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Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. You are developing a Snowpark Python application and want to write unit tests for your custom UDFs using pytest. Consider the following UDF:

Which of the following pytest tests are correctly written and effectively test the UDF, including handling potential exceptions? (Select all that apply)

A)

B)

C)

D)

E)

2. Consider the following Snowpark code snippet:

Which of the following statements are TRUE regarding the execution and performance of this code?

A) The 'count? operation will use the cached results of and apply an additional filter on the cached data.
B) Removing 'cached_df = line would significantly improve the overall performance because caching always adds overhead.
C) The 'filter' operation Ccol('column_a') > 100') will be executed only once because 'cached_df stores the materialized result.
D) The 'countl' operation will trigger the materialization and caching of 'filtered_df.
E) The 'filter' operation Ccol('column_a') > 100') will be executed twice.

3. You are optimizing a Snowpark application that performs complex data transformations on a large dataset. The transformation involves multiple joins and aggregations. You notice that the query execution time is excessive. Which of the following techniques would be MOST effective in improving the performance of this application, assuming you have the appropriate Snowflake role and privileges?

A) Use the ' DataFrame.explain(Y method to analyze the query plan and identify potential bottlenecks. Optimize the code based on the query plan analysis, focusing on reducing data shuffling and improving join strategies.
B) Rewrite the Snowpark code to use more UDFs (User-Defined Functions). UDFs are always more efficient than built-in functions.
C) Reduce the overall complexity of the query, but increase the number of queries being executed. This allows for a more incremental approach.
D) Increase the size of the Snowflake warehouse used for the computation. A larger warehouse provides more resources for parallel processing.
E) Disable result caching for the Snowflake session. This ensures that the most up-to-date data is always used, even if it slows down performance.

4. You are working with a Snowpark DataFrame that contains product information including 'product_name' and 'description'. You need to create a new column named 'search_terms' that contains the first three words from the 'description' column, converted to lowercase. If the description has fewer than three words, the 'search_terms' column should contain all the words available. The words should be separated by a space. What is the MOST efficient way to achieve this using Snowpark?

A)

B)

C)

D)

E)

5. You are developing a Snowpark application that utilizes a DataFrame named 'transactions df containing transactional data. You need to apply a series of complex transformations, including window functions and joins with other DataFrames. To optimize performance and manage resources effectively, you want to control how Snowpark executes these operations within Snowflake. Which of the following actions or configurations would have the MOST significant impact on controlling the execution plan and resource utilization of your Snowpark application?

A) Explicitly cache the 'transactions_df DataFrame using before applying any transformations. This forces Snowpark to materialize the DataFrame in memory.
B) Specify the 'num_partitionS parameter when creating or transforming the 'transactions_df DataFrame. This controls the number of partitions used for parallel processing.
C) Configure the 'net.snowflake.snowpark.use_native_execution' parameter to 'true' at the session level. This forces Snowpark to translate DataFrame operations into native Snowflake SQL queries.
D) Use the 'DataFrame.explain()' method to analyze the generated SQL query plan before executing the transformations. Then, manually optimize the code based on the query plan output.
E) Implement iterative algorithms within your Snowpark application using imperative Python loops instead of declarative DataFrame operations. This provides finer-grained control over the execution flow.

Solutions: