Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. Consider the following Python code snippet using Snowpark:

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

A) The code will fail because password authentication is deprecated and replaced by Key Pair authentication.
B) The code reads data from a table named 'my_table' in Snowflake.
C) The code uses best practices by explicitly closing the Snowflake session, preventing resource leaks.
D) The code establishes a connection to Snowflake using the provided credentials.
E) 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.

2. 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) Increase the warehouse size significantly to provide more resources for the scalar UDF execution.
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) Replace the Python scalar UDF with a Java scalar UDF, as Java typically offers better performance than Python in Snowflake.
E) Rewrite the UDF logic directly in SQL, leveraging Snowflake's built-in functions for statistical calculations.

3. You are developing a Snowpark application in Python to perform sentiment analysis on customer reviews stored in a Snowflake table named 'CUSTOMER_REVIEWS. The table has columns 'REVIEW ONT), 'REVIEW TEXT (VARCHAR), and 'SENTIMENT SCORE (FLOAT). You want to define a UDF using Snowpark that leverages a pre-trained sentiment analysis model from the 'nltk' library (already uploaded to a stage). The UDF should take 'REVIEW TEXT' as input and return the sentiment score. Which of the following code snippets will correctly define and register the UDF, ensuring it's accessible for use in Snowpark DataFrames, taking into account potential serialization issues with 'nltk' models?

A)

B)

C)

D)

E)

4. You have a Snowpark Python application that reads data from a Snowflake table named 'SALES DATA', performs several transformations using DataFrames, and then writes the results back to a new table named 'AGGREGATED SALES'. The application runs successfully, but you notice that the write operation to 'AGGREGATED SALES' is consistently slow. After examining the query profile, you observe significant skew in the data being written, causing some worker nodes to be overloaded. Which of the following techniques could you use within your Snowpark application to mitigate the data skew and improve the write performance to 'AGGREGATED SALES'?

A) Use the method to specify a clustering key on the 'AGGREGATED SALES' table during table creation. This will physically organize the data on disk based on the skew key, improving write performance.
B) Use the 'DataFrame.sort(col)' method to sort the data by the skew key before writing it to 'AGGREGATED SALES'. This will ensure that rows with similar values are processed by the same worker node.
C) Implement custom partitioning logic using a User-Defined Function (UDF) that calculates a hash value based on the skew key and then uses the 'DataFrame.repartitionByRange(col)' method to partition the data based on the hash values.
D) Use the method to evenly redistribute the data across a larger number of partitions before writing it to 'AGGREGATED SALES'.
E) Increase the size of the Snowflake warehouse being used to execute the Snowpark application. This will provide more compute resources to handle the data skew.

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

Solutions: