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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Snowpark Concepts and Architecture | 25% | - Snowpark architecture and execution model
|
| Topic 2: Performance and Best Practices | 10% | - Security and governance
|
| Topic 3: Data Transformations and Operations | 35% | - DataFrame manipulation
|
| Topic 4: Snowpark API and Development | 30% | - Python API fundamentals
|
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NEW QUESTION # 44
You have a Snowpark DataFrame named and want to create a stored procedure that calculates the average purchase amount for each customer. The stored procedure should accept the DataFrame as input, perform the aggregation, and return a new DataFrame with the results. Which of the following code snippets BEST demonstrates how to correctly define and deploy this stored procedure?
Answer: C
Explanation:
Option E is the most correct. It correctly registers the stored procedure using 'session.sproc.register' with the correct return type annotation for a DataFrame (using TableType and StructFields) and explicitly declares the input type to be DataFrame using 'input_types' parameter in registration. Option A uses the '@sproc' decorator which is the older API (still valid, but less explicit). Option B uses session.sql which is not appropriate for creating DataFrame based stored procedures. Option C is not correct because the return type SeqType(FloatType()) expects a sequence of float types instead of a DataFrame structure. Option D writes the result to a table, which is not the defined requirement to return a DataFrame.
NEW QUESTION # 45
You have a complex data pipeline implemented using Snowpark Tasks in a Directed Acyclic Graph (DAG). One of the tasks, , depends on the successful completion of two parent tasks, and 'task B'. You need to implement error handling such that if 'task_R fails, 'task_C' should not be executed, but should still complete its execution regardless of status. If 'task B' fails, 'task_C' should not be executed. How do you configure the task dependencies and error handling in Snowflake to achieve this behavior?
Answer: B
Explanation:
Setting to execute 'AFTER task_A, task_B' establishes the dependency. Configuring on both 'task_R and 'task_B' ensures that if either task fails, it will suspend itself and prevent 'task_C' from running. This achieves the required behavior. Option A is incorrect because setting the parameter on 'task_C' will not prevent the execution of 'task_C' if either 'task_R or failed, but only suspend it after it tries to execute and fails. Option B is not correct because Snowflake's default behavior does not inherently skip 'task_C' unless specifically configured through dependencies. Option C is overly complex and doesn't accurately reflect intended behaviour.Option E is overly complex and doesn't accurately reflect intended behaviour. Option D will ensure only if both 'task_A' and runs successfully then runs else it will suspend the further execution.
NEW QUESTION # 46
You are developing a Snowpark stored procedure to process PDF files stored in a Snowflake stage. You need to extract text from these PDF files and store the extracted text in a Snowflake table. Due to security requirements, you cannot use any external packages that require internet access. Which of the following approaches can you use to accomplish this task securely and efficiently? (Select all that apply)
Answer: A,B
Explanation:
Options B and C are correct. Option B: Java UDFs allow you to leverage existing Java libraries (like PDFBox, which can be included in the UDF's JAR file) to parse PDFs securely within the Snowflake environment. Option C: Using and a pure-Python PDF parsing library (which doesn't require external network access) is another viable approach. The entire library's code must be embedded within the stored procedure. Option A is incorrect because Snowflake does not have built-in PDF parsing functions. Option D is not ideal as you are trying to avoid any external dependencies and internet access. Option E, although workable, adds an external preprocessing step which isn't the most efficient way.
NEW QUESTION # 47
Consider two Snowpark DataFrames, 'employees' and 'departments' , with the following schemas: 'employees': (employee_id: Integer Type, employee_name: StringType, department_id: Integer Type, salary: IntegerType) 'departments': (department_id: Integer Type, department_name: StringType, location: StringType) You want to find the highest salary within each department, along with the department name and location, and display the results in a Snowpark DataFrame. Which of the following Snowpark Python code snippets correctly achieves this?
Answer: D
Explanation:
Option A correctly groups the 'employees' DataFrame by 'department_id' , calculates the maximum salary for each department using and then joins the resulting DataFrame with the 'departments' DataFrame on Finally, it selects the required columns: department name, location, and the calculated maximum salary. Option B is not correct as it calculates the rank of all salaries within each department but it does not aggregate on 'department_id'. Option C incorrectly attempts to group by columns from both tables before joining. Option D incorrectly uses the un-joined employees table where rank_salaries are from department_id. Option E will not work due to table names during select. It tries to use 'joined_df table for columns which doesn't have it.
NEW QUESTION # 48
You are building a Snowpark application using the 'snowflake-cli' to manage Snowflake connections. You have configured multiple connection profiles using 'snowflake connection add'. Which of the following Python code snippets demonstrates the most efficient and idiomatic way to create a Snowpark session using a specific connection profile named 'my_profile' defined in your 'snowflake-cli' configuration? Assume snowflake-cli is correctly configured and authenticated.
Answer: D
Explanation:
The correct answer is D. is the most direct and idiomatic way to create a Snowpark session from a named connection profile defined in the 'snowflake-cli' configuration. This avoids manually parsing connection details or using environment variables. Option A uses , which is for loading connection parameters from a .env' file, not a snowflake-cli profile. Option B relies on environment variables, not the 'snowflake-cli' profile. Option C is overly complex, using 'subprocess' to execute the 'snowflake-cli' when Snowpark provides a direct method. Option E is incorrect because the 'profile' parameter is not a valid parameter for 'snowflake.connector.connect'
NEW QUESTION # 49
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