Snowflake DEA-C02

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

1. You are developing a data pipeline in Snowflake that processes sensitive customer data'. You need to implement robust data governance controls, including column-level security and data masking. Which of the following combinations of Snowflake features, when used together, provides the MOST comprehensive solution for achieving this?

A) Row-level security policies and data masking policies.
B) Dynamic tables and masking policies.
C) Data masking policies and network policies.
D) Object tagging, column-level security policies (using views), and masking policies.
E) Row access policies and data masking policies on base tables, supplemented with object tagging and column-level security policies on views that grant limited access to specific user roles.

2. You have implemented a Snowpipe using auto-ingest to load data from an AWS S3 bucket. The pipe is configured to load data into a table with a 'DATE column ('TRANSACTION DATE'). The data files in S3 contain a date field in the format 'YYYYMMDD'. Occasionally, you observe data loading failures in Snowpipe with the error message indicating an issue converting the string to a date. The 'FILE FORMAT' definition includes 'DATE FORMAT = 'YYYYMMDD''. Furthermore, you are also noticing that after a while, some files are not being ingested even though they are present in the S3 bucket. How to effectively diagnose and resolve these issues?

A) The issue may arise if the time zone of the Snowflake account does not match the time zone of your data in AWS S3. Try setting the 'TIMEZONE parameter in the FILE FORMAT definition. For files that are not being ingested, manually refresh the Snowpipe with 'ALTER PIPE ... REFRESH'.
B) The error could be due to invalid characters in the source data files. Implement data cleansing steps to remove invalid characters from the date fields before uploading to S3. For files not being ingested, check S3 event notifications for missing or failed events.
C) Snowflake's auto-ingest feature has limitations and may not be suitable for inconsistent data formats. Consider using the Snowpipe REST API to implement custom error handling and data validation logic. Monitor the Snowflake event queue to ensure events are being received.
D) Verify that the 'DATE FORMAT is correct and that all files consistently adhere to this format. Check for corrupted files in S3 that may be preventing Snowpipe from processing subsequent files. Additionally, review the Snowpipe error notifications in Snowflake to identify the root cause of ingestion failures. Use 'SYSTEM$PIPE to troubleshoot the files not ingested
E) The 'DATE FORMAT parameter is case-sensitive. Ensure it matches the case of the incoming data. Also, check the 'VALIDATION MODE and ERROR parameters to ensure error handling is appropriately configured for files with date format errors. For the files that are not ingested use 'SYSTEM$PIPE to find the cause of the issue.

3. A Snowflake data warehouse is experiencing performance degradation. A critical reporting dashboard, which relies on a complex SQL query, is taking significantly longer to execute. You've examined the query profile and identified that a user-defined function (UDF) called 'PARSE ADDRESS, written in Python, is consuming a large portion of the execution time. The 'PARSE ADDRESS UDF is used to parse address strings into individual components (street, city, state, zip). What are the most effective strategies to improve the performance of this query, assuming it's difficult to rewrite the query without the UDF?

A) Utilize external functions with a more performant compute service (e.g. AWS Lambda) if the environment allows.
B) Increase the virtual warehouse size to compensate for the UDF's overhead.
C) convert the Python UDF 'PARSE_ADDRESS' into a Java UDE
D) Rewrite the Python UDF 'PARSE ADDRESS in SQL using Snowflake's built-in string functions.
E) Create a materialized view that pre-calculates the output of the 'PARSE ADDRESS UDF for frequently queried address values and joins with the original table.

4. Consider a table 'EVENT DATA' that stores events from various applications. The table has columns like 'EVENT ID, 'EVENT TIMESTAMP, 'APPLICATION ID', 'USER ID', and 'EVENT _ TYPE. A significant portion of queries filter on 'EVENT TIMESTAMP ranges AND 'APPLICATION ID. The data volume is substantial, and query performance is crucial. You observe high clustering depth after initial loading. Which combination of actions will provide the MOST effective performance optimization, addressing both clustering depth and query performance?

A) Cluster the table on 'EVENT TIMESTAMP' and periodically run 'OPTIMIZE TABLE EVENT DATA' using a small warehouse. Also, create a separate table clustered on 'APPLICATION
B) Create multiple materialized views: one filtering on common 'EVENT TIMESTAMP' ranges, and another filtering on common 'APPLICATION ID' values.
C) Cluster the table on '(EVENT TIMESTAMP, APPLICATION IDY and periodically run 'OPTIMIZE TABLE EVENT DATA' using a warehouse sized appropriately for the table size. Then, monitor clustering depth regularly.
D) Cluster the table on 'USER ICY and rely solely on Snowflake's automatic reclustering feature, without running 'OPTIMIZE TABLES manually.
E) Create separate tables for each ' , each clustered on 'EVENT_TIMESTAMP'. Then, create a view that UNION ALLs these tables.

5. A Snowflake data warehouse contains a table named 'SALES TRANSACTIONS' with the following columns: 'TRANSACTION ID', 'PRODUCT D', 'CUSTOMER D', 'TRANSACTION DATE, and 'SALES AMOUNT'. You need to optimize a query that calculates the total sales amount per product for a given month. The 'SALES TRANSACTIONS' table is very large (billions of rows), and queries are slow. Given the following initial query: SELECT PRODUCT ID, SUM(SALES AMOUNT) AS TOTAL SALES FROM SALES TRANSACTIONS WHERE TRANSACTION DATE BETWEEN '2023-01-07' AND '2023-01-31' GäOUP BY PRODUCT ID; Which of the following actions, when combined, would MOST effectively improve the performance of this query?

A) Create a clustering key on 'PRODUCT_ID and 'TRANSACTION_DATE columns in the 'SALES_TRANSACTIONS' table.
B) Create a temporary table with the results of the query and query that table instead.
C) Increase the virtual warehouse size to the largest available size.
D) Convert the column to a VARCHAR data type.
E) Create a materialized view that pre-aggregates the total sales amount per product and month.

Solutions: