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| Section | Objectives |
|---|---|
| Data Engineering with Snowpark | - Pipeline development
|
| User Defined Functions and Stored Procedures | - Extending Snowpark with custom logic
|
| Testing, Debugging, and Deployment | - Production readiness
|
| Snowpark Fundamentals | - Snowpark architecture and concepts
|
| DataFrame Operations and Data Processing | - Data transformation workflows
|
| Performance Optimization and Best Practices | - Efficient Snowpark execution
|
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NEW QUESTION # 50
You're working with a Snowpark DataFrame named 'sales_df' that contains sales transaction data'. You need to create a new DataFrame that includes only the rows where the 'order_date' is within the last 30 days. The 'order_date' column is currently stored as a string in 'YYYY-MM-DD' format. You want to create a schema and apply the schema to the dataframe. Choose the correct options that defines the schema in below code snippets:
Answer: B
Explanation:
Option C correctly defines the schema with DateType for 'order_date' , converts the string column to a DateType using 'to_date' , and then filters the DataFrame based on the date difference. Options A, B and D do not use StringType at right places and are therefore inefficient. Option E applies to_date without any need.
NEW QUESTION # 51
You have a Snowpark Python application that reads data from a Snowflake table, performs several transformations, and then writes the results back to a new Snowflake table. The transformations involve complex calculations and aggregations. During testing, you observe that the application is consuming a significant amount of credits. Which of the following optimization strategies would be MOST effective in reducing the credit consumption of your Snowpark application?
Answer: A,C
Explanation:
Converting Python UDFs to Java UDTFs can significantly improve performance and reduce credit consumption because Java UDTFs execute within the Snowflake engine. Minimizing data transfer by pushing down transformations and using stored procedures reduces the amount of data processed and transferred, thus reducing credit consumption. Disabling auto-scaling might reduce peak credit consumption but could also increase overall execution time and potentially increase costs if the warehouse is under-sized. Caching can help in some cases but introduces memory management complexities. Snowflake automatically handles index management and query optimization; explicitly specifying hints is generally not necessary and using cache might not be suitable for large datasets.
NEW QUESTION # 52
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?
Answer: A,E
Explanation:
Options C and D are both valid and efficient solutions. Option C leverages Snowpark's 'with_column' and 'when' functions to conditionally update the 'status' column within the DataFrame and persists the changes back to the table using 'save_as_table'. Using lit function makes sure that 'Archived' status is passed as a literal. Option D uses a direct SQL update statement executed through the Snowpark session. This bypasses DataFrame operations but directly modifies the table, which can sometimes be more efficient for simple update operations. Option A is almost correct but the with_column and filter are applied separately. Option B doesn't work due to the way update functions are implemented in Snowpark. Option E unnecessarily collects the filtered data back to the driver, creating a new DataFrame from the collected data, then applying 'with_column'. This is an inefficient approach.
NEW QUESTION # 53
You are developing a Snowpark application that needs to read data from a set of CSV files stored in a Snowflake stage named ' my_stage'. The files have a header row and are comma-delimited. You want to use the Snowpark API to create a DataFrame from these files, automatically inferring the schema. Which of the following code snippets correctly achieves this?
Answer: D
Explanation:
Option D is the correct way to read CSV files from a stage in Snowpark with header and schema inference using .format("csv").option(...y. Option A is close, but it doesn't use the correct syntax for specifying the stage location; it expects a file path relative to the current working directory.Option B incorrectly uses .csv()' with directly.Option C correctly passes options, but must use the load method in conjunction with format option. Option E has incorrect path specification in addition to incorrect function chain. Option D uses .load()" after setting the format and options is most appropriate.
NEW QUESTION # 54
You are working with a Snowpark DataFrame containing customer data'. One of the columns, 'phone number', contains phone numbers in various formats (e.g., '123-456-7890', '(123) 456-7890', '1234567890'). You need to standardize all phone numbers to the format '+1-123-456-7890' using Snowpark for Python. You also want to handle cases where the phone number is NULL gracefully, replacing them with '+1-000-000-0000'. Which of the following Snowpark code snippets is the most efficient and correct way to achieve this?
Answer: D
Explanation:
Option C is the most efficient because it uses built-in Snowpark functions (when, regexp_replace, substring, concat, length, and lit) to perform the transformation directly on the server-side. It first handles NULL values. It then removes non-numeric characters. Finally, it checks the length of the remaining digits before formatting, ensuring only valid 10-digit numbers are transformed, setting others to NULL. Options A, D, and E do not handle the case where after removing non-numeric characters, the length of phone number is not 10. Option B uses a UDF, which is generally less efficient than using built-in functions as it involves serialization/deserialization overhead .
NEW QUESTION # 55
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