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Snowflake DEA-C02 Exam Syllabus Topics:

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

1. You are using Snowpipe to ingest data from Azure Blob Storage into a Snowflake table. You have successfully set up the pipe and configured the event notifications. However, you notice that duplicate records are appearing in your target table. After reviewing the logs, you determine that the same file is being processed multiple times by Snowpipe. Which of the following strategies can you implement to prevent duplicate data ingestion, assuming you cannot modify the source data in Azure Blob Storage to include a unique ID or timestamp?

A) Use a data masking policy with the 'MASK' function to obfuscate duplicate records based on their similarity, making them effectively invisible to downstream queries.
B) Configure the Snowpipe definition with the 'PURGE = TRUE parameter. This will ensure that each file is only processed once.
C) Create a Snowflake stream on the target table and use it to incrementally load data into a separate, deduplicated table using a merge statement with conditional logic to insert or update records based on a combination of columns.
D) Implement idempotent logic within a Snowflake stored procedure that is triggered by a task after the data is loaded by Snowpipe. The stored procedure should identify and remove duplicate rows based on all other columns in the table.
E) Modify the Azure Event Grid subscription configuration to filter events based on file size or creation time to avoid resending events for already processed files.

2. Your company is implementing data governance policies in Snowflake and wants to automatically classify data to track Personally Identifiable Information (PII). You have defined a classification policy with a tag 'PII' and associated tag values like 'Email', 'CreditCard', and 'SSN'. You want to monitor the usage of PII data'. Which of the following approaches is the MOST efficient way to track access and modifications to columns tagged with the 'PII' tag and any of its tag values?

A) Enable Snowflake's data governance features, including object tagging and data classification. Then, leverage the ACCESS HISTORY view, filtering based on POLICY _ TAGS. This will show all accesses to data tagged as PII.
B) Implement a custom UDF that intercepts all queries. Inside the UDF, check if any of the accessed tables or columns are tagged with 'PII', and log those queries into a separate logging table for monitoring.
C) Create a masking policy that redacts all PII data. Monitor access attempts via the ACCESS HISTORY view. Although it hinders data usability, this approach guarantees no PII data leakage and allows easy monitoring.
D) Create a scheduled task to query the INFORMATION SCHEMCOLUMNS view and filter based on the TAG DATABASE, TAG SCHEMA, and TAG NAME columns to identify PII columns, then query the QUERY_HISTORY view and filter based on the identified column names.
E) Use Snowflake's native data governance capabilities in conjunction with Snowflake Horizon to actively monitor data quality and access patterns, specifically focusing on PII-tagged columns.

3. Consider a scenario where you're optimizing a data pipeline in Snowflake responsible for aggregating sales data from multiple regions. You've identified that the frequent full refreshes of the target aggregated table are causing significant performance overhead and resource consumption. Which strategies could be employed to optimize these full refreshes without sacrificing data accuracy?

A) Utilize Snowflake's Time Travel feature to clone the previous version of the aggregated table, apply the necessary changes to the clone, and then swap the clone with the original table using 'ALTER TABLE SWAP WITH'. Note that this will impact data availability during the swap operation.
B) Schedule the full refreshes during off-peak hours when the Snowflake warehouse is less utilized. This minimizes the impact on other workloads but does not reduce the actual processing time.
C) Leverage Snowflake's search optimization service on the base tables. While costly, this will dramatically speed up full table scans performed in the aggregation.
D) Implement incremental data loading using streams and tasks. This allows you to only process and load the changes that have occurred since the last refresh, reducing the amount of data that needs to be processed.
E) Replace the full refresh with a 'TRUNCATE TABLE' followed by an 'INSERT statement. This approach is faster than 'CREATE OR REPLACE TABLE' and reduces locking.

4.

A) The Snowflake external function is not correctly parsing the JSON response from the Lambda function. Implement a wrapper function in Snowflake to parse the JSON and extract the discount value before returning it.
B) The Lambda function is returning a string instead of a number. Modify the Lambda function to return the discount as a number (e.g., 'discount = 0.15' instead of 'discount = '0.15")
C) The Lambda function returns the discount within a nested JSON structure Tdata': [[discount]]}'. The Snowflake function is not designed to handle this. The lambda function should return '{'data':
D) The data types in the Lambda function and Snowflake function definition do not match. Specifically, the Lambda function expects strings while Snowflake is sending numbers and vice versa. Modify the Lambda function to handle numeric inputs and ensure the Snowflake function definition aligns with the expected output data type (FLOAT).
E) The 'RETURNS NULL ON NULL INPUT clause in the external function definition is causing the function to return NULL even when valid inputs are provided. Remove this clause.

5. You are developing a Snowpark Python application that transforms data from a source table "ORDERS RAW' into a target table 'ORDERS CLEANED'. The transformation involves multiple steps, including data validation, cleansing, and aggregation. You need to ensure that either all steps succeed, or none of them do, to maintain data integrity. You are considering different approaches to transaction management. Which of the following strategies offer the MOST comprehensive and reliable approach to manage transactions in this scenario, especially considering potential network interruptions and session timeouts?

A) Leverage the 'with session.transaction_context() as transaction:' statement in Python (Snowpark 2.x or later). Inside the block perform all data transformations. If an exception is raised the context manager will automatically rollback the transaction, otherwise it will commit.
B) Rely on Snowpark's auto-commit mode and assume that each operation is atomic. If an error occurs, manually revert the changes by deleting any partially processed data.
C) Disable autocommit using 'session.autocommit = False' , perform all transformations, and then use 'session.commit()' at the end. If a network interruption occurs, the transaction will automatically be rolled back by Snowflake.
D) Implement a custom transaction management system using temporary tables. Write intermediate results to temporary tables and only copy the data to the target table if all steps succeed. Drop the temporary tables if an error occurs.
E) Explicitly start a transaction using 'session.autocommit = False' and manually commit the transaction using 'session.commit()' at the end of the process. Catch any exceptions and call 'session.rollback()' in case of an error. Implement retry logic for network interruptions.

Solutions: