Snowflake Certified SnowPro Specialty - Snowpark : SPS-C01

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Snowflake SPS-C01 Exam Syllabus Topics:

Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. A data engineer is tasked with transforming a large dataset of customer transactions using Snowpark Python. The dataset contains personally identifiable information (PII) that needs to be masked before further analysis. They decide to use a UDF to perform the masking. Consider the following Python UDF:

The engineer registers this UDF and attempts to apply it to a column named 'customer email' in a Snowpark DataFrame named 'customer data'. Which of the following code snippets is the MOST efficient and secure way to apply this UDF and replace the 'customer email' column with the masked values?

A)

B)

C)

D)

E)

2. Which of the following are key benefits of using Snowpark's server-side execution capabilities for data processing tasks within Snowflake, compared to performing the same tasks on the client-side? (Select all that apply)

A) Simplified code deployment and maintenance.
B) Greater flexibility in choosing programming languages for data processing.
C) Increased processing speed due to leveraging Snowflake's scalable compute resources.
D) Improved security by keeping data within Snowflake's secure environment.
E) Reduced data transfer costs between the client and Snowflake.

3. A data engineering team is using Snowpark Python to build a complex ETL pipeline. They notice that certain transformations are not being executed despite being defined in the code. Which of the following are potential reasons why transformations in Snowpark might not be executed immediately, reflecting the principle of lazy evaluation? Select TWO correct answers.

A) The 'eager_execution' session parameter is set to 'True'.
B) The size of the data being processed exceeds Snowflake's memory limits, causing transformations to be skipped.
C) Snowpark employs lazy evaluation to optimize query execution by delaying the execution of transformations until the results are actually required.
D) Snowpark operations are only executed when an action (e.g., 'collect()', 'show()', is called on the DataFrame or when the DataFrame is materialized.
E) Snowpark automatically executes all transformations as soon as they are defined, regardless of whether the results are needed.

4. A data scientist has developed a Snowpark Python stored procedure named 'model_training'. This procedure utilizes a large machine learning model and requires significant compute resources. The data scientist wants to optimize the cost and performance of running this stored procedure. Which of the following strategies would be the MOST effective for achieving this goal?

A) Specify a warehouse size using the 'warehouse' parameter within the '@sproc' decorator and leverage auto-suspend and auto-resume features to minimize costs when the procedure is idle.
B) Split the stored procedure into multiple smaller procedures and execute them sequentially on a smaller warehouse.
C) Convert the Python stored procedure to a SQL stored procedure to leverage Snowflake's SQL optimization engine.
D) Run the stored procedure on a larger Snowflake warehouse to reduce execution time, regardless of potential idle time.
E) Register the stored procedure with the '@sproc' decorator without specifying any warehouse size, letting Snowflake automatically manage the warehouse.

5. 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)

Solutions: