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

1. 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) 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.
C) 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.
D) Materialize the historical data into a separate table that utilizes clustering and indexing for faster query performance. Refresh this table periodically.
E) 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.

2. You have created a secure external function that uses a Snowflake secret to retrieve a cryptographic key and mask sensitive data'. However, users are still able to see the sensitive data'. Which of the following actions is MOST likely to resolve the issue?

A) Ensure that the user has the 'USAGE privilege on the database and schema where the secret is stored.
B) Verify that the user has the 'OWNERSHIP privilege on the table containing the sensitive data.
C) Grant the ' SELECT privilege on the Snowflake secret object to the user.
D) Ensure that the user calling the function has the 'USAGE privilege on the security integration associated with the external function.
E) Grant the 'EXECUTE FUNCTION' privilege on the function to the user.

3. You have a Snowflake Stream named 'ORDERS STREAM' on an 'ORDERS' table, which is used to incrementally load data into a historical orders table named 'HISTORICAL ORDERS'. The data pipeline involves a series of tasks: 1) Consume changes from the 'ORDERS STREAM', 2) Apply transformations and data quality checks, and 3) Merge the changes into 'HISTORICAL ORDERS' using a MERGE statement. After a recent data load, you notice that the 'HISTORICAL ORDERS' table contains duplicate records for certain 'ORDER values. The MERGE statement uses 'ORDER ID' as the matching key. You have confirmed that the transformation logic is correct and idempotent. Examine the MERGE statement below. What could be causing the duplicates, given the context of Streams and incremental loading?

A) The MERGE statement is not correctly handling updates and deletes from the stream. The 'WHEN NOT MATCHED' and 'WHEN MATCHED' clauses are not mutually exclusive, leading to potential insertions of duplicate rows.
B) The stream's or 'BEFORE clause is being used incorrectly, potentially rewinding the stream to an earlier point in time.
C) Multiple tasks are concurrently consuming from the same 'ORDERS STREAM' without proper coordination, causing records to be processed multiple times.
D) The stream is not configured to capture DELETE operations from the ORDERS table, causing records that should have been removed in HISTORICAL ORDERS to remain.
E) The 'ORDERS STREAM' is retaining historical data beyond the data retention period, causing older records to be re-processed.

4. A Snowflake table 'ORDERS' is clustered on the 'ORDER DATE column. After several months, you notice that many micro-partitions contain data from a wide range of 'ORDER DATE values, and query performance on date range filters is degrading. Which of the following actions could improve performance and reduce the overlap in micro-partitions?

A) Change the ORDER DATE column to VARCHAR and then cluster.
B) Increase the virtual warehouse size used for loading data into the "ORDERS' table.
C) Run 'OPTIMIZE TABLE ORDERS' to recluster the table.
D) Change the clustering key to a composite key including ORDER DATE and another relevant column.
E) Drop and recreate the table with a different clustering key.

5. You are developing a Snowpark Python application that reads data from a large Snowflake table, performs several transformations, and then writes the results back to a new table. You notice that the write operation is taking significantly longer than the read and transformation steps. The target table is not clustered. Which of the following actions, either individually or in combination, would likely improve the write performance most significantly ?

A) Disable auto-tuning for the warehouse to ensure consistent performance
B) Increase the size of the Snowflake warehouse used for the Snowpark session.
C) Use the 'DataFrame.repartition(numPartitions)' method before writing to the table. Choose a 'numPartitionS value that is significantly higher than the number of virtual warehouses in your warehouse size.
D) Use the FILE SIZE', value)' method to reduce the size of the output files, potentially leading to more parallelism during the write operation.
E) Cluster the target table on the primary key before writing to it. Then, ensure the data being written is pre-sorted according to the clustering key.

Solutions: