Study with SPS-C01 Actual Test Questions

You have a DataFrame 'df in Snowpark representing customer data'. One of the columns, 'customer_details', contains JSON objects with varying structures. Some objects contain 'address' and 'phone' fields, while others only contain 'email'. You need to write a Snowpark query to extract the 'city' from the 'address' field if it exists; otherwise, return NULL. What is the most efficient way to achieve this using the function?

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Consider the following Snowpark Python code snippet designed to read data from a Snowflake table, apply a user-defined function (UDF) for data transformation, and then write the transformed data to another table. The UDF, 'calculate_score' , requires a configuration file ('config.json') to be loaded. Which of the following code snippets demonstrates the CORRECT and MOST efficient way to load and access the "config.json' file within the UDF, ensuring that it's available to all UDF invocations without requiring network access?

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You are developing a Snowpark Python stored procedure that needs to interact with an external REST API. The API requires authentication using an API key, which you want to store securely and access within the stored procedure. What is the MOST secure and recommended way to store and retrieve the API key within the stored procedure?

• A. Store the API Key as a comment in the Store procedure code, and retrieve it using REGEX
• B. Store the API key as a constant string within the stored procedure's code.
• C. Store the API key in a Snowflake Secret and access it using the 'secrets' module within the stored procedure.
• D. Store the API key in a Snowflake table and query it within the stored procedure.
• E. Store the API key as an environment variable within the Snowflake warehouse configuration.

You are designing a Snowpark application to process streaming data ingested into Snowflake using Snowpipe. The application needs to apply a complex set of transformations and aggregations to the incoming data in real-time. Which of the following approaches would be MOST suitable for this scenario, leveraging the strengths of Snowpark architecture?

• A. Continuously query the incoming data from Snowpipe using a Snowpark DataFrame and perform the transformations and aggregations on the client-side in a loop.
• B. Create a Snowpark DataFrame that represents the incoming data and use the 'write_pandaS function to write the transformed data to a separate Snowflake table after each micro-batch.
• C. Utilize Snowflake's Streams and Tasks feature and define views with complex SQL transformations that leverages Snowpipe.
• D. Define a Snowpark Stored Procedure ('sproc') that is triggered automatically by a Snowflake Task whenever new data arrives via Snowpipe. The stored procedure performs the transformations and aggregations and stores the results in a new table.
• E. Use Snowpark to define a series of chained UDFs that perform the transformations and aggregations directly within the Snowpipe pipeline.

You're developing a Snowpark Python application to process log files stored in an external stage 's3_logs'. These logs are in plain text, with each line representing a log entry. You need to filter log entries based on a specific keyword and extract timestamps from the matching lines. Which of the following approaches, using and Snowpark DataFrames, will efficiently accomplish this, avoiding unnecessary data transfer to the client?

• A. Create a UDF that accepts a 'SnowflakeFile' object as input, reads the file line by line inside the UDF, filters lines based on the keyword, extracts timestamps, and returns a list of timestamps. Call this UDF on a Snowpark DataFrame created using 'session.read.option('PATTERN', ' .10g').format('CSV').load('@s3_logs'V.
• B. Use 'session.read.option('PATTERN', ' to load all log files into a Snowpark DataFrame with a single column containing the log lines. Create a UDTF that reads a partition of the DataFrame, filters lines based on the keyword, extracts timestamps, and returns a table of timestamps.
• C. Download each log file using 'SnowflakeFile.get' , read the file line by line in the client application, filter lines based on the keyword, extract timestamps, and then create a Snowpark DataFrame with the extracted timestamps.
• D. Create an external function that accesses the files directly from S3, performs the filtering and timestamp extraction, and returns the results. Then create a Snowpark DataFrame to invoke the external function.
• E. Use pattern=' .log')' to load all log files into a Snowpark DataFrame with a single column containing the log lines. Then, filter the DataFrame using like' to find lines containing the keyword and extract timestamps using regular expressions in a subsequent select statement.