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Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. You are developing a secure UDF in Snowpark Python that needs to access sensitive data stored in an internal stage. The UDF should be accessible to users without granting them direct access to the stage. Which of the following security measures and code snippets are required to achieve this, assuming the stage is already created?

A) Create an external function and grant access to the API integration that provides the security context.
B) Create a UDF and grant USAGE on the stage to the role that owns the UDF.
C) Create a secure UDF and use a stored procedure owned by a role with access to the internal stage to retrieve data, passing the data to the UDF as an argument.
D) Create a secure UDF using the ' VOLATILE keyword, allowing it to access secured data with current user's permissions.
E) Create a secure UDF and use the function to access stage credentials within the UDF's handler function.

2. 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) 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.
B) 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.
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) 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.
E) Load the CSV file into a Snowflake table and then query the table from within the UDF using 'session.sql(V.

3. You're building a Snowpark Python application that processes sensor data from various devices. The data arrives as a stream of JSON objects, each containing the device ID, timestamp, and sensor readings. You want to use a Streamlit application to visualize near real- time aggregates on the data'. You're aiming to create a Snowpark DataFrame from this data, perform transformations, and then serve this DataFrame to Streamlit. Which of the following approaches concerning creating the initial DataFrame from JSON data is generally the MOST efficient and scalable for handling such a stream of data?

A) Utilize Snowpipe with auto-ingest configured to load the JSON data into a raw data Snowflake table, and subsequently, establish a Snowpark DataFrame using 'session.table('raw_data_table')'. You can then apply necessary transformations using Snowpark.
B) Iteratively append each JSON object to a Python list, then create a Snowpark DataFrame from the list using 'session.createDataFrame(list_of_json_objectsy.
C) Write each incoming JSON object to a temporary file in cloud storage (e.g., AWS S3 or Azure Blob Storage) and then periodically use 'session.read.json()' to create a Snowpark DataFrame from the files.
D) Read the JSON data directly from the stream into a Pandas DataFrame using , then convert the Pandas DataFrame to a Snowpark DataFrame using 'session.createDataFrame(pandas_df)'.
E) Use Snowflake's Kafka connector to ingest the JSON data directly into a Snowflake table, and then create a Snowpark DataFrame from that table using 'session.table()'.

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

5. Consider the following Snowpark Python code snippet:

A)

B) This code will ovemrite the table if it already exists.
C) This code will fail because is not a valid method for Snowpark DataFrames.
D) The 'result_df DataFrame will be persisted to the 'AGGREGATED SALES table in the default schema of the user running the code.
E) The code will fail because there is no call to or on the 'result_df dataframe and Snowflake performs lazy evaluation.

Solutions: