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Snowflake DEA-C02 Exam Syllabus Topics:

Snowflake SnowPro Advanced: Data Engineer (DEA-C02) Sample Questions:

1. Your company utilizes Snowflake Streams and Tasks for continuous data ingestion and transformation. A critical task, 'TRANSFORM DATA', consumes data from a stream 'RAW DATA STREAW on table 'RAW DATA' and loads it into a reporting table 'REPORTING TABLE. You observe that 'TRANSFORM DATA is failing intermittently with a 'Stream is stale' error. What steps can you take to diagnose and resolve this issue? Choose all that apply.

A) Ensure that the ' TRANSFORM DATA' task is consuming the stream data frequently enough to prevent the stream from becoming stale.
B) Modify the task definition to use the 'WHEN condition to prevent execution when the stream is empty.
C) Use the "AT' or 'BEFORE clause when querying the stream to explicitly specify a point in time to consume data from.
D) Drop and recreate the stream and task to reset their states.
E) Increase the parameter at the database level to ensure Time Travel data is available for a longer period.

2. You are performing a series of complex data transformations on a large table named 'TRANSACTIONS' in Snowflake. After running several DML statements, you realize that an earlier transformation step introduced incorrect data into the table. You want to rollback the table to a state before that specific transformation occurred. Which of the following methods could be used to achieve this rollback, assuming you know the exact timestamp or query ID of the state you want to revert to? Select all that apply.

A) Create a new table with the correct data and load from the original table filtered by a range of transaction IDs excluding the incorrect range.
B) Restore the entire Snowflake account to a point in time before the incorrect transformation.
C) Use Time Travel to query the historical version of the 'TRANSACTIONS' table using the 'AT' or 'BEFORE clause with either the timestamp or query ID. Then, use 'INSERT OVERWRITES or ' REPLACE TABLES statement to replace the current content of the original table with the historical data.
D) Use the UNDROP TABLE command if the table was dropped accidentally, then manually re-apply the correct transformations.
E) Create a clone of the ' TRANSACTIONS' table using Time Travel, specifying the 'AT' or 'BEFORE clause with either the timestamp or query ID of the desired state. Then, replace the original table with the cloned table.

3. A data engineer wants to use Snowpark to read a large CSV file from an external stage and infer the schema automatically. However, some columns in the CSV contain data that Snowflake cannot automatically infer the type for. Which of the following code snippets demonstrates the CORRECT way to read the CSV file with schema inference and handle potentially problematic columns by explicitly specifying their data types?

A)

B)

C)

D)

E)

4. You are tasked with implementing column-level security on the 'EMPLOYEE table to restrict access to the 'SALARY column. Only users with the 'HR ROLE' should be able to view the actual salary. All other users should see NULL. You create a masking policy as follows:

What additional steps are necessary to enforce this policy?

A) Grant OWNERSHIP on the masking policy to the HR ROL
B) Apply the masking policy to the SALARY column: ALTER TABLE EMPLOYEE MODIFY COLUMN SALARY SET MASKING POLICY salary_mask;
C) Grant the APPLY MASKING POLICY privilege to the HR_ROLE.
D) Grant the SELECT privilege on the EMPLOYEE table to the HR ROLE.
E) Apply the masking policy to the EMPLOYEE table: ALTER TABLE EMPLOYEE SET MASKING POLICY salary_mask;

5. 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) Constructing the SQL query using 'session.sql()' and string concatenation, ensuring proper escaping of single quotes within the product categories string.
B) 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.
C) 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.
D) Using Python's string formatting to build the SQL query directly, and then executing it using 'session.sql()'.
E) Using Python's string formatting along with the and 'session.sql()' functions to build and execute the SQL query securely, avoiding SQL injection vulnerabilities.

Solutions: