SPS-C01 Questions & Answers & SPS-C01 Study Guide & SPS-C01 Exam Preparation

Snowflake SPS-C01 : Snowflake Certified SnowPro Specialty - Snowpark

Exam Code: SPS-C01

Exam Name: Snowflake Certified SnowPro Specialty - Snowpark

Updated: Jun 25, 2026

Q & A: 374 Questions and Answers

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Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. You are developing a Snowpark application that needs to read data from a set of CSV files stored in a Snowflake stage named ' my_stage'. The files have a header row and are comma-delimited. You want to use the Snowpark API to create a DataFrame from these files, automatically inferring the schema. Which of the following code snippets correctly achieves this?

A)

B)

C)

D)

E)


2. A Snowpark application needs to process a large dataset (1 TB) residing in Snowflake, performing complex transformations. Due to network constraints and the complexity of the transformations, the execution takes a considerable amount of time. The application's end-users are complaining about the latency. Which of the following strategies would MOST effectively enhance performance, specifically targeting the reduction of overall execution time considering the synchronous vs. asynchronous execution models and the implications of the 'block' parameter?

A) Employ asynchronous actions with 'block=True' to immediately retrieve results, ensuring data consistency but potentially increasing overall latency due to blocking.
B) Utilize synchronous actions to trigger transformations and retrieve results; this approach guarantees immediate availability but might not be optimal for long-running, complex operations.
C) Minimize data transfer by applying aggressive filtering and aggregation within Snowflake before transferring data to the Snowpark application for further processing, and use synchronous action with a small warehouse size.
D) Increase the Snowflake warehouse size to improve compute capacity, regardless of the synchronous or asynchronous execution model.
E) Implement asynchronous actions with 'block=False' to initiate transformations without waiting for completion, allowing other parts of the application to proceed concurrently. Subsequently, use a mechanism (e.g., polling, callbacks) to retrieve the results when available, leveraging parallel execution to enhance overall responsiveness.


3. A data engineering team is building a Snowpark application in Python to perform advanced time series analysis on sensor data stored in Snowflake. They need to leverage a specific, but older, version of the 'pandas' library (version 1.1.5) that is not available in the default Snowflake Anaconda channel. Which of the following approaches is the MOST efficient and recommended way to ensure this specific version of 'pandas' is available to their Snowpark application, while minimizing security risks and operational overhead?

A) Create a new Snowflake Anaconda environment and install pandas 1.1.5 into this environment, then configure the Snowpark session to use this custom environment. Ensure the 'snowflake-snowpark-python' package is also included.
B) Package the pandas 1.1.5 library within the Snowpark application code itself, by including it in the application deployment package.
C) Build a custom Anaconda channel outside of Snowflake, and configure the Snowpark session to point to that channel.
D) Use 'conda install pandas==l .1.5' within the Snowpark Python code directly. This will install pandas at runtime.
E) Upload the pandas 1.1.5 wheel file directly into a Snowflake stage and reference it within the session configuration using along with


4. You have a Snowpark DataFrame 'product_df and you want to create a view named 'active_products' that only includes products with a 'status' column value equal to 'ACTIVE'. This view should be accessible to all users in the 'PUBLIC' role. Which of the following code snippets correctly creates the view and grants the necessary privileges?

A)

B)

C)

D)

E)


5. You have a Snowpark DataFrame 'sales df with columns 'product_id' (INTEGER), 'sale date' (DATE), and 'sale_amount' (DOUBLE). You need to filter the DataFrame to include only sales that occurred in the year 2023 and where the 'sale_amount' is greater than the average 'sale_amount' for that specific 'product id'. Which of the following Snowpark Python code snippets will correctly achieve this?

A)

B)

C)

D)

E)


Solutions:

Question # 1
Answer: B
Question # 2
Answer: E
Question # 3
Answer: A
Question # 4
Answer: D
Question # 5
Answer: E

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