Snowflake DEA-C02 Exam : SnowPro Advanced: Data Engineer (DEA-C02)

You are tasked with designing a data pipeline that ingests JSON data from an external stage (AWS S3). The JSON files contain records for various product types, each having a different set of attributes. Some product types might have attributes that are not present in other types. You want to create a single Snowflake table that can accommodate all product types without defining a rigid schema upfront and also be queryable efficiently. Which of the following approaches, combining external tables, schema evolution and querying, would be MOST effective? (Choose two)

• A. Load all the data into a raw Snowflake internal table. Use dynamic SQL to infer distinct product types and create views on top of the raw table for each product type.
• B. Create a single external table with a VARIANT column to store the entire JSON record for each product. Use LATERAL FLATTEN to extract specific attributes during querying.
• C. Create a stored procedure that dynamically infers the schema from the JSON files and creates a new Snowflake table based on the inferred schema.
• D. Create a separate external table for each product type, defining the schema for each table based on the attributes present in the corresponding JSON files.
• E. Create a single external table with a VARIANT column and use the 'VALIDATE function to identify and handle schema inconsistencies during data loading.

You are building a data pipeline using Snowflake Tasks to orchestrate a series of transformations. One of the tasks, 'task _ transform data', depends on the successful completion of another task, 'task extract_data'. However, occasionally fails due to transient network issues. You want to implement a retry mechanism for 'task_extract data' without impacting the overall pipeline execution time significantly. Which of the following approaches is the most appropriate and efficient way to achieve this within the Snowflake Task framework?

• A. Configure the task with an error notification integration that sends alerts upon failure. Manually monitor these alerts and manually resume the task if it fails. Use 'ALTER TASK task extract data RESUME;'
• B. Implement a TRY...CATCH block within the task definition to catch any exceptions. Inside the CATCH block, use SYSTEM$WAIT to pause for a few seconds, then re- execute the core logic of the task. Repeat this process a limited number of times before failing the task permanently.
• C. Modify the task definition to call a stored procedure. The stored procedure implements a loop with a retry counter. Inside the loop, execute the data extraction logic. If an error occurs, catch the exception, wait for a few seconds, and retry the extraction. After a specified number of retries, raise an exception to signal task failure.
• D. Create a new root-level task that checks the status of 'task_extract_data'. If it failed, the root-level task will execute a copy of the 'task_extract data' task. After this, it updates the 'task_transform_data"s 'AFTER' condition to depend on the new task that retries extraction.
• E. Use the 'AFTER keyword in the 'CREATE TASK' statement for 'task_transform_data' to only execute if succeeds on its first attempt. If fails, the entire pipeline will stop, ensuring data consistency.

Snowpark DataFrame 'employee_df' contains employee data, including 'employee_id', 'department', and 'salary'. You need to calculate the average salary for each department and also retrieve all the employee details along with the department average salary.
Which of the following approaches is the MOST efficient way to achieve this?

• A. Use a correlated subquery within the SELECT statement to calculate the average salary for each department for each employee.
• B. Use 'groupBV to get a dataframe containing average salary by department and then use a Python UDF to iterate through the 'employee_df and add the value to each row
• C. Create a separate DataFrame with average salaries per department, then join it back to the original DataFrame.
• D. Use the 'window' function with 'avg' to compute the average salary per department and include it as a new column in the original DataFrame.
• E. Create a temporary table with average salaries per department, then join it back to the original DataFrame.

You are building a data pipeline in Snowflake using Snowpark Python. As part of the pipeline, you need to create a dynamic SQL query to filter records from a table named 'PRODUCT REVIEWS based on a list of product categories. The list of categories is passed to a stored procedure as a string argument, where categories are comma separated. The filtered data needs to be further processed within the stored procedure. Which of the following approaches are MOST efficient and secure ways to construct and execute this dynamic SQL query using Snowpark?

• A. Using Python's string formatting along with the and 'session.sql()' functions to build and execute the SQL query securely, avoiding SQL injection vulnerabilities.
• B. Using Python's string formatting to build the SQL query directly, and then executing it using 'session.sql()'.
• C. Constructing the SQL query using 'session.sql()' and string concatenation, ensuring proper escaping of single quotes within the product categories string.
• D. Using Snowpark's on the list of product categories after converting them into a Snowflake array, and then using 'session.sql()' to execute the query.
• E. Using the Snowpark "functions.lit()' function to create literal values from the list of product categories and incorporating them into the SQL query, then use 'session.sql()' to run it.

You have a table 'EMPLOYEE DATA' containing Personally Identifiable Information (PII), including 'salary' and 'email'. You need to implement column-level security such that: 1) The 'salary' column is only visible to users in the 'FINANCE ROLE. 2) The 'email' column is masked with a SHA256 hash for all users except those in the 'HR ROLE. You create the following masking policies:

Which of the following SQL statements correctly applies these masking policies to the 'EMPLOYEE DATA table?

• A. CREATE OR REPLACE TAG employee_data.salary VALUE 'mask_salary'; CREATE OR REPLACE TAG employee_data.email VALUE 'mask_email';
• B. ALTER TABLE EMPLOYEE_DATA MODIFY COLUMN salary SET MASKING POLICY = mask_salary; ALTER TABLE EMPLOYEE_DATA MODIFY COLUMN email SET MASKING POLICY = mask email;
• C. ALTER TABLE EMPLOYEE_DATA MODIFY COLUMN salary SET MASKING POLICY mask_salary; ALTER TABLE EMPLOYEE_DATA MODIFY COLUMN email SET MASKING POLICY mask email;
• D. ALTER TABLE EMPLOYEE_DATAAPPLY MASKING POLICY mask_salary ON COLUMN salary; ALTER TABLE EMPLOYEE_DATAAPPLY MASKING POLICY mask email ON COLUMN email;
• E. ALTER TABLE EMPLOYEE_DATAALTER COLUMN salary SET MASKING POLICY mask_salary; ALTER TABLE EMPLOYEE_DATAALTER COLUMN email SET MASKING POLICY mask email;