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Snowflake SPS-C01 Exam Syllabus Topics:

Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

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

2. Consider the following Python code snippet using Snowpark:

Which of the following statements are true regarding this Snowpark code?

A) The code uses best practices by explicitly closing the Snowflake session, preventing resource leaks.
B) The code reads data from a table named 'my_table' in Snowflake.
C) The code will fail because password authentication is deprecated and replaced by Key Pair authentication.
D) The code calculates the sum of the 'sales' column, grouped by 'category' , and saves the result to a new table named , overwriting it if it exists.
E) The code establishes a connection to Snowflake using the provided credentials.

3. You have a Snowpark application that processes sensor data from a large number of devices. You've implemented a scalar UDF in Python to calculate a complex statistical metric for each sensor reading. Initial tests show poor performance. Which of the following strategies would be MOST effective in improving the performance of this application, considering the nature of the computation and the data volume?

A) Replace the Python scalar UDF with a Java scalar UDF, as Java typically offers better performance than Python in Snowflake.
B) Implement caching for the UDF results, assuming the statistical metric calculations are frequently repeated for the same sensor readings.
C) Replace the Python scalar UDF with a Python vectorized UDF, ensuring the UDF processes data in batches instead of row-by-row.
D) Rewrite the UDF logic directly in SQL, leveraging Snowflake's built-in functions for statistical calculations.
E) Increase the warehouse size significantly to provide more resources for the scalar UDF execution.

4. You are building a Snowpark application to process sensitive data'. To enhance security, you want to leverage ephemeral sessions. Which configurations, passed to 'snowpark.Session.builder.configS , are required and sufficient to create an ephemeral session? Assume your Snowflake environment is properly configured to allow ephemeral sessions.

A) Setting the 'role' parameter to a role with appropriate privileges and 'warehouse'.
B) Setting the 'role' parameter to a role with appropriate privileges, 'database', 'schema', and 'warehouse'.
C) Ephemeral sessions do not have specific configurations; they are automatically created by Snowflake when the application connects.
D) Setting the 'role' parameter to a role with appropriate privileges and 'database' and 'schema'.
E) Only setting the 'role' parameter to a role with appropriate privileges.

5. You've created a Snowpark Python stored procedure designed to perform sentiment analysis on customer reviews stored in a Snowflake table. This procedure utilizes a third-party Python library, 'transformers', for its sentiment analysis model. You need to operationalize this stored procedure for scheduled execution. Which of the following options represents the MOST efficient and reliable approach for deploying and managing the 'transformers' dependency within the Snowflake environment for your stored procedure, minimizing deployment complexity and potential runtime errors?

A) Create a Snowflake Anaconda channel, upload the 'transformers' library to this channel, and specify the channel in the stored procedure definition using the packageS parameter.
B) Include the 'transformers' library as a part of the task definition and make it available for the stored procedure that the task is invoking.
C) Upload the 'transformers' library as a zip file to a Snowflake stage and reference it in the 'imports' parameter when creating the stored procedure.
D) Install the 'transformers' library on the Snowflake compute warehouse nodes directly using a startup script.
E) Include the 'transformers' library directly within the stored procedure's Python code as a string literal and execute it using or 'eval()'.

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