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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 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).
B) 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.
C) The virtual warehouse is being overwhelmed by concurrent queries. Implement workload management rules to prioritize the sales report query.
D) The statistics on the PRODUCT table are outdated. Run 'ANALYZE TABLE PRODUCT' to refresh them.
E) 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.

2. You need to unload data from a Snowflake table named 'CUSTOMER DATA to an AWS S3 bucket The data should be unloaded in Parquet format, partitioned by the 'CUSTOMER REGION' column, and automatically compressed with GZIP. Furthermore, you only want to unload customers whose 'REGISTRATION DATE is after '2023-01-01'. Which of the following 'COPY INTO' statement correctly achieves this?

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

3. A data engineering team is building a real-time dashboard in Snowflake to monitor website traffic. The dashboard relies on a complex query that joins several large tables. The query execution time is consistently exceeding the acceptable threshold, impacting dashboard responsiveness. Historical data is stored in a separate table and rarely changes. You suspect caching is not being utilized effectively. Which of the following actions would BEST improve the performance of this dashboard and leverage Snowflake's caching features?

A) Replace the complex query with a series of simpler queries. This will reduce the amount of data that needs to be processed at any one time.
B) Materialize the historical data into a separate table that utilizes clustering and indexing for faster query performance. Refresh this table periodically.
C) Create a materialized view that pre-computes the results of the complex query. Snowflake will automatically refresh the materialized view when the underlying data changes.
D) Increase the size of the virtual warehouse. A larger warehouse will have more resources to execute the query, and the results will be cached for a longer period.
E) Use 'RESULT_SCAN' to cache the query result in the user session for subsequent queries. This is especially effective for large datasets that don't change frequently.

4. You have a large dataset stored in AWS S3 in Parquet format. The data is constantly updated by an external process, but you need to run read-only analytical queries against the most current data in Snowflake without ingesting it. Which approach is the MOST efficient and cost-effective way to achieve this, considering minimal latency for query results?

A) Create a view on top of the S3 files using a custom Java UDF to read Parquet data directly. Then, query the view.
B) Use Snowpipe to continuously ingest the Parquet files from S3 into a Snowflake table.
C) Create a Snowflake external table pointing to the S3 location. Manually refresh the external table metadata periodically using the 'ALTER EXTERNAL TABLE REFRESH' command.
D) Create a Snowflake internal stage, load the Parquet files into the stage periodically, and create a table from the staged files.
E) Create an external table in Snowflake pointing to the S3 location. Configure automatic data refresh using the 'AUTO REFRESH' parameter and a notification integration.

5. A data engineer is responsible for maintaining a Snowflake data warehouse. They notice a significant slowdown in the performance of a specific query that aggregates data from a table called 'SALES DATA', which contains billions of rows. The query is used for generating daily sales reports. The engineer suspects that the issue might be related to clustering. How would you diagnose the effectiveness of the clustering on the 'SALES DATA' table and identify potential improvements?

A) Use the 'SHOW TABLES command to view the clustering key defined on the table. Verify that the clustering key is appropriate for the query workload.
B) Examine the query profile in the Snowflake web interface to identify stages that are scanning large amounts of data. Check if these stages are benefiting from clustering.
C) Use the 'VALIDATE table command. This command detects fragmentation in the data due to poor clustering.
D) Use the 'DESCRIBE TABLE SALES_DATA' command and check the 'clustering_key' property, then run 'SELECT SYSTEM$MEASURE CLUSTERING DEPTH('SALES to check the average depth of the table. Compare the clustering depth to the number of micro- partitions to assess clustering effectiveness. A depth closer to zero is best.
E) Use the SYSTEM$CLUSTERING_INFORMATION' function to analyze the clustering depth of the table. A high clustering depth indicates poor clustering.

Solutions: