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Snowflake SnowPro Advanced: Data Engineer (DEA-C02) Sample Questions (Q39-Q44):
NEW QUESTION # 39
You are developing a data pipeline in Snowflake that uses SQL UDFs for data transformation. You need to define a UDF that calculates the Haversine distance between two geographical points (latitude and longitude). Performance is critical. Which of the following approaches would result in the most efficient UDF implementation, considering Snowflake's execution model?
Answer: C
Explanation:
SQL UDFs are generally the most efficient for simple calculations within Snowflake because they are executed within the Snowflake engine, minimizing data movement and overhead. While Java UDFs (option B) can offer optimizations, the overhead of invoking the Java environment often outweighs the benefits for this type of calculation. External Functions (option C) introduce significant latency due to network communication. Option D provides temporary performance improvements for the specific session, but is not the most efficient general solution. Vectorized keyword doesn't exists in snowflake to create UDFs, Hence it won't allow compilation. This questions emphasis on understanding the trade-offs between different UDF types and their performance implications within the Snowflake architecture.
NEW QUESTION # 40
You need to implement both a row access policy and a dynamic data masking policy on the 'EMPLOYEE table in Snowflake. The requirements are as follows: 1. Employees should only be able to see their own record in the 'EMPLOYEE table. 2. The 'SALARY' column should be masked for all employees except those with the 'HR ADMIN' role. Unmasked values are required for compliance reasons, they need to be available for 'HR ADMIN' role. Given the following table structure: CREATE TABLE EMPLOYEE ( EMPLOYEE ID INT, EMPLOYEE NAME STRING, SALARY NUMBER, EMAIL STRING ) ; Which of the following sets of steps correctly implement the row access policy and dynamic data masking policy?
Answer: B
Explanation:
Option B implements both policies correctly. The row access policy correctly checks if the 'EMPLOYEE ID matches the 'CURRENT_USER()'. Although the use of is not correct in this situation, it is being used with 'employee_id' so can only see his own record in the 'EMPLOYEE table. The masking policy uses 'CURRENT correctly to check if the role in the session is 'HR_ADMIN'. If it is, the original salary value is returned; otherwise, it masks it to Other masking policy options will return a string representation ("MASKED") or return a hash of the value, which is not a valid 'NUMBER. Option A uses IS ROLE IN SESSION rather than CURRENT_ROLE. 'CURRENT_ROLE only returns the primary role used to initialize the session whereas will return TRUE if the role is the primary role or any of the active secondary roles in the current session.
NEW QUESTION # 41
A data engineer is tasked with creating a Snowpark Python UDF to perform sentiment analysis on customer reviews. The UDF, named 'analyze_sentiment' , takes a string as input and returns a string indicating the sentiment ('Positive', 'Negative', or 'Neutral'). The engineer wants to leverage a pre-trained machine learning model stored in a Snowflake stage called 'models'. Which of the following code snippets correctly registers and uses this UDF?
Answer: B
Explanation:
The most concise and recommended way to define a Snowpark UDF in Python is using the @F.udf decorator. This decorator automatically handles registration with Snowflake and simplifies the code. It also correctly specifies the 'return_type' , 'input_types' , and required packages'. Options A, B, C and E are either missing the decorator or have issues with specifying input types or session usage. The session.add_packageS is not a proper way to define packages used by UDFs and 'StringType' is not imported from 'snowflake.snowpark.types , so the correct way is to set return_type and input_types within the decorator.
NEW QUESTION # 42
You're designing a Snowpark data transformation pipeline that requires running a Python function on each row of a large DataFrame. The Python function is computationally intensive and needs access to external libraries. Which of the following approaches will provide the BEST combination of performance, scalability, and resource utilization within the Snowpark architecture?
Answer: A,C
Explanation:
Options B and D are the best choices. UDFs and UDTFs allow you to leverage Snowflake's compute resources for parallel processing. The function execution happens on Snowflake's servers, close to the data, minimizing data transfer. By specifying 'packages=['my_package']' , you ensure that the external libraries are available in the execution environment. A UDF is suitable for one-to-one row transformations, while a UDTF is more appropriate if the Python function needs to return multiple rows for each input row (one-to-many). Option A, DataFrame.foreacW , is inefficient for large DataFrames as it processes rows sequentially. Option C, loading into Pandas, is also not ideal as it can lead to out-of-memory errors for very large DataFrames and transfers the data to the client machine. Option E, stored procedures with loops, is less scalable and efficient than UDFs or UDTFs.
NEW QUESTION # 43
You are designing a CI/CD pipeline for your Snowflake data transformations. One stage involves testing a new stored procedure that modifies several tables in your data warehouse. To ensure data integrity and proper rollback capabilities during testing in your development environment, you want to use a combination of cloning and Tme Travel. Select the option that represents the most robust strategy for testing with the ability to revert to the original state in case of failures. Choose all that apply.
Answer: A,D
Explanation:
Options B and C provide the most robust and Snowflake-native rollback capabilities. Cloning the schema provides a complete and consistent snapshot of all affected tables. Time Travel offers fine-grained control for reverting individual tables to a specific point in time. Option A is less efficient as you would need to manage numerous clones and track them manually which would impact the development. Option D 'UNDROP TABLE' command is for objects that have been dropped already, it is not relevant here. Cloning database in Option E is too resource intensive for regular testing within a CI/CD pipeline.
NEW QUESTION # 44
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