Snowflake SPS-C01 pass test : Snowflake Certified SnowPro Specialty - Snowpark

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Snowflake SPS-C01 Exam Syllabus Topics:

Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. When creating UDFs/UDTFs in Snowpark Python, what are the advantages of explicitly specifying data types (either via Python type hints or the registration API) compared to relying on implicit type inference?

A) Automatic data type conversion by Snowflake, eliminating the need for explicit casting within the UDF/UDTF.
B) Improved performance due to reduced overhead in data type resolution at runtime.
C) Early detection of type-related errors during development, preventing runtime failures.
D) Reduced deployment time.
E) Enhanced code readability and maintainability, making it easier to understand the expected data types.

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

3. You are tasked with optimizing a Snowpark application that performs complex data transformations on a large dataset (1 TB) stored in Snowflake. The application currently uses Snowpark DataFrames and is experiencing slow performance. You suspect the issue might be related to data transfer overhead between the Snowflake engine and the Python environment. Which of the following strategies would be MOST effective in minimizing this overhead and improving performance?

A) Convert the Snowpark DataFrame to a Pandas DataFrame and perform the transformations locally within the Python environment.
B) Increase the virtual warehouse size to the largest available option (e.g., X-Large) to improve processing power within Snowflake, regardless of data transfer costs.
C) Implement vectorization techniques within the Snowpark DataFrame operations using built-in functions and optimized expressions where applicable.
D) Utilize User-Defined Functions (UDFs) written in Python to encapsulate the transformations and execute them within the Snowflake engine.
E) Reduce the data volume by applying aggressive filtering and aggregation using Snowpark DataFrame operations before any other transformations, minimizing the amount of data transferred.

4. 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 custom Anaconda channel containing the geocoding library and 'geodata.db'. Configure the Snowflake account to use this channel. No need to use virtual environment.
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) 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.
D) 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.
E) 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.

5. You are developing a Snowpark application to process sales data. The application uses a UDF that calls an external Python library with a large memory footprint. After deploying the application, you observe that the Snowflake warehouse frequently runs out of memory, causing the application to fail. Which of the following strategies would be MOST effective in mitigating this issue, while minimizing cost and maintaining performance? Assume the data volume is relatively large and the UDF is computationally intensive.

A) Implement a caching mechanism within the UDF to store intermediate results and reduce the number of calls to the external library. Deploy the UDF with the same warehouse size.
B) Increase the warehouse size to the largest available option. This will provide more memory to the UDE
C) Rewrite the UDF in Java using Snowpark API, which generally has a smaller memory footprint than Python. Deploy the UDF with the same warehouse size.
D) Implement the Python UDF as a Snowpark Stored Procedure. Deploy the UDF with the same warehouse size.
E) Modify the UDF to process data in smaller batches using a generator pattern, reducing memory consumption at any given time. Deploy the UDF with the same warehouse size.

Solutions: