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Snowflake SPS-C01 Exam Syllabus Topics:

Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. A Snowpark application needs to manage multiple sessions concurrently, each connected to a different Snowflake warehouse for resource isolation. The application receives warehouse names dynamically at runtime. How can the application efficiently create and manage these sessions while ensuring proper resource cleanup?

A) Create a global session object and use 'session.use_warehouse(warehouse_name)' before each operation. This implicitly switches the warehouse for all operations on that session.
B) Create a single session object with connection pooling enabled and let Snowpark handle warehouse switching automatically based on the queries executed.
C) Create separate sessions for each warehouse, but only close the last session created to avoid disconnecting all connections.
D) Create a separate session object for each warehouse using a loop and store them in a list. Ensure each session is closed using 'session.close()' in a 'finally' block after use or using a context manager.
E) Create a single session and create multiple Snowpark DataFrames, specifying the warehouse in each DataFrame's 'createOrReplaceTempView' method.

2. You have a Snowflake table 'user_profiles' with a VARIANT column 'profile_data'. This column contains JSON objects, and one of the fields within these objects is an array called 'interests'. The 'interests' array contains JSON objects, each with 'name' and 'category' fields. You need to use Snowpark to flatten the 'interests' array and extract the 'name' and 'category' for all user profiles, but only for profiles where the user's 'status' is 'active'. You want to write this in the most efficient way possible. Which of the following code snippets will achieve this?

A)

B)

C)

D)

E)

3. You are working with Snowpark to create a DataFrame from a Python dictionary where keys represent column names and values are lists representing column data'. However, the dictionary contains lists of varying lengths for different columns. You need to create a DataFrame from the Python dictionary but are unsure how to create it. Which approach should you take and why?

A) Transform the dictionary into a list of dictionaries or tuples, padding the short lists with 'None' values. Then, define a schema and use 'session.createDataFrame(data, schema=schema)' to create the DataFrame.
B) Create a Pandas DataFrame from the dictionary first. Pandas handles lists of unequal lengths by filling the shorter lists with NaN. Then, convert the Pandas
C) Manually pad all lists in the dictionary with 'None' values until they have the same length. Then, create the DataFrame using 'session.createDataFrame(data)'.
D) DataFrame to a Snowpark DataFrame using 'session.createDataFrame(pandas_df)'. Snowpark does not support creating DataFrames directly from dictionaries with lists of varying lengths. The code will throw an error. So, manually build the logic of combining the lists.
E) Attempt to create the DataFrame directly using 'session.createDataFrame(data)'. Snowpark will automatically pad the shorter lists with 'NULL' values to match the length of the longest list.

4. You are developing a Snowpark Python application to process streaming data from a Kafka topic, enrich it with data from a Snowflake table, and store the results in another Snowflake table. The enrichment process involves joining the streaming data with a large dimension table in Snowflake. Which of the following Snowpark features would be most efficient and scalable for this use case, considering the continuous nature of the streaming data and the size of the dimension table?

A) Utilizing Snowpark UDFs to fetch data from the dimension table for each row of the streaming data DataFrame.
B) Employing a Snowpark Stored Procedure that periodically refreshes a smaller, aggregated version of the dimension table and using that for joining with the streaming data.
C) Leveraging a dynamic table to incrementally update the join of the streaming data and dimension table.
D) Creating a Snowflake materialized view that pre-joins the streaming data (landing in a table) with the dimension table and using Snowpark to query the materialized view.
E) Using a standard Snowpark DataFrame join operation with a broadcast hint on the smaller streaming data DataFrame.

5. A data engineer is tasked with creating a Snowpark Python application that needs to access data from multiple Snowflake accounts and regions. All accounts are using Snowflake's Business Critical edition. Which of the following approaches would be the MOST efficient and maintainable for managing and switching between different Snowpark sessions in this scenario?

A) Create a configuration file (e.g., YAML or JSON) that stores the account identifiers and other connection details for each Snowflake account and region. Load this configuration and create separate Snowpark session objects for each account, storing them in a dictionary or list.
B) Use a single Snowpark session object and dynamically update the connection parameters (account identifier, username, password) whenever switching to a different account and region.
C) Create a new Anaconda environment for each Snowflake account and install the necessary packages in each environment.
D) Create separate Python scripts for each account and region, hardcoding the account identifiers and credentials within each script.
E) Use the 'snowflake.connector.connect' method to directly establish connections without using the Snowpark Session object.

Solutions: