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Snowflake Certified SnowPro Specialty - Snowpark 認定 SPS-C01 試験問題:

1. You are tasked with operationalizing a Snowpark Python UDF for batch scoring of a large dataset. The UDF takes a set of feature columns and returns a prediction. You want to optimize performance and resource utilization. Select all the strategies that would effectively improve the operational efficiency and scalability of your UDF execution.

A) Utilize the 'vectorized' argument during UDF registration to enable batch processing of input data within the UDF.
B) Ensure that the Snowpark DataFrame being passed to the UDF is appropriately partitioned based on a relevant column (e.g., a geographical region) before invoking the UDF.
C) Adjust the 'MAX BATCH SIZE parameter for the warehouse executing the UDF to the largest possible value to minimize overhead.
D) Always use a warehouse size of 'X-Large' or larger regardless of the data volume to guarantee sufficient resources for UDF execution.
E) If the UDF performs external API calls, implement retry logic with exponential backoff to handle transient network errors gracefully.

2. You are developing a Snowpark application that processes large volumes of JSON data from an external stage. Initial testing on a MEDIUM warehouse results in significant query queuing. You suspect the issue is CPU bound due to complex JSON parsing and UDF execution within Snowpark. Considering only warehouse sizing options and assuming cost is a secondary concern to performance during peak processing hours, which strategy is MOST effective for optimizing performance? Consider the impact on concurrency.

A) Scale out to multiple MEDIUM warehouses using auto-scaling. This increases concurrency, allowing more queries to run simultaneously, but might not address CPU-bound operations within a single query.
B) Implement query acceleration using materialized views to pre-compute JSON parsing results. Then, add warehouses as needed for concurrent requests
C) Scale down to a SMALL warehouse. Smaller warehouses are optimized for smaller operations and can process certain types of operations faster. This could improve latency.
D) Upgrade the warehouse to a LARGE. This provides more CPU and memory for the existing workload, potentially resolving the bottleneck and improving overall throughput.
E) Upgrade to an X-LARGE or higher warehouse, leveraging the increased resources to handle complex parsing and UDF execution more efficiently. Monitor CPU utilization after the upgrade.

3. You are developing a Snowpark application to analyze website traffic data'. You have a DataFrame named 'website_logs' with columns 'user_id', 'page_url', and 'timestamp'. You need to create a new DataFrame that contains the count of distinct users who visited each page within a specific time window Consider the following (incomplete) Snowpark Python code:

Which of the following code lines, when inserted into the Complete the following line...' comment, will correctly calculate the approximate distinct user count for each page within the specified time window?

A) website_logs.with_column('distinct_users', F.approx_count_distinct('user_id').over(window_spec))
B) website_logs.groupBy('page_url').agg(F.countDistinct('user_id').alias('distinct_users'))
C) _logs.with_column('distinct_users', F.countDistinct('user_id').over(window_spec)) website
D) website_logs.with_column('distinct_users', F.count('user_id').over(window_spec))
E) website_logs.groupBy('page_url', F.window('timestamp', '1 hour')).agg(F.countDistinct('user_id').alias('distinct_users'))

4. You have created a Snowpark UDF that uses a custom Python module 'my_module.py', containing a function 'process data'. This module is not available through Anaconda'. You've packaged the module into a zip file named 'my module.zip'. What steps are necessary to deploy this UDF in Snowflake so that it can correctly use the 'my_module'?

A) Upload 'my_module.zip' to an internal stage, then create the UDF using 'session.add_import' within the UDF definition, specifying the stage path. No additional configuration is needed.
B) Upload 'my_module.zip' to an internal stage. When creating the UDF, specify the stage path in the 'imports' argument. Within the UDF, modify 'sys.path' to include the path where Snowflake unpacks the zip file.
C) Upload 'my_module.zip' to an internal stage. When creating the UDF, specify the stage path in the 'imports' argument. No changes to sys.path are required within the UDF.
D) Upload 'my_module.zip' to an external stage (e.g., AWS S3 or Azure Blob Storage). Configure Snowflake to access the external stage. Create the UDF, specifying the external stage path in the 'imports' argument.
E) Upload 'my_module.zip' to an internal stage. When creating the UDF, specify the stage path in the 'packages' argument. Within the UDF, modify 'sys.path' to include the path where Snowflake unpacks the zip file.

5. 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) 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.
B) The UDTF will be executed within the same Python process as the Snowpark driver program, limiting its scalability for large datasets.
C) The UDTF will process each input string in parallel, with Snowflake automatically distributing the processing across multiple worker nodes.
D) The UDTF can only be used with DataFrames that have been explicitly persisted as Snowflake tables.
E) 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.

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