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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Transformations and Operations | 35% | - DataFrame manipulation
|
| Topic 2: Performance and Best Practices | 10% | - Security and governance
|
| Topic 3: Snowpark Concepts and Architecture | 25% | - Snowpark architecture and execution model
|
| Topic 4: Snowpark API and Development | 30% | - Multi-language support
|
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問題 #293
You are developing a Snowpark application that requires calling a stored procedure. Which of the following approaches is the MOST secure and efficient way to call a stored procedure from your Snowpark Python code, assuming the stored procedure returns a single value?
答案：C
解題說明：
The 'session.call()' method (B) is the most direct and efficient way to call a stored procedure and retrieve its single return value from Snowpark Python. It handles the execution and data retrieval efficiently. Using 'session.sql()' (A and C) is less efficient as it requires parsing SQL and manual extraction of the result. 'session.sproc()' (D) is generally used to register Python functions as stored procedures and not for calling existing ones. is not valid, 'session.call()' is the right way to call procedure
問題 #294
You have a Snowpark DataFrame 'employees' with columns 'employee_id' (INT), 'name' (STRING), 'department' (STRING), and 'salary' (DOUBLE). You want to create a new DataFrame that contains the top 3 highest-paid employees within each department. Which of the following approaches is the MOST efficient and correct way to achieve this using Snowpark Python?
答案：B,C,E
解題說明：
The most efficient and correct approaches are A, C, and D. These options utilize window functions in Snowpark to efficiently rank employees within each department. Option A uses , which assigns a unique rank to each row within a partition, even if there are ties in salary. Option C uses 'rank()' , which assigns the same rank to rows with equal salary values, resulting in possible gaps in the ranking sequence. Option D uses , which assigns consecutive ranks without gaps, even if there are ties in salary. The most appropriate ranking function will depend on the specific ranking requirement but all three would provide top 3 employees. Option B is incorrect because Snowpark 'apply' does not support groupby operations effectively, resulting in an error. Finally E will only return 2 employees, which would make it incorrect.
問題 #295
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?
答案：C
解題說明：
Option D correctly implements the required transformations: Since 'customer_id' is always a string, but should be converted to Integer, we can directly cast. is used for to handle cases where the item ID might not be a valid number. 10, is used for 'total_amount' to handle potential casting errors and to cast as decimal with precision 10 and scale 2 Options A, B, C, and E will fail when the values are incorrect data types, they do not perform handle exceptions.
問題 #296
You have a Snowpark DataFrame 'df' containing customer data with columns 'customer id', 'name', 'age', and 'city'. You want to filter the DataFrame to include only customers from 'New York' who are older than 30, then extract the 'customer id' and 'name' into a Rows object, and finally print the 'name' of the first row in the Rows object. Which of the following code snippets correctly achieves this using Snowpark Python?
答案：B
解題說明：
The correct answer is C. The code first filters the DataFrame based on the specified conditions. Then, it selects the 'customer_id' and 'name' columns. The 'collect()' method retrieves the data as a list of Rows objects. Finally, correctly accesses the 'name' attribute of the first row in the list. A uses dictionary access which is incorrect for Row objects, B iterates the dataframe and does not get the first row correctly, D accesses the list by index (incorrect approach) and E is only required in scala
問題 #297
A Snowpark application frequently experiences performance degradation during peak hours. You suspect that resource contention on the virtual warehouse is the primary cause. Which of the following actions, when implemented together, would BEST address this issue and ensure consistent performance?
答案：B,C
解題說明：
Workload management rules (A) allow you to prioritize Snowpark workloads, ensuring they receive preferential access to resources during peak hours. Creating separate virtual warehouses (C) isolates different workloads, preventing resource contention and allowing you to apply resource monitors for cost control. Shortening AUTO SUSPEND (B) may lead to frequent warehouse starts, increasing costs. Query acceleration (D) can help, but it might not be the most cost-effective solution for all queries. Converting DataFrames to views (E) doesn't directly address resource contention.
問題 #298
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