Snowflake Certified SnowPro Specialty - Snowpark: SPS-C01 Exam Premium File as PDF & Test Engine

Consider the following Snowpark code snippet designed to create a temporary table:

A developer encounters an error when calling this function. The error message indicates that the table already exists. How should the developer modify the code to handle this scenario gracefully, preventing the error and ensuring the temporary table is either created or overwritten?

• A. First drop the table using 'session.sql(fDROP TABLE IF EXISTS {table_name}')' before calling .
• B. Add the 'mode='append" option to the function. This will append the data to the existing table.
• C. Add the 'mode='overwrite" option to the function. This will replace the existing table with the new data.
• D. Use to create the temporary table.
• E. Add the 'mode='ignore" option to the function. This will silently skip the creation if the table already exists.

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You have a complex Snowpark Python UDF that aggregates data from various sources and returns a dictionary containing several metrics (e.g., '{'average price': 12.50, 'total sales': 1000, 'customer count': 50}'). You need to operationalize this UDF and ensure proper data type handling for each metric. Which of the following is the MOST appropriate way to define the return type using the registration API?

• A. Use a 'MapType' with 'StringType' as the key type and 'VariantType' as the value type.
• B. Use a single 'VariantType' to represent the entire dictionary.
• C. Define a 'StructType' with ' StructFielcf for each metric, specifying the appropriate data type (e.g.,
• D. Define the return type as 'StringType' and serialize the dictionary to JSON within the UDF.
• E. Use a single 'ArrayType' to represent the entire dictionary. 'Integer Type').

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You are profiling a Snowpark application that uses a combination of SQL queries and Python UDFs. You observe that a particular stage involving a UDF is taking significantly longer than expected. You suspect that the UDF's performance is the bottleneck. Which of the following steps would be the MOST comprehensive approach to diagnose and address the performance issue?

• A. Implement caching for the UDF's results to avoid recomputing the same values multiple times.
• B. Replace the Python UDF with an equivalent SQL query using Snowflake's built-in functions. If the SQL query performs better, the Python UDF was the bottleneck.
• C. Increase the warehouse size and re-run the application. If the execution time improves significantly, the issue was resource contention.
• D. Convert the scalar UDF to a vectorized UDF, even without fully understanding the source of the performance bottleneck.
• E. Use Snowflake's query profile to examine the execution plan and identify the UDF-related stages with the highest execution time. Then, analyze the UDF's code for inefficiencies, such as unnecessary loops or complex calculations.

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Consider a Snowflake table 'orders' with columns 'order_id', 'customer_id', 'order_date', and 'status'. You need to update the 'status' of all orders placed before January 1, 2023, to 'Archived'. Which of the following approaches is the most efficient and idiomatic way to achieve this using Snowpark DataFrames, assuming 'orders df DataFrame represents the 'orders' table?

• A. Option B
• B. Option A
• C. Option D
• D. Option C
• E. Option E

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You are using Snowflake Notebooks to develop a Snowpark application and want to leverage a custom Python library that is not available in the default environment. What steps are necessary to make this library available within your Snowflake Notebook?

• A. Install the library directly within the Snowflake Notebook using '!pip install
• B. Create a deployment file using setup.py, upload deployment file to stage, and create function
• C. Upload the Python library's ' .py' file directly to the Snowflake stage and import it using 'import sys; sys.path.append("); import
• D. Install the library using pip in the Snowflake Notebook's terminal and then restart the Snowflake Notebook.
• E. Create a conda environment specification file ('environment.yml') that includes the custom library, upload it to a Snowflake stage, and then create a new environment based on that file when creating or modifying the Snowflake Notebook.

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