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

1. A data engineering team is loading a large fact table 'SALES DATA' daily, partitioned by 'SALE DATE. After several months, query performance degrades significantly. An analyst reports that queries filtering on 'CUSTOMER are slow, despite 'CUSTOMER ID' having high cardinality. The table definition is as follows: CREATE TABLE SALES_DATA ( SALE DATE DATE NOT NULL, CUSTOMER_ID NUMBER NOT NULL, PRODUCT ID NUMBER NOT NULL, SALE_AMOUNT ... Which of the following actions would BEST improve query performance for queries filtering on 'CUSTOMER ID', considering the existing partitioning by 'SALE DATE'?

A) Create a secondary index on 'CUSTOMER ID'
B) Create a materialized view that aggregates data by 'CUSTOMER_ID and relevant dimensions.
C) Increase the virtual warehouse size.
D) Partition the table by 'CUSTOMER_ID instead of 'SALE_DATE.
E) Cluster the 'SALES DATA' table on 'CUSTOMER ID.

2. You are responsible for monitoring a critical data pipeline that loads data from an external Kafka topic into a Snowflake table 'ORDERS' Data anomalies have been frequently observed, impacting downstream reporting. You want to implement a solution that proactivelyidentifies and alerts on data quality issues such as missing values, invalid formats, and unexpected data distributions. Which combination of Snowflake features and approaches would be MOST effective for achieving this objective with minimal performance overhead on the pipeline itself?

A) Employing Snowflake's built-in statistics and histogram features to analyze data distribution in the 'ORDERS' table and configure alerts based on deviations from historical patterns, combined with a Snowflake Native App for data quality reporting.
B) Creating a separate Snowflake pipeline that reads from the same Kafka topic, performs data quality checks in real-time using Snowpipe and streams the results to an alert system.
C) Using Snowflake's 'VALIDATE' table function after the data load to check for data corruption and then trigger alerts based on the validation results.
D) Leveraging Snowflake's Data Governance features along with Snowpark UDFs to define and enforce data quality rules at the time of ingestion using a Python- based library like Great Expectations, configured to trigger alerts through Snowflake Notifications.
E) Implementing custom SQL-based data quality checks within a scheduled Snowflake task that runs after the data load and writing results to an audit table for monitoring.

3. You are developing a JavaScript UDF in Snowflake to perform complex data validation on incoming data'. The UDF needs to validate multiple fields against different criteria, including checking for null values, data type validation, and range checks. Furthermore, you need to return a JSON object containing the validation results for each field, indicating whether each field is valid or not and providing an error message if invalid. Which approach is the MOST efficient and maintainable way to structure your JavaScript UDF to achieve this?

A) Create separate JavaScript functions for each validation check (e.g., 'isNull', 'isValidType', 'isWithinRange'). Call these functions from the main UDF and aggregate the results into a JSON object.
B) Directly embed SQL queries within the JavaScript UDF to perform data validation checks using Snowflake's built-in functions. Return a JSON string containing the validation results.
C) Define a JavaScript object containing validation rules and corresponding validation functions. Iterate through the object and apply the rules to the input data, collecting the validation results in a JSON object. This object is returned as a string.
D) Utilize a JavaScript library like Lodash or Underscore.js within the UDF to perform data manipulation and validation. Return a JSON string containing the validation results.
E) Use a single, monolithic JavaScript function with nested if-else statements to handle all validation logic. Return a JSON string containing the validation results.

4. You have a complex data pipeline implemented using Snow park Python. The pipeline involves multiple Data Frame transformations, joins, aggregations, and window functions. To enhance the maintainability and readability of the code, you want to modularize the pipeline into reusable functions. You also need to handle potential errors and exceptions gracefully. Consider the following code snippet:

A)

B)

C)

D)

5. A data engineer is tasked with optimizing a Snowflake data pipeline that ingests data from multiple external sources, transforms it, and loads it into a reporting table. The pipeline uses a series of Snowflake tasks orchestrated with a root task and child tasks. Performance monitoring shows inconsistent execution times for the transformation tasks. Which of the following strategies would provide the MOST granular insights into the performance bottlenecks within the pipeline and allow for targeted optimization?

A) Enable query profiling for all queries executed within the transformation tasks using 'ALTER SESSION SET QUERY PROFILE = 'ON" , then analyze the query profiles for performance bottlenecks after each task run.
B) Implement a custom logging mechanism within the transformation tasks to record execution times for each stage of the transformation process, and store these logs in a Snowflake table for analysis.
C) Rely solely on the Snowflake web UI's Task History view to identify slow-running tasks.
D) Leverage Snowflake's event tables like QUERY HISTORY and TASK HISTORY in the ACCOUNT USAGE schema joined with custom metadata tags to correlate specific transformation steps to execution times and resource usage. Also set up alerting based on defined performance thresholds.
E) Use Snowflake's Resource Monitors to track overall warehouse consumption and assume that high consumption during transformation tasks indicates a bottleneck within those tasks.

Solutions: