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

1. You are implementing a data share between two Snowflake accounts. The provider account wants to grant the consumer account access to a function that returns anonymized customer data based on a complex algorithm. The provider wants to ensure that the consumer cannot see the underlying implementation details of the anonymization algorithm. Which of the following approaches can achieve this goal? (Select TWO)

A) Share the underlying table and provide the consumer account with the anonymization algorithm separately.
B) Create a view that calls the secure UDF and share that view with the consumer account.
C) Create a standard UDF in the provider account and grant usage on the UDF to the share. Share the share with the consumer account.
D) Create an external function in the provider account and grant usage to the share. Share the share with the consumer account.
E) Create a secure UDF in the provider account and grant usage on the secure UDF to the share. Share the share with the consumer account.

2. Your team is implementing Snowpipe Streaming to ingest data from multiple IoT devices into a Snowflake table. Each device sends data continuously, and it's crucial to minimize latency. However, you observe that some records are occasionally missing in the target table. You have verified that the devices are sending all data and the client-side application using Snowpipe Streaming is functioning correctly. Which of the following is the MOST likely cause of the missing records, and how can you address it?

A) The Sequence Token is not being managed correctly, leading to potential gaps or out-of-order delivery; ensure correct implementation of the Sequence Token mechanism in your ingestion code.
B) There is a delay in Snowflake's metadata refresh causing some new tables to be temporarily unavailable, retry after some time or manually refresh metadata.
C) The Snowpipe Streaming client is exceeding its buffer capacity, leading to data loss; increase the buffer size on the client-side.
D) Duplicate records are being dropped due to Snowflake's internal deduplication mechanism; configure the table to allow duplicates.
E) The Snowflake virtual warehouse is not scaled up sufficiently to handle the ingestion rate; increase the virtual warehouse size.

3. Consider the following Snowflake UDTF definition written in Python:

Which of the following statements are TRUE regarding the deployment and usage of this UDTF?

A) The UDTF can be called directly in SQL using 'SELECT FROM TABLE(process_json(VARlANT COLUMN));' without any prior registration.
B) The library needs to be explicitly installed and configured within the UDTF's environment using a Snowpark session.
C) The UDTF will automatically be available in all schemas across all databases in the Snowflake account.
D) The UDTF needs to be registered using 'session.udtf.register' or 'create or replace function' with the 'imports' clause referencing the Python file, and the handler' specifying the function name.
E) The return type of the generator 'yield' must strictly adhere to the declared output schema , or errors will occur during execution.

4. You're designing a Snowpark Scala stored procedure that must execute a series of complex data quality checks on a Snowflake table.
These checks involve multiple steps, including validating data types, checking for null values, and verifying data consistency against external reference data'. You want to ensure that the stored procedure is resilient to errors, provides detailed logging, and can be easily monitored. Which of the following approaches would be the MOST robust and scalable for handling errors and logging within this Snowpark Scala stored procedure?

A) Wrap each data quality check in a try-catch block and use 'println' statements to log error messages to the Snowflake console.
B) Rely on Snowflake's built-in error handling and logging mechanisms. If an error occurs, the stored procedure will automatically fail, and the error details can be retrieved from Snowflake's query history.
C) Implement a custom logging framework within the Scala stored procedure that writes detailed logs to a dedicated Snowflake table. Use try-catch blocks to handle exceptions and log error details, including timestamps, error codes, and relevant data values. Use Snowflake's 'SYSTEM$LAST QUERY ID()' function to track query lineage.
D) Use Scala's 'Try' monad to handle exceptions, mapping successes to informational messages and failures to error messages. Log these messages using Snowflake's event tables.
E) Use Scala's 'Option' type to handle potential null values and exceptions. Return a string message indicating success or failure for each check. Log these messages using 'System.out.println'.

5. You have a Snowflake table, 'CUSTOMER ORDERS', with columns like 'CUSTOMER ID', 'ORDER DATE', 'ORDER AMOUNT', and 'REGION'. A Bl dashboard relies on a query that aggregates data from this table, but the query performance is consistently slow. The query frequently filters by 'ORDER DATE and groups by 'REGION'. Based on the following 'EXPLAIN' output, which combination of techniques should be considered to improve the performance the most?

A) Create a materialized view that pre-aggregates the data by 'ORDER DATE and 'REGION'
B) Redesign the dashboard to minimize the data being displayed at once to the user.
C) Increase the virtual warehouse size to 'LARGE or 'XLARGE.
D) Cluster the 'CUSTOMER ORDERS table on 'ORDER DATE' and 'REGION'.
E) Create an index on the 'ORDER DATE column.

Solutions: