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Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. You have a Snowpark Python application that uses a UDF to perform custom data transformations. The UDF relies on a large, read-only lookup table that is stored as a CSV file on a Snowflake stage. Which of the following strategies would be the MOST efficient way to access the lookup table within the UDF?

A) Read the CSV file from the stage every time the UDF is called using 'snowflake.connector.connect()' and then load the data into a Pandas DataFrame within the UDF function.
B) Read the CSV file from the stage once when the UDF is first called, cache the data in a global variable within the UDF module, and then reuse the cached data for subsequent calls.
C) Load the CSV file into a Snowflake stage, and in the python UDF code, use the get_stage_file API from session object to read the file once. Then the data cached in-memory within the UDF module, and reuse the cached data for subsequent calls.
D) Use the 'cachetoolS library with a Least Recently Used (LRU) cache to store the lookup table in memory. The UDF will check the cache before reading the CSV file, and update the cache if necessary. The CSV file is read with get_stage_file API from session.
E) Load the CSV file into a Snowflake table and then query the table from within the UDF using 'session.sql(V.

2. You are working with Snowpark and need to persist the results of a DataFrame 'df to a Snowflake stage named 'my_stage'. You want to achieve the following: 1. Write the data in JSON format. 2. Use snappy compression. 3. Handle potential write errors gracefully. 4. Overwrite any existing files with the same name. Which of the following approaches can achieve these requirements? (Select all that apply)

A) Wrap the entire write operation in a try-except block and implement retry logic with exponential backoff in case of transient errors.
B) Use compression='snappy', mode='overwrite')' and handle potential exceptions using a 'try-except' block.
C) Configure the stage 'my_stage' with FILE_FORMAT = (TYPE = 'JSON', COMPRESSION = 'SNAPPY') and then use within a 'try-except' block.
D) Define a UDF to write the dataframe into stage along with exception handling logic.
E) Use 'df.write.option('compression', inside a 'try-except block.

3. You are developing a Snowpark Python application to process large datasets stored in a Snowflake table called 'CUSTOMER DATA' The application needs to perform complex data transformations and aggregations that benefit from Snowpark's lazy evaluation and query optimization. Which of the following approaches will lead to the MOST efficient execution in terms of resource utilization and performance?

A) Create a Snowpark DataFrame from the "CUSTOMER_DATX table using 'session.table('CUSTOMER DATA')', perform all the transformations and aggregations using Snowpark DataFrame API, and materialize the final result into a new Snowflake table using .
B) Execute a series of individual SQL queries using 'session.sql()' to perform the transformations and aggregations, and then combine the results into a final Pandas DataFrame for further processing before loading it back into Snowflake.
C) Load the data into a temporary table, then create a Stored Procedure in SQL to perform transformations, and call the procedure from the Snowpark Python application.
D) Fetch all the data from 'CUSTOMER_DATR into a Pandas DataFrame using , perform the transformations in Pandas, and then load the results back into a new Snowflake table using 'session.write_pandas()'.
E) Use 'session.table('CUSTOMER to fetch all the data into a list of Row objects in Python, perform transformations using standard Python loops and data structures, and then write the processed data to a new Snowflake table using 'session.createDataFrame(V.

4. You have JSON files stored in an internal stage named 'json_stage' within your Snowflake account. Each JSON file contains an array of product objects, with potentially nested structures. You need to create a Snowpark DataFrame to analyze this data, but the schema is complex and you want to avoid explicitly defining it in your Python code. Which of the following Snowpark code snippets will MOST effectively achieve this, assuming you have a Snowpark session object named 'session'?

A)

B)

C)

D)

E)

5. You have a Snowpark DataFrame named 'products' with columns 'product_id' (INT), 'product_name' (STRING), and 'price' (DOUBLE). You want to apply a transformation to calculate a 'discounted_price' column, which is the 'price' reduced by 10% if the price is greater than $100.00. Which of the following code snippets is the most efficient way to achieve this using Snowpark Python?

A)

B)

C)

D)

E)

Solutions: