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Snowflake SPS-C01 Exam Overview:

Snowflake SPS-C01 Exam Syllabus Topics:

Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

Question 1

Consider the following Snowpark Python code snippet for creating a stored procedure:

What is the PRIMARY reason for explicitly defining 'input_types' and during the stored procedure registration?

A. To allow Snowflake to automatically generate documentation for the stored procedure's input and output types.
B. To enable the stored procedure to be called from other programming languages besides Python.
C. To improve the performance of the stored procedure by enabling compile-time optimizations.
D. To ensure data type safety and schema validation during deployment and execution, preventing unexpected runtime errors due to type mismatches between the stored procedure and the calling environment.
E. To allow Snowsight to correctly display the stored procedure's metadata, making it easier for users to understand its functionality.

Question 2

Consider a Snowflake table 'orders' with columns 'order_id', 'customer_id', 'order_date', and 'status'. You need to update the 'status' of all orders placed before January 1, 2023, to 'Archived'. Which of the following approaches is the most efficient and idiomatic way to achieve this using Snowpark DataFrames, assuming 'orders df DataFrame represents the 'orders' table?

A. Option E
B. Option D
C. Option B
D. Option C
E. Option A

Question 3

You have a Snowpark Python stored procedure that needs to access environment variables stored securely within Snowflake. Which of the following code snippets demonstrates the correct way to retrieve the value of an environment variable named 'API KEY' within your stored procedure?

A.

B.

C.

D.

E.

Question 4

Given a Snowpark DataFrame 'df with a column named 'data' of VARIANT type, where the VARIANT contains JSON objects with nested fields. You need to extract the value of the nested field 'address.city' as a STRING and the value of as a DOUBLE, handling cases where either 'address' or 'items' might be missing. Which combination of Snowpark functions is best suited to achieve this robustly and efficiently?

A.

B.

C.

D.

E.

Question 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 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.
B. Deploy the UDTF as an external function, leveraging an external API gateway to invoke the Python code running on a serverless compute platform.
C. 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.
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.

Solutions: