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Snowflake SPS-C01 Exam Syllabus Topics:

Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

Question 1

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

Question 2

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. 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.
B. 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.
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. 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.
E. 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.

Question 3

You have a Python function that performs complex data transformations, too intricate to express directly in Snowpark SQL. You want to register this as a User-Defined Table Function (UDTF) so that it can be used to expand rows in a Snowpark DataFrame. The UDTF takes two arguments: an ID (integer) and a string. It returns a table with three columns: (integer), (string), and 'timestamp' (timestamp). Which of the following code snippets correctly registers this UDTF, making it available for use within Snowpark?

A.

B.

C.

D.

E.

Question 4

Consider the following Snowpark Python code snippet that defines and registers a User-Defined Table Function (UDTF):

Which of the following statements is MOST accurate regarding the behavior and limitations of this UDTF when used in a Snowpark DataFrame transformation?

A. The 'input_string' argument passed to the 'process' method will always be a single string value, even if the input DataFrame column contains NULL values.
B. The UDTF will process each input string in parallel, with Snowflake automatically distributing the processing across multiple worker nodes.
C. The UDTF can only be used with DataFrames that have been explicitly persisted as Snowflake tables.
D. The UDTF will be executed within the same Python process as the Snowpark driver program, limiting its scalability for large datasets.
E. If the input DataFrame column contains NULL values, the 'process' method will receive 'None' as the value for 'input_string'. The 'output_schema' correctly defines the structure of the output rows.

Question 5

You've created a Snowpark Python stored procedure designed to perform sentiment analysis on customer reviews stored in a Snowflake table. This procedure utilizes a third-party Python library, 'transformers', for its sentiment analysis model. You need to operationalize this stored procedure for scheduled execution. Which of the following options represents the MOST efficient and reliable approach for deploying and managing the 'transformers' dependency within the Snowflake environment for your stored procedure, minimizing deployment complexity and potential runtime errors?

A. Install the 'transformers' library on the Snowflake compute warehouse nodes directly using a startup script.
B. Include the 'transformers' library as a part of the task definition and make it available for the stored procedure that the task is invoking.
C. Upload the 'transformers' library as a zip file to a Snowflake stage and reference it in the 'imports' parameter when creating the stored procedure.
D. Include the 'transformers' library directly within the stored procedure's Python code as a string literal and execute it using or 'eval()'.
E. Create a Snowflake Anaconda channel, upload the 'transformers' library to this channel, and specify the channel in the stored procedure definition using the packageS parameter.

Solutions: