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| Section | Weight | Objectives |
|---|---|---|
| Snowpark Concepts | 15% | - Client-side vs. Server-side execution - Snowpark architecture and core concepts - Transformations vs. Actions - Snowpark Sessions and connection management - Stored procedures and conditional logic - Snowpark DataFrames and query plans |
| Snowpark API for Python | 30% | - User-Defined Functions (UDFs) and Stored Procedures - DataFrame creation and manipulation - Establishing connections and session management - Working with Semi-structured data - Reading and writing data |
| Data Transformations and DataFrame Operations | 35% | - Using built-in functions - Persisting transformed data - Window functions - Filtering, Aggregating, and Joining DataFrames - Complex data pipelines |
| Performance Optimization and Best Practices | 20% | - Caching strategies - Warehouse sizing for Snowpark - Minimizing data transfer - Debugging and explain plans - Query pushdown and optimization - Vectorized UDFs |
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問題 #63
You have a Snowflake table 'orders_json' with a VARIANT column named "order details'. This column contains JSON objects, and one of the fields within these objects is an array called 'items'. You need to use Snowpark to flatten the 'items' array into rows, extracting the 'item_id' , 'item_name' , and 'quantity' from each item in the array. Which of the following Snowpark code snippets will correctly achieve this, assuming 'df is a DataFrame representing 'orders_json'?
答案：D
解題說明：
Option E is correct because it first extracts the 'items' array into a new column 'items_array' using 'withColumn' . Then, it uses the explode' function to flatten the array into rows, aliasing the exploded column as 'item'. Finally, it selects the desired fields 'item_name' , quantity') from the 'item' column using 'getltem'.
問題 #64
You have a large CSV file containing product descriptions that you need to analyze using a sentiment analysis UDF. The CSV file is too large to fit in memory on your local machine. You want to stream the data directly from a Snowflake stage to your UDF for processing, avoiding the need to download the entire file. Which of the following approaches allows you to achieve this using Snowpark?
答案：C
解題說明：
Option B is the correct approach. Creating an external table allows you to treat the CSV file on the stage as a regular table within Snowflake. You can then use a Snowpark DataFrame to query this external table and efficiently process the data in parallel using the sentiment analysis UDF. Option A is not feasible because you cannot directly load the file into Pandas, as it requires the file to be local to UDF execution environment, defeating the purpose of using a stage directly. Option C: is not intended for reading arbitrary files; it's typically used for Snowpipe ingestion. Option D introduces unnecessary data movement and complexity. Option E is discouraged due to security concems and potential resource conflicts; UDFs should not establish new connections to Snowflake.
問題 #65
You have a Snowpark DataFrame 'df with a column 'transaction_date' of STRING type, containing dates in 'YYYY-MM-DD' format, and a 'transaction_amount' column of STRING type, containing currency values like '$1 ,234.56'. You need to create a new DataFrame 'df_transformed' that contains the 'transaction_date' as a DATE type and 'transaction_amount' as a DOUBLE type. What Snowpark Python code snippet will accomplish this transformation most efficiently and handle potential errors during the cast?
答案：C
解題說明：
Option C is the most robust because it uses and , which handle potential conversion errors by returning NULL instead of throwing exceptions. It also correctly uses 'regexp_replace' to remove both '$ and ',' from the 'transaction_amount' string before casting. Option A doesn't handle the format string for 'to_date' or special characters in amount. Option B doesnt remove special charcaters from transaction amount.Option D will not return other existing Columns, and also it doesn't handle the format string for 'to_date' or special characters in amount. Option E will not handle errors gracefully and redundantly imports 'snowflake.snowpark.functions as sf, and it also requires providing a format string for 'to_date' .
問題 #66
You have a Snowpark DataFrame named containing order data that needs to be inserted into the 'ORDERS table. However, due to a recent data ingestion issue, some records in might already exist in the 'ORDERS table based on the 'ORDER ID' column. Your goal is to insert only the new orders into the 'ORDERS table while avoiding duplicates. Which of the following approaches, combining efficiency and correctness, is most suitable for this task? Assume 'session' and required libraries are already imported.
答案：A,E
解題說明：
Options A and C are both suitable and efficient. Option A uses a 'left_anti' join to identify records in 'staged_orders' that do not exist in the 'ORDERS' table based on 'ORDER ID. This is a standard and efficient way to filter out existing records using Snowpark's DataFrame operations. Option C suggests a stored procedure with a MERGE statement, which is highly efficient for upsert operations directly within Snowflake. Option B is inefficient because it collects all the order IDs from the 'ORDERS table into the driver's memory, which could cause memory issues with large datasets. Option D is incorrect as 'on_duplicate_key' is not a valid parameter for insert_into method. Option E is using pandas dataframe to insert, which might not perform well in terms of scale.
問題 #67
You have a Snowpark DataFrame containing data to be loaded into a Snowflake table You want to use the 'merge' operation to update existing records and insert new records. The has a column (BOOLEAN) that indicates whether a record should be updated (TRUE) or inserted (FALSE) if it doesn't already exist. Which of the following SQL expressions correctly implement this logic within the 'merge' operation's 'WHEN MATCHED' and 'WHEN NOT MATCHED' clauses?
答案：C
解題說明：
The correct SQL expression utilizes the column within the 'WHEN MATCHED and 'WHEN NOT MATCHED clauses to conditionally determine whether to update or insert a record. Option E correctly checks the for 'TRUE in the 'WHEN MATCHED' clause and 'FALSE' in the 'WHEN NOT MATCHED clause to conditionally trigger the 'UPDATE' and 'INSERT operations, respectively. It uses Boolean algebra directly for concise condition checking. When the update_flag is True the update is executed, when false it is inserted. This implementation allows for a single 'merge' statement to handle both update and insert operations based on a flag within the staging data.
問題 #68
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