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| Section | Weight | Objectives |
|---|---|---|
| Snowpark API and Development | 30% | - Python API fundamentals
|
| Performance and Best Practices | 10% | - Security and governance
|
| Data Transformations and Operations | 35% | - DataFrame manipulation
|
| Snowpark Concepts and Architecture | 25% | - Session management and connection
|
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NEW QUESTION # 247
You have a Snowpark DataFrame named 'employee_df with columns 'employee_id', 'department', and 'salary'. You want to calculate the average salary for each department and add it as a new column named 'avg_department_salary' to the original DataFrame. Additionally, you want to sort the resulting DataFrame by department and then by salary in descending order. Which of the following Snowpark code snippets correctly implements this requirement?
Answer: B,C
Explanation:
Option D and E are correct. Both implement the window function correctly to add the average salary per department as a new column and then sort the DataFrame as required. Option E is very close and just has the window partition defined with 'col('department')'. Option A is correct but uses sort. Option B is incorrect because it does not include the original columns other than department when grouping so will loose important data, and attempts to sort on a column that no longer exists.Option C uses 'avg('salary')' instead of avg(col('salary'))
NEW QUESTION # 248
You have a Snowpark DataFrame named containing daily sales transactions. The DataFrame includes columns like 'transaction_id' , 'product id', 'sale_date', and 'sale_amount'. You need to perform the following transformations and persist the results: (1) Calculate the total sales amount for each product on a daily basis. (2) Store the aggregated results into a new table named , partitioned by 'sale_date'. (3) Ensure that if the table already exists, the new data is appended to the existing table. Which of the following code blocks achieve these requirements in the most efficient and correct manner?
Answer: A
Explanation:
Option A is the most efficient and correct. It first aggregates the data as required, then uses 'mode('append')' to add new data without overwriting existing data, and for partitioning. Option B will overwrite existing data. Option C is equivalent to option A, but is a less common coding style. Option D renames the default aggregate column name which is less readable but also works correctly. Option E, 'mergeSchema' isn't a valid option to be passed.
NEW QUESTION # 249
A Snowpark application needs to manage multiple sessions concurrently, each connected to a different Snowflake warehouse for resource isolation. The application receives warehouse names dynamically at runtime. How can the application efficiently create and manage these sessions while ensuring proper resource cleanup?
Answer: D
Explanation:
Option B is the most reliable and recommended way to manage multiple sessions. It ensures resource isolation by creating separate sessions per warehouse. Closing each session properly, especially using a 'finally' block or context manager, prevents resource leaks and ensures sessions are closed even if errors occur. Option A can lead to unpredictable behavior if multiple threads or asynchronous tasks interact with the global session. Options C, D and E are generally incorrect or don't properly manage resource cleanup or isolation.
NEW QUESTION # 250
A UDTF (User-Defined Table Function) named 'split_sentences" takes a text string as input and returns a table with each row containing a single sentence from the input. You need to grant SELECT privilege on this UDTF to a specific role, 'DATA ANALYST'. Which of the following SQL statements will achieve this?
Answer: D
Explanation:
Since UDTFs return a table, you need to grant the 'SELECT privilege on the table function to allow the role to query the results produced by the UDTF. The syntax is 'GRANT SELECT ON TABLE FUNCTION'. option A is wrong it's for UDE Options C and E provide too many privileges, potentially allowing unintended modifications. Option D is not the correct Privilege.
NEW QUESTION # 251
A data science team wants to operationalize a Snowpark Python UDF that performs sentiment analysis on customer reviews. The UDF, 'analyze sentiment(review_text)', is currently defined within a Snowpark session. Which of the following approaches is the MOST efficient and scalable way to deploy this UDF for real-time scoring of incoming review data in a Snowflake table named 'CUSTOMER REVIEWS'?
Answer: A
Explanation:
Registering the UDF as a persistent UDF in Snowflake allows it to be called directly from SQL, leveraging Snowflake's query engine for optimal performance and scalability. Option A keeps the transformation entirely within Snowpark, which is a valid approach, but less scalable. Option C involves converting to a Pandas DataFrame, which moves data outside of Snowflake and negates its benefits. Option D creates a stored procedure that is correct but the question ask to perform in real time so it's not ideal. Option E is not the way to persist Snowpark with UDF.
NEW QUESTION # 252
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