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最新的 Snowflake Certification SPS-C01 免費考試真題:

1. 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) 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.
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) Install the 'transformers' library on the Snowflake compute warehouse nodes directly using a startup script.

2. Consider the following Snowpark code snippet:

Which of the following statements are TRUE regarding the execution and performance of this code?

A) Removing 'cached_df = line would significantly improve the overall performance because caching always adds overhead.
B) The 'filter' operation Ccol('column_a') > 100') will be executed twice.
C) The 'count? operation will use the cached results of and apply an additional filter on the cached data.
D) The 'countl' operation will trigger the materialization and caching of 'filtered_df.
E) The 'filter' operation Ccol('column_a') > 100') will be executed only once because 'cached_df stores the materialized result.

3. You have a Snowpark Python stored procedure that reads data from a Snowflake table, performs a complex calculation using Pandas, and then writes the results back to another Snowflake table. You are experiencing performance issues, and you suspect the data transfer between Snowpark and Pandas is a bottleneck. Which of the following techniques could significantly improve the performance of this stored procedure? (Select two)

A) Use the 'TABLE function in Snowpark to directly access the source table instead of reading the entire table into a Snowpark DataFrame at once.
B) Increase the warehouse size to the largest possible value before executing the stored procedure.
C) Utilize vectorized operations within Pandas to minimize explicit looping and improve calculation speed.
D) Convert the Pandas DataFrame to a Dask DataFrame for distributed computation.
E) Leverage Snowpark's optimized functions and UDFs wherever possible to perform transformations within Snowflake's engine instead of transferring data to Pandas.

4. You have a Snowpark Python UDTF that splits a comma-separated string into individual elements and returns them as rows. The UDTF is defined as follows:

Which of the following SQL queries correctly calls and uses this UDTF?

A)

B)

C)

D)

E)

5. You have a Snowflake table 'user_profiles' with a VARIANT column 'profile_data'. This column contains JSON objects, and one of the fields within these objects is an array called 'interests'. The 'interests' array contains JSON objects, each with 'name' and 'category' fields. You need to use Snowpark to flatten the 'interests' array and extract the 'name' and 'category' for all user profiles, but only for profiles where the user's 'status' is 'active'. You want to write this in the most efficient way possible. Which of the following code snippets will achieve this?

A)

B)

C)

D)

E)