Snowflake Certified SnowPro Specialty - Snowpark : SPS-C01

SPS-C01 - Snowflake Certified SnowPro Specialty - Snowpark 考古題讓你考試達到事半功倍的效果

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擁有 SPS-C01 - Snowflake Certified SnowPro Specialty - Snowpark 學習指南你就能賺到了很大的一筆財富，它可以幫你提升工作職位和生活水準。并且還可以加強你的就業前景，開發很多好的就業機會。這是一個很適合參加 SPS-C01 證照考試考生的所必備的考試資料，不僅能為考生提供 Snowflake SPS-C01 考試相關的所有資訊，而且還可以提供一次不錯的學習機會。

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

1. Consider a Snowpark DataFrame with columns 'DEPARTMENT, 'SALARY , and 'YEAR. You want to find the average salary for each department over all years and then filter the departments to only include those where the average salary is greater than 100000. Which of the following approaches is the MOST efficient and correct way to achieve this using Snowpark Python?

A)

B)

C)

D)

E)

2. Consider two Snowpark DataFrames, 'employees' and 'departments' , with the following schemas: 'employees': (employee_id: Integer Type, employee_name: StringType, department_id: Integer Type, salary: IntegerType) 'departments': (department_id: Integer Type, department_name: StringType, location: StringType) You want to find the highest salary within each department, along with the department name and location, and display the results in a Snowpark DataFrame. Which of the following Snowpark Python code snippets correctly achieves this?

A)

B)

C)

D)

E)

3. You have a DataFrame 'df in Snowpark containing order data, including a VARIANT column named 'order details'. The 'order detailS column contains a nested JSON structure with fields like 'customer id' (always a string), 'items' (an array of item IDs, sometimes numbers, sometimes strings), and 'total amount' (inconsistent data type - sometimes string, sometimes number). You need to perform the following transformations: 1. Extract the "customer_id' and cast it to an integer. 2. Extract the first item ID from the 'items' array, attempting to cast it to an integer, handling potential casting errors. 3. Extract the 'total_amount' and cast it to a decimal (precision 10, scale 2), handling potential casting errors. Which of the following code snippets correctly implements these transformations using Snowpark?

A)

B)

C)

D)

E)

4. You have a Snowpark Python stored procedure 'process_data' that takes a Snowpark DataFrame as input, performs several data transformations using functions defined in a separate Python module 'data utils.py', and returns a transformed DataFrame. The 'data utils.py' file is located in your local directory. You want to register this stored procedure so that it can be called from Snowflake. Which of the following code snippets demonstrate(s) the correct way to register the stored procedure, ensuring that the 'data utils.py' module is available within the Snowpark environment? (Select TWO)

A)

B)

C)

D)

E)

5. You have a Snowpark application that utilizes a vectorized Python UDF to perform complex calculations on a large dataset. You notice that the performance is still not optimal. You suspect that the bottleneck might be related to how the data is being partitioned and processed by Snowflake. Which of the following actions, when performed in conjunction with vectorization, would MOST likely improve performance?

A) Increase the number of UDF worker threads within the UDF definition.
B) Repartition the Snowpark DataFrame using to align the data distribution with the computational needs of the UDF.
C) Broadcast the DataFrame to all compute nodes before applying the UDF.
D) Ensure that the data is pre-sorted according to the primary key of the table before applying the UDF.
E) Convert the DataFrame to a Pandas DataFrame before applying the UDF.

問題與答案：