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Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. You have a Snowpark Python stored procedure that reads data from a Snowflake table, performs a complex calculation using Pandas, and then writes the results back to another Snowflake table. You are experiencing performance issues, and you suspect the data transfer between Snowpark and Pandas is a bottleneck. Which of the following techniques could significantly improve the performance of this stored procedure? (Select two)

A) Increase the warehouse size to the largest possible value before executing the stored procedure.
B) Leverage Snowpark's optimized functions and UDFs wherever possible to perform transformations within Snowflake's engine instead of transferring data to Pandas.
C) Use the 'TABLE function in Snowpark to directly access the source table instead of reading the entire table into a Snowpark DataFrame at once.
D) Convert the Pandas DataFrame to a Dask DataFrame for distributed computation.
E) Utilize vectorized operations within Pandas to minimize explicit looping and improve calculation speed.

2. You are developing a Snowpark Python UDF to perform sentiment analysis on product reviews. The UDF takes a text review (STRING) as input and returns a sentiment score (FLOAT). You want to operationalize this UDF, ensuring type safety and performance. Which of the following approaches is MOST recommended, considering both ease of use and explicit type declaration?

A) Using only the function name without any explicit type declaration or registration.
B)

C) Using gudf(return_type=FloatType(), decorator with explicit data type registration.
D) Using Python type hints alone: 'def sentiment_score(review: str) -> float: .... and relying on Snowpark to infer types.
E) Casting the result of the UDF to FLOAT within the UDF definition.

3. You have a Snowpark Python application that performs several data transformations on a DataFrame representing customer transactions. The application is experiencing performance issues, and you suspect that some transformations are unnecessarily expensive. Which of the following techniques can MOST effectively optimize the performance of your Snowpark application, specifically focusing on minimizing data movement and leveraging Snowflake's query optimization capabilities?

A) Explicitly call .cache()' on the DataFrame after each transformation to materialize intermediate results in memory.
B) Leverage Snowpark's built-in DataFrame transformations (e.g., .groupBy()') to allow Snowflake to optimize the query execution plan. Avoid pulling large amounts of data into the client application for simple operations. Only call 'collect()' as the last and final option, as this is the most costly activity of all.
C) Always use the largest available Snowflake warehouse size to ensure sufficient compute resources.
D) Use User-Defined Functions (UDFs) written in Python for all transformations, regardless of their complexity.
E) Take the dataframe to Pandas dataframe as soon as possible in between transformations, since Pandas dataframes will be faster.

4. Consider a DataFrame 'products df loaded from a SnoMlake table. It contains a 'features' column of type VARIANT, where each row contains a JSON object representing product features. Your task is to create a new DataFrame where each feature becomes a separate column. You need to dynamically extract these features without knowing the specific feature names in advance. Which of the following approaches could achieve this using Snowpark, and what considerations are important? Choose all that apply:

A) Use the 'FLATTEN' function within a Snowpark DataFrame transformation. This allows you to transform the key-value pairs within the VARIANT column into separate rows, which can then be pivoted to create new columns.
B) It's not possible to dynamically extract feature names and create columns in Snowpark without knowing the schema in advance.
C) The function can be used in conjunction with a Snowpark SQL query to dynamically extract the json into separate columns.
D) Use the function on the VARIANT column to get an array of feature names. Then, use a loop to iterate over this array and dynamically create new columns using bracket notation (e.g.,
E) Use a User-Defined Function (UDF) to parse the JSON and return a dictionary. Then, use a loop to iterate over the keys in the dictionary and create new columns based on these keys.

5. You're using Snowpark in Python and need to execute a complex SQL query. The query involves several joins and aggregations, and you want to optimize its performance. You are using "session.sql(query)' to execute the query. Which of the following strategies, applied before executing 'session.sql(query)' , would likely lead to the most significant performance improvement for a very large dataset?

A) Convert the SQL query into a series of Snowpark DataFrame operations (e.g., 'groupBy()', 'agg()').
B) Use the method on the DataFrame returned by 'session.sql(queryy.
C) Reduce the size of the data by filtering the DataFrame returned by 'session.sql(queryy using 'where()' before executing any further operations.
D) Ensure that the SQL query includes appropriate comments to improve readability.
E) Create a view of the underlying data source instead of directly querying the table.

Solutions: