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Snowflake SPS-C01 Exam Syllabus Topics:

Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. You have a Snowpark DataFrame with columns 'sale_date', 'product_id', and 'revenue'. You need to calculate the cumulative revenue for each product over time. Which of the following approaches will accomplish this in Snowpark using window functions?

A)

B)

C)

D)

E)

2. You are developing a Snowpark Python application that processes streaming data using a dynamic table. The application is experiencing frequent 'net.snowflake.client.jdbc.SnowflakeSQLException: SQL compilation error: Unsupported feature 'Streaming Dynamic Table'. ' errors, even though dynamic tables are enabled in your Snowflake account and the user has the necessary privileges. Which of the following are potential causes and solutions for this error? (Select TWO)

A) The warehouse being used for the Snowpark session is not configured with the feature enabled. Verify that the warehouse configuration includes = TRUE'.
B) The user role lacks the 'EXECUTE MANAGED TASK privilege. Grant this privilege to the user role executing the Snowpark application.
C) The Snowpark Python client version is outdated and does not support streaming dynamic tables. Upgrade to the latest version of the 'snowflake-snowpark- python' package.
D) The dynamic table materialization schedule is too frequent, overwhelming the Snowflake warehouse. Increase the 'WAREHOUSE_SIZE' parameter of the dynamic table definition.
E) The dynamic table definition contains unsupported SQL syntax, such as 'QUALIFY with complex window functions. Rewrite the dynamic table definition to use standard SQL constructs.

3. You have a requirement to create Snowpark DataFrames from CSV files located in an AWS S3 external stage. Some CSV files have a header row, while others do not. The files also use different delimiters (comma, semicolon, or tab). You want to create a single function that can handle all these variations, without creating separate functions for each combination. The 'create_dataframe' function receives the stage path, the delimiter, and a boolean indicating whether a header is present. Which of the following code snippets, when implemented inside the function, BEST achieves this goal using the Snowpark Python API? Assume a Snowpark session 'session'.

A)

B)

C)

D)

E)

4. A data engineer is developing a Snowpark application using Python and needs to connect to Snowflake. They want to avoid hardcoding credentials directly in the script and utilize environment variables for authentication. Which of the following approaches is the MOST secure and RECOMMENDED way to retrieve Snowflake connection parameters (account, user, password, database, schema, warehouse, role) from environment variables and establish a Snowpark session?

A) Utilize the 'Session.builder.getorcreate()' method, assuming that Snowflake connection information (user, password, account, warehouse, etc.) are already set as environment variables with standard names, and let Snowpark automatically infer the parameters.
B) Store all connection parameters as a JSON string in a single environment variable and parse it within the Snowpark application.
C) Manually retrieve each parameter using 'os.environ.get()' and pass them directly into the 'Session.builder.configs()' method.
D) Use the SnowCLl to configure a connection profile, and then reference this profile name when creating the Snowpark session. Ensure each environment variable is also separately available, but are not explicitly called in the code to establish connection but only to set up SnowCLl.
E) Leverage the 'snowflake.connector.connect()' function with 'os.environ.get()' for credentials and then create a Snowpark session from the connection using Session. builder. from_connection(connectiony.

5. You are developing a Snowpark application that uses a Python UDF to perform geocoding operations. This UDF relies on a third-party geocoding library and a large dataset of geographical data stored in a file named 'geodata.db'. The UDF needs to be operationalized with minimal latency. Which of the following strategies will result in the FASTEST execution of the UDF and optimal resource utilization?

A) Create a Java UDF that performs the geocoding using a Java geocoding library. Upload the JAR file and 'geodata.db' to a stage and reference them using the 'imports' clause. Java UDFs always perform faster than Python UDFs.
B) Package the geocoding library and 'geodata.db' file into a ZIP file. Upload the ZIP file to a Snowflake stage and reference it using 'imports' in the UDF definition. Use a virtual environment to manage package dependencies.
C) Create a custom Anaconda channel containing the geocoding library and 'geodata.db'. Configure the Snowflake account to use this channel. No need to use virtual environment.
D) Use an external function that calls a geocoding service over the internet. Store 'geodata.db' in an S3 bucket and access it from the external function. Call the external service whenever it requires it.
E) Package the geocoding library and 'geodata.db' file into a ZIP file. Upload the ZIP file to a Snowflake stage and reference it using 'imports' in the UDF definition. Ensure 'geodata.db' is loaded only once into memory per worker process using global variable and proper caching for subsequent UDF invocations. Use a virtual environment to manage package dependencies.

Solutions: