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Snowflake DEA-C02 Exam Syllabus Topics:

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

1. A data engineering team is using Snowflake's data lineage features, and they need to audit changes to data masking policies applied to a table named 'EMPLOYEES'. They want to identify when a masking policy was added, modified, or removed from specific columns.
What are the recommended Snowflake features or audit logs that the data engineering team could use to get these requirements?

A) The 'OBJECT DEPENDENCIES' view in the ACCOUNT USAGE schema will directly track changes related to masking policies applied to tables since that is the best place for lineage information.
B) Snowflake event tables provide complete audit trail capabilities. These tables capture all the events including policies.
C) Snowflake's native Data Lineage feature automatically captures all changes to data masking policies without any additional configuration, and those changes are then available to the data steward through the user interface.
D) The 'INFORMATION SCHEMA.POLICY REFERENCES view to determine what masking policies are currently in place. Then, combine that with the use of Snowflake's Alerting framework to get notified on the creation/removal of tables, and also on changes on the masking policies via SYSTEM$GET_PRIVILEGES() function.
E) The Account Usage view 'POLICY REFERENCES coupled with 'QUERY HISTORY, filtering for 'ALTER TABLE MODIFY COLUMN SET MASKING POLICY statements and also comparing snapshots of the 'POLICY_REFERENCES' view over time.

2. You have an external table named in Snowflake that points to a set of CSV files in an AWS S3 bucket. The CSV files have a header row, and the data is comma-separated. However, some of the files in the S3 bucket are gzipped. You need to define the external table to correctly read both compressed and uncompressed files. Which of the following SQL statements BEST achieves this?

A) Option A
B) Option D
C) Option E
D) Option C
E) Option B

3. A data engineering team is implementing Row Access Policies (RAP) on a table 'employee_data' containing sensitive salary information. They need to ensure that only managers can see the salary information of their direct reports. A user-defined function (UDF) 'GET returns a comma-separated string of manager usernames for a given username. Which of the following SQL statements correctly creates and applies a RAP to achieve this?

A) Option A
B) Option D
C) Option E
D) Option C
E) Option B

4. You are developing a Secure UDF in Snowflake to encrypt sensitive customer data'. The UDF should only be accessible by authorized roles. Which of the following steps are essential to properly secure the UDF?

A) Setting the 'SECURITY INVOKER clause when creating the UDF to execute the UDF with the privileges of the caller.
B) Ensuring that the UDF is owned by a role with appropriate permissions and limiting access to this role.
C) Granting the EXECUTE privilege on the UDF only to the roles that require access.
D) Using masking policies instead of Secure UDFs is the recommended approach for data security
E) Using the 'SECURE keyword when creating the UDF to prevent viewing the UDF definition.

5. 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) 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.
B) 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.
C) 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;'
D) 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.
E) 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.

Solutions: