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| Section | Objectives |
|---|---|
| DataFrame Operations and Data Processing | - Data transformation workflows
|
| Performance Optimization and Best Practices | - Efficient Snowpark execution
|
| Data Engineering with Snowpark | - Pipeline development
|
| Snowpark Fundamentals | - Snowpark architecture and concepts
|
| User Defined Functions and Stored Procedures | - Extending Snowpark with custom logic
|
| Testing, Debugging, and Deployment | - Production readiness
|
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NEW QUESTION # 207
You've transformed a large Snowpark DataFrame and want to persist it to a Snowflake stage for downstream applications. Your requirements are: 1. The data must be written in CSV format. 2. The files must be GZIP compressed. 3. A header row should be included in each file. 4. The files should be stored in a stage named 'customer_stage' in your Snowflake database. Which of the following code snippets correctly implements this, ensuring optimal performance and resource utilization?
Answer: D
Explanation:
Option B provides the most concise and readable way to achieve the desired outcome using the dedicated writer method. It directly specifies the header and compression options as parameters. Options A, D, and E require specifying the file format separately using 'format('csv')' and configuring header and compression through options, which is less direct. Option C has incorrect order - it need to set format first before setting options, so its less readable.
NEW QUESTION # 208
Consider a Snowflake table named 'raw events' containing web event data in a VARIANT column called 'event data'. Each 'event_data' JSON object may contain an array of product IDs under the key 'product_ids', and another array of user IDs who interacted with those products under the key 'user_ids'. These arrays are not guaranteed to be the same length, and a missing array should be handled gracefully (return null/empty result for that row rather than throwing error). Your objective is to create a Snowpark DataFrame that flattens both arrays into rows, pairing each product ID with each user ID for the specific event, generating as many rows as possible, and including event timestamp. You can use a zero-based index for the array elements using 'ARRAY CONSTRUCT()' and then using that array for using the EXPLODE function. Which of the following are necessary steps to fulfill this requirement and which are not?
Answer: A,B,D
Explanation:
Options A, C, and E are correct. A: Using and along with ensures dynamic index array generation and graceful handling of missing or non-numeric dat a). C: Using 'LEFT OUTER JOIN LATERAL FLATTEN' allows both 'product_ids' and 'user_ids' arrays to be flattened, even if one or both are missing. The LEFT JOIN ensures all rows from the original data are kept. The use of handles cases where either array is null or empty, preventing errors and providing default values. E:Using or 'case when' handles exception and different length of arrays.
NEW QUESTION # 209
You have developed a Snowpark Python stored procedure that calculates the average sales per region from a large sales data table. The procedure is currently defined inline within your Snowflake notebook. You want to operationalize this by creating the stored procedure from a local Python file named The file contains the following code: "'python from snowflake.snowpark.session import Session def calculate_avg_sales(session: Session, sales_table_name: str, region_column: str, sales_column: str) -> float: sales df = session.table(sales table name) avg_sales df = sales_df.group_by(region_column).agg({sales_column: 'avg'}) avg_sales = avg_sales_df.collect() return avg_sales[0][1] Which of the following code snippets correctly creates the stored procedure 'AVG SALES PROC in Snowflake, referencing the Python file, and handles potential dependency issues? Assume you have already established a Snowpark session named 'session' and that the stage 'my_stage' already exists.
Answer: D
Explanation:
Option D is the most appropriate because it correctly reads the Python file's content, constructs the CREATE PROCEDURE SQL statement dynamically, and uses the correct HANDLER syntax. It also correctly specifies the imports from the stage. It uses f-strings to create the SQL command in the code and make it dynamic. Option A would attempt to define the stored procedure inline, negating the purpose of creating it from an external file. Option B fails to correctly point to the handler function within the imported file, causing errors when the procedure executes. The entire file contents are being injected in to an SQL command instead of using the module import feature. Option C is incomplete. The handler attribute in the 'CREATE PROCEDURE' command needs to be explicitly defined for stored procedures created from files on a stage. Option C is also unnecessarily complex with dynamic module loading. Snowflake can handle loading from the stage directly using the HANDLER attribute correctly. There is no need to import the module yourself using importlib and then wrapping it in another sproc. Option E attempts to create the stored procedure in the current session but load the code from local. This will cause issues. It has to load code locally or stage.
NEW QUESTION # 210
You are working with a Snowpark DataFrame named containing information about products, including 'CATEGORY , 'SUBCATEGORY , and 'PRICE'. You want to determine the maximum price for each subcategory within each category. Furthermore, you need to filter the results to only include categories that have more than 5 subcategories. Which of the following Snowpark Python code snippets accomplishes this task? (Select all that apply)
Answer: A,E
Explanation:
The correct options are D and E. They both address the requirement and count distinct subcategories within each category. Option D: Correctly calculates the maximum price for each subcategory within each category. Then separately calculates the count of DISTINCT subcategories for each category. Filters the categories to include only those with more than 5 subcategories. Joins the two resulting DataFrames to provide the final output. This is a standard and explicit way to accomplish the task. Using countDistinct to ensure each subcategory is only counted once. Option E: Aggregates Max Price and CountDistinct of subcategory into same dataframe, this addresses all requirements in a more concise manner. Option A: Does not give the right result since 'count' will not give the countDistinct. Also using 'COUNT(SUBCATEGORYV in category_countS dataframe results in count of all rows in the group instead of the number of groups (number of Subcategories). Option B: Window functions is irrelevant here, and requires more coding while can be handled efficiently by aggregating and countDistinct combination Option C: Option C doesn't aggregate maximum price for each subcategory within each category as the question mentions.
NEW QUESTION # 211
Given a Snowpark DataFrame 'df with a column named 'data' of VARIANT type, where the VARIANT contains JSON objects with nested fields. You need to extract the value of the nested field 'address.city' as a STRING and the value of as a DOUBLE, handling cases where either 'address' or 'items' might be missing. Which combination of Snowpark functions is best suited to achieve this robustly and efficiently?
Answer: D
Explanation:
Option C is the most robust because it uses 'get_patW combined with and 'get_patm is specifically designed for extracting nested fields from VARIANT data, and handle cases where the path doesn't exist (returning NULL instead of an error).option A will fail if 'address' or 'items' is missing. Option B doesn't handle the NULL cases gracefully and 'to_double' expects a string not a variant. Option D doesn't handle the NULL cases gracefully and used 'to_varchaff which is deprecated. Option E will fail if 'address' or 'items' is missing and doesn't use get_path
NEW QUESTION # 212
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