Snowflake SPS-C01 : Snowflake Certified SnowPro Specialty - Snowpark

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Snowflake SPS-C01 Exam Syllabus Topics:

Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. You are developing a Snowpark application that requires calling a stored procedure. Which of the following approaches is the MOST secure and efficient way to call a stored procedure from your Snowpark Python code, assuming the stored procedure returns a single value?

A)

B)

C)

D)

E)

2. 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)

3. You are tasked with optimizing a Snowpark Python application that performs complex data transformations on a large dataset. The application is running slower than expected, and you suspect that data serialization and transfer between the Snowpark client and the Snowflake engine are bottlenecks. Which of the following strategies could you implement to improve performance? (Select all that apply.)

A) Minimize the amount of data transferred between the client and the engine by pushing down as much computation as possible to Snowflake using Snowpark DataFrame operations.
B) Convert all dataframes to Pandas dataframes locally and perform data manipulation with Pandas methods to take advantage of local resources.
C) Increase the configuration parameter to maximize parallelism within the Snowpark engine without considering resources or potential bottleneck.
D) Create and utilize temporary tables within Snowflake to store intermediate results of complex transformations.
E) Utilize smaller batch sizes when writing data back to Snowflake to reduce memory pressure on the client.

4. You are building a Snowpark application to process sensitive data'. To enhance security, you want to leverage ephemeral sessions. Which configurations, passed to 'snowpark.Session.builder.configS , are required and sufficient to create an ephemeral session? Assume your Snowflake environment is properly configured to allow ephemeral sessions.

A) Setting the 'role' parameter to a role with appropriate privileges and 'warehouse'.
B) Only setting the 'role' parameter to a role with appropriate privileges.
C) Setting the 'role' parameter to a role with appropriate privileges and 'database' and 'schema'.
D) Setting the 'role' parameter to a role with appropriate privileges, 'database', 'schema', and 'warehouse'.
E) Ephemeral sessions do not have specific configurations; they are automatically created by Snowflake when the application connects.

5. You are tasked with building a Snowpark application to process sensor data from IoT devices. The data arrives as JSON strings and needs to be transformed into a tabular format before being stored in a Snowflake table. You decide to use a User-Defined Table Function (UDTF) written in Python to handle this transformation. Which of the following approaches is the MOST efficient and scalable way to deploy and execute this UDTF in a production Snowpark environment, considering the possibility of high data volumes and concurrency?

A) Deploy the UDTF as a stored procedure that is then called from your Snowpark application. The UDTF processes the data serially, one record at a time.
B) Deploy the UDTF using the '@udtf decorator and explicitly specify a parameter when registering the function to control the size of input batches processed by each worker node.
C) Deploy the UDTF as an external function, leveraging an external API gateway to invoke the Python code running on a serverless compute platform.
D) Deploy the UDTF as an inline Python function within the Snowpark DataFrame transformation code, relying on the client-side Python interpreter for execution.
E) Package the Python UDTF into a zip file and upload it to a Snowflake stage. During UDTF registration, specify the stage location and use [...l, return_type=..., packages=['snowflake-snowpark-python', 'your_package'])'. Ensure that is appropriately tuned based on data size and complexity.

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