SPS-C01 New Dumps Files, SPS-C01 Latest Dumps Questions

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Snowflake Certified SnowPro Specialty - Snowpark Sample Questions (Q370-Q375):

NEW QUESTION # 370
You have a Snowpark DataFrame 'df' containing customer data with columns 'customer id', 'name', 'age', and 'city'. You want to filter the DataFrame to include only customers from 'New York' who are older than 30, then extract the 'customer id' and 'name' into a Rows object, and finally print the 'name' of the first row in the Rows object. Which of the following code snippets correctly achieves this using Snowpark Python?

Answer: D

Explanation:
The correct answer is C. The code first filters the DataFrame based on the specified conditions. Then, it selects the 'customer_id' and 'name' columns. The 'collect()' method retrieves the data as a list of Rows objects. Finally, correctly accesses the 'name' attribute of the first row in the list. A uses dictionary access which is incorrect for Row objects, B iterates the dataframe and does not get the first row correctly, D accesses the list by index (incorrect approach) and E is only required in scala


NEW QUESTION # 371
You are developing a Snowpark stored procedure in Python that utilizes the 'requests' library to fetch data from an external API. Your Snowflake account is configured to use Anaconda packages. You encounter an error indicating that the 'requests' library is not found. Which of the following steps are MOST effective in ensuring the 'requests' library is available to your stored procedure?

Answer: D

Explanation:
The 'packages' argument in the 'CREATE OR REPLACE PROCEDURE statement is the correct way to specify dependencies on Anaconda packages. Snowflake will automatically resolve and make these packages available to the stored procedure. Option B is incomplete; while Anaconda integration is necessary, it doesn't automatically import the library. Options A, C and E are incorrect and not best practices.


NEW QUESTION # 372
You've written a Snowpark stored procedure in Python that utilizes a custom Python library named 'my utils.py' containing utility functions for data transformation. This library is not available in Anaconda'. You want to deploy the stored procedure and ensure that the custom library is accessible during execution. Which is the most appropriate way to operationalize your Snowpark stored procedure with the custom library?

Answer: A

Explanation:
Option A is the most appropriate. The 'imports' argument in 'session.sproc.register' is specifically designed for including custom modules and files that are not available through Anaconda. Uploading 'my_utils.py' to a stage and referencing it in the 'imports' argument makes the library available during stored procedure execution. Packaging into a wheel is used for libraries that are available through pypi but may require a specific version. Option C is bad practice, and options D and E are impossible.


NEW QUESTION # 373
You are using Snowpark Python to transform a large DataFrame containing customer transaction data'. You need to persist the resulting DataFrame as a new Snowflake table named 'CUSTOMER TRANSACTIONS AGGREGATED', replacing the existing table if it exists. You want to explicitly define the schema of the new table to ensure data types are correctly enforced. Which of the following code snippets achieves this most efficiently and correctly?

Answer: C

Explanation:
Option A is the simplest and most direct way to achieve the desired outcome using the method with the 'overwrite' mode. While defining the schema is important, Snowflake infers the schema from the DataFrame if not explicitly provided. If schema inference isn't working, it should be investigated as a separate issue. Option B requires an intermediary view, which is less efficient. Options C and D are not valid Snowpark options. While you can specify file format related options (e.g. CSV options when writing to cloud storage), 'table_type' isn't one of them. Option E introduces the concept of schema definition, which, while important in general, is unnecessary if Snowflake can infer the correct schema. The question asks for the most efficient and correct answer, which is A.


NEW QUESTION # 374
You have a Snowpark Python application that performs several data transformations. You need to implement error handling to catch exceptions during DataFrame operations and log them to a Snowflake table named 'error _ log'. You also need to ensure that if any exception occurs, the application continues to run and processes the remaining data. Which of the following approaches (or combination of approaches) would be MOST suitable?

Answer: B,C

Explanation:
Options B and C, when combined, provide the most robust error handling. Wrapping individual DataFrame operations in try-except blocks (B) allows you to catch specific exceptions and log them appropriately, preventing a single error from crashing the entire application. Implementing a UDF with error handling (C) encapsulates complex logic and allows you to return both processed data and error messages, making it easier to identify and address issues. A global try-except block (A) might catch errors, but it doesn't provide granular control or context. Relying solely on Snowflake's built-in mechanisms (D) is insufficient for custom error logging and continuing execution. While a Snowpark logging API (E) would be ideal, such an API doesn't currently exist and relies on an assumption.


NEW QUESTION # 375
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