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| Section | Objectives |
|---|---|
| DataFrame Operations and Data Processing | - Data transformation workflows
|
| User Defined Functions and Stored Procedures | - Extending Snowpark with custom logic
|
| Snowpark Fundamentals | - Snowpark architecture and concepts
|
| Testing, Debugging, and Deployment | - Production readiness
|
| Performance Optimization and Best Practices | - Efficient Snowpark execution
|
| Data Engineering with Snowpark | - Pipeline development
|
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NEW QUESTION # 249
You have a Pandas DataFrame 'products df containing product information that you want to upload to a Snowflake table named 'PRODUCTS' within the schema 'SALES'. The table might or might not exist. If it doesn't, you want to create it automatically. The products df DataFrame has a column 'product_name' that contains Unicode characters. Furthermore, you need to specify the column types directly as part of the operation. Which of the following options provides the most appropriate approach to achieve this with Snowpark?
Answer: B
Explanation:
Option A lacks the auto-creation of the table if it doesn't exist. Option B involves defining the schema explicitly and creating the dataframe first, which increases complexity, especially if the Pandas DataFrame has many columns or data types. Option C is close but does not allow to specify schema. Option D is incorrect because quote_identifiers = false would be the invalid parameter for write_pandas. session.write_pandas(df=products_df,table_name='SALES.PRODUCTS',schema='SALES', auto_create_table=True) allows for efficient data transfer, schema management, and table creation.
NEW QUESTION # 250
You have a Snowpark DataFrame named with the following schema: '(timestamp: TmestampType, sensor_id: StringType, value: FloatType)'. You need to identify the top 3 sensors with the highest average value over the entire dataset. Which of the following Snowpark Python code snippets correctly implements this requirement?
Answer: E
Explanation:
Option A provides the correct and most concise solution. It groups the 'sensor_data' DataFrame by , calculates the average value for each sensor using , orders the results by the average value in descending order using ascending-Falsey , and then limits the results to the top 3 sensors using 'limit(3)'. Option B attempts to use window function over the entire dataset without grouping by sensor, the result would be incorrect. Option C incorrectly attempts to use window specification without partition. Option D is similar as C with missing group by partition. Option E use 'sort' instead of 'orderBy' .
NEW QUESTION # 251
You are developing a Snowpark Python application and want to write unit tests for your custom UDFs using pytest. Consider the following UDF:
Which of the following pytest tests are correctly written and effectively test the UDF, including handling potential exceptions? (Select all that apply)
Answer: A,B,C,E
Explanation:
Options A, B, C, and D represent correctly written pytest tests. A tests a valid discount, B tests the exception handling for an invalid percentage, C tests the case with no discount, and D tests a full discount. E is incorrect because the calculate_discount function does not explicitly check for negative prices, therefore 'pytest.raises(ValueErrorV will cause a failed test since no ValueError is raised.
NEW QUESTION # 252
You are working with Snowpark and a DataFrame named 'orders df that contains order data, including a column named 'items' which is a VARIANT type and holds an array of JSON objects, where each object represents an item in the order. You need to explode this array into separate rows, extracting the 'item_id' and 'quantity' for each item. Which of the following Snowpark snippets correctly performs this transformation AND handles potential NULL or empty arrays in the 'items' column?
Answer: C
Explanation:
Option E is the most robust. It explicitly handles NULL or empty arrays in the 'items' column by replacing them with an empty array before exploding. This prevents errors during the explode operation. The other options will fail if the array is NULL because you cannot explode a NULL array. Additionally the items column need to be dropped after explode, so it won't be there in the last query. Column 'col' created by expload function must be used to extract the 'item_id' and 'quantity' values.
NEW QUESTION # 253
You have a Snowflake table named 'CUSTOMER DATA' with columns 'CUSTOMER ID', 'NAME, 'CITY , and 'ORDER COUNT. You want to create a Snowpark DataFrame named 'customer_df containing only customers from 'New York' with an 'ORDER COUNT greater than 10. Which of the following code snippets is the MOST efficient and correct way to achieve this, minimizing data transfer and maximizing pushdown optimization to Snowflake?
Answer: C
Explanation:
Option A is the most efficient. It directly uses the 'filter' method with the combined condition, allowing Snowpark to push down the entire filter to Snowflake for execution. Option B uses 'session.sqr, which might not be as optimized as using the table API directly. Option C pulls the entire table into Pandas, which is highly inefficient for large tables, defeating the purpose of Snowpark. Option D, while functional, is less readable than A, and the multiple 'where' calls are logically equivalent to a single 'filter' with combined conditions. Option E, while functional, contains redundant 'select(' 'V operation.
NEW QUESTION # 254
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