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Snowflake DEA-C02 Exam Syllabus Topics:

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

1. A data engineer observes that a Snowflake query, used for generating a daily sales report, consistently runs slower each day, despite the dataset size remaining relatively stable. The query joins a large sales table (SALES) with a smaller product dimension table (PRODUCT) on PRODUCT ID. You've already confirmed that virtual warehouse sizing is adequate and data clustering is enabled on SALES(SALE DATE). Analyze the following scenarios and identify the MOST likely cause of the performance degradation and potential solution:

A) The Snowflake query optimizer is selecting a suboptimal execution plan because the query is complex. Rewrite the query using temporary tables to break down the logic into smaller steps.
B) The virtual warehouse is being overwhelmed by concurrent queries. Implement workload management rules to prioritize the sales report query.
C) The data clustering on SALES(SALE DATE) is ineffective as the query also filters on PRODUCT CATEGORY, which isn't clustered. Re-cluster SALES by both SALE DATE and PRODUCT CATEGORY.
D) The join between SALES and PRODUCT is causing excessive data spill to local storage due to an inefficient join order. Consider using a JOIN hint, specifically a BROADCAST hint on the PRODUCT table (small table).
E) The statistics on the PRODUCT table are outdated. Run 'ANALYZE TABLE PRODUCT' to refresh them.

2. 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) 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.
B) Use a single, monolithic JavaScript function with nested if-else statements to handle all validation logic. 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) 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.
E) 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.

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

4. Consider a scenario where you need to transform data in a Snowflake table using a complex custom transformation logic best implemented in Java'. You decide to use a Snowpark Java UDF. You've packaged your Java code into a JAR file and uploaded it to an internal stage named Which of the following steps are necessary and correctly ordered to deploy and use this Java UDF within Snowflake?

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

5. A data engineering team is building a data pipeline in Snowflake. They are using tasks and streams to incrementally load data into a fact table. The team needs to monitor the pipeline's performance and ensure data lineage. What are the valid and most effective techniques to ensure that this pipeline adheres to compliance and governance rules?

A) Leverage Snowflake's replication features for disaster recovery, monitor only the replication lag, and disable all security policies to improve performance since those tasks have already been validated during the initial deployment of the software.
B) Enable Snowflake Horizon features, which include Data Lineage, Object Dependencies and Discovery and integrate it with the data lake and also tag the data pipeline.
C) Use Account Usage views like 'TASK HISTORY and 'STREAM_LAG' to track task execution and stream latency, create stored procedures to log metadata about each pipeline run to a separate metadata table, and rely on developers to manually document the pipeline's data flow and policy enforcement.
D) Use a third-party data catalog to track lineage, monitor task performance via 'TASK_HISTORY, and ignore data masking and row-level security policies for simplicity in the initial implementation.
E) Implement Snowflake's Data Lineage and Object Dependencies features to track data flow automatically, create Alerts based on 'TASK HISTORY to monitor task failures, and enforce data masking and row-level security policies at the table level. Use Snowflake's tags to categorise and classify objects.

Solutions: