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Snowflake SPS-C01 Exam Syllabus Topics:

Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. You are working with Snowpark and need to persist the results of a DataFrame 'df to a Snowflake stage named 'my_stage'. You want to achieve the following: 1. Write the data in JSON format. 2. Use snappy compression. 3. Handle potential write errors gracefully. 4. Overwrite any existing files with the same name. Which of the following approaches can achieve these requirements? (Select all that apply)

A) Use compression='snappy', mode='overwrite')' and handle potential exceptions using a 'try-except' block.
B) Define a UDF to write the dataframe into stage along with exception handling logic.
C) Configure the stage 'my_stage' with FILE_FORMAT = (TYPE = 'JSON', COMPRESSION = 'SNAPPY') and then use within a 'try-except' block.
D) Use 'df.write.option('compression', inside a 'try-except block.
E) Wrap the entire write operation in a try-except block and implement retry logic with exponential backoff in case of transient errors.

2. You are working with a Snowpark DataFrame 'sales_data' containing sales transactions. The DataFrame includes columns 'transaction_id' (STRING), 'product_id' (IN T), 'sale_date' (DATE), and 'sale_amount' (DOUBLE). You need to calculate the total sales amount for each product on a daily basis. Furthermore, you want to filter out any days where the total sales amount for a specific product is less than $50. Which of the following code snippets correctly achieves this using Snowpark Python?

A)

B)

C)

D)

E)

3. You are tasked with building a machine learning pipeline in Snowpark to predict customer churn. You plan to use the scikit-learn library for model training and want to deploy the trained model as a Snowpark UDF for real-time scoring. Consider the following code snippet:

A) The code will fail because the trained model needs to be saved to a stage and loaded within the UDF, rather than being passed directly.
B) The code will fail because the 'moder object cannot be directly serialized and passed to the UDE A different serialization method (e.g., pickle) is required.
C) The code will fail because the return type of the UDF is not explicitly defined. Snowpark requires explicit type hints for UDF return values.
D) The code will fail because scikit-learn is not a supported library for Snowpark UDFs by default, and needs to be explicitly added to the session imports.
E) The code will execute successfully and create a UDF that predicts churn using the trained model.

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) Split the stored procedure into multiple smaller procedures and execute them sequentially on a smaller warehouse.
B) Register the stored procedure with the '@sproc' decorator without specifying any warehouse size, letting Snowflake automatically manage the warehouse.
C) Run the stored procedure on a larger Snowflake warehouse to reduce execution time, regardless of potential idle time.
D) 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.
E) Convert the Python stored procedure to a SQL stored procedure to leverage Snowflake's SQL optimization engine.

5. A financial firm is using Snowpark Python to analyze stock trading data'. They have a DataFrame named 'trades' with columns 'trade_id', 'stock_symbol', 'trade_price', and 'trade_timestamp'. They want to identify potentially fraudulent trades based on the following criteria: 1. Trades where the 'trade_price' deviates significantly from the average price of that 'stock_symbol' over the past hour. 2. Trades originating from user accounts where the price is above $1000.3. Trades which has stock symbol 'XYZ'. The firm wants to apply multiple filters to the DataFrame to extract only the fraudulent trades and needs an efficient and concise approach using Snowpark. Which of the following code snippets, using 'trade_price' > 1000 as user identifier, MOST accurately and efficiently implements this filtering logic? Assume that a Snowflake user has a maximum amount they can spend on a trade, and therefore, the user ID is associated with 'trade_price'.

A)

B)

C)

D)

E)

Solutions: