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

1. You need to load data from a stream of CSV files into a Snowflake table. The CSV files are delivered to an AWS S3 bucket and contain header rows. The files occasionally include records where a text field contains a delimiter character (comma) within the text itself, but these fields are properly enclosed within double quotes. You want to create a file format object that correctly handles the data, including quoted delimiters, and skips the header row. Which of the following file format options are required to achieve this? (Choose two)

A) FIELD OPTIONALLY ENCLOSED BY =
B) FILE_FORMAT = (TYPE = CSV)
C) FIELD DELIMITER = ','
D) SKIP HEADER = 1
E) ERROR ON COLUMN COUNT MISMATCH = FALSE

2. 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) 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.
B) 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.
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) 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.
E) 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;'

3. A data engineering team is using a Snowflake stream to capture changes made to a source table named 'orders'. They want to only capture 'INSERT and 'UPDATE operations but exclude 'DELETE operations from being captured in the stream. Which of the following configurations will achieve this requirement? Assume the stream has already been created and is named 'orders_stream'.

A) Use task and stream combination. In the task, create view using 'select from orders where metadata$isDelete = false' and create stream on that view.
B) Create a Snowflake task that periodically truncates the stream's metadata table, removing DELETE records.
C) Create a view on top of the base table that filters out deleted rows, and then create a stream on the view.
D) Alter the stream using the 'HIDE_DELETES parameter: 'ALTER STREAM orders_stream SET HIDE_DELETES = TRUE;'
E) It's impossible to configure a stream to exclude specific DML operations. All changes are always tracked.

4. You are responsible for monitoring data quality in a Snowflake data warehouse. Your team has identified a critical table, 'CUSTOMER DATA, where the 'EMAIL' column is frequently missing or contains invalid entries. You need to implement a solution that automatically detects and flags these anomalies. Which of the following approaches, or combination of approaches, would be MOST effective in proactively monitoring the data quality of the 'EMAIL' column?

A) Implement a Streamlit application connected to Snowflake that visualizes the percentage of NULL and invalid 'EMAIL' values over time, allowing the team to manually monitor trends.
B) Schedule a daily full refresh of the 'CUSTOMER DATA' table from the source system, overwriting any potentially corrupted data.
C) Use Snowflake's Data Quality features (if available) to define data quality rules for the 'EMAILS column, specifying acceptable formats and thresholds for missing values. Configure alerts to be triggered when these rules are violated.
D) Utilize an external data quality tool (e.g., Great Expectations, Deequ) to define and run data quality checks on the 'CUSTOMER DATA' table, integrating the results back into Snowflake for reporting and alerting.
E) Create a Snowflake Task that executes a SQL query to count NULL 'EMAIL' values and invalid 'EMAIL' formats (using regular expressions). The task logs the results to a separate monitoring table and alerts the team if the count exceeds a predefined threshold.

5. You are tasked with creating a SQL UDF in Snowflake to mask sensitive customer data (email addresses) before it's used in a reporting dashboard. The masking should replace all characters before the '@' symbol with asterisks, preserving the domain part. For example, '[email protected]' should become ' @example.com'. Which of the following SQL UDF definitions correctly implements this masking logic, while minimizing the impact on Snowflake compute resources?

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

Solutions: