. The UDF should call the streamlit api to retrieve the already loaded model.
C) Utilize the 'context.add_dependency' method in Snowpark to specify the model file as a dependency. Snowflake will automatically distribute and cache the model file to the worker nodes.
D) Load the model outside the UDF definition within the Snowpark session, pass it as an argument to the UDF, then use the model as part of a vectorized UDF.
E) Use the 'cachetools' library to cache the loaded model within the UDF. This will help to avoid reloading the model every time the UDF is called.

5. You're developing a Snowpark Python application that reads data from a Snowflake table, performs several transformations, and writes the result to another table. You notice that the application throws a 'net.snowflake.snowpark.exceptions.SnowparkClientException: JDBC driver encountered an unexpected error.' intermittently. Examining the Snowflake query history, you observe many queries failing due to 'Warehouse Suspended'. Which of the following strategies would BEST address this issue in a production environment?

A) Implement retry logic within the Snowpark application using a try-except block and exponential backoff to handle transient connection errors.
B) Use a larger warehouse size to prevent warehouse overload during query execution, which might trigger the auto-suspension.
C) Schedule the Snowpark application to run during off-peak hours when warehouse load is lower and less likely to be suspended.
D) Modify the Snowpark code to explicitly resume the warehouse at the beginning of the application execution and suspend it at the end.
E) Increase the AUTO SUSPEND parameter of the warehouse to a larger value, preventing it from suspending so quickly.

Solutions: