Snowflake DEA-C02

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Snowflake DEA-C02 試験シラバストピック:

Snowflake SnowPro Advanced: Data Engineer (DEA-C02) 認定 DEA-C02 試験問題：

問題 #1
You are tasked with creating a Snowpark Java stored procedure to calculate a complex, custom rolling average for a time series dataset. This rolling average requires access to external libraries for statistical calculations. Which of the following steps are necessary to successfully deploy and execute this stored procedure?

A. Grant the necessary privileges on the stage and the database to the role executing the stored procedure.
B. Package the Java code and all necessary external libraries into a single JAR file.
C. Upload the JAR file to a Snowflake stage.
D. All of the above.
E. Create a stored procedure in Snowflake, specifying the fully qualified path to the JAR file in the stage, the handler class, and the return type.

問題 #2
You have a table named 'TRANSACTIONS which is frequently queried by 'TRANSACTION_DATE and 'CUSTOMER ID. You want to define a clustering strategy for this table. You are aware that defining multiple clustering keys is possible. Given the following considerations, which of the following clustering strategies would provide the BEST performance AND minimize reclustering costs, assuming both columns have similar cardinality and are equally used in WHERE clauses? (Assume cost optimization is the most critical factor if performance difference is minimal.)

A.

B.

C. Create two separate tables: one clustered by 'TRANSACTION DATE and another clustered by 'CUSTOMER ID', and use appropriate views to redirect queries to the correct table.
D.

E.

問題 #3
You are designing a data pipeline that uses the Snowflake SQLAPI to execute a series of complex SQL queries. These queries involve multiple joins, aggregations, and user-defined functions (UDFs). You need to ensure that the pipeline is resilient to transient network errors and can handle a large volume of concurrent requests. Which of the following strategies would you implement to enhance the reliability and performance of your pipeline?

A. Use the SQL API's asynchronous execution mode and poll for query status to handle long-running queries without blocking.
B. Increase the timeout value for the SQL API requests to accommodate potentially long-running queries.
C. Implement exponential backoff and retry logic in your client application to handle transient errors when calling the SQL API.
D. Bundle all the SQL queries into a single, large SQL statement to reduce the number of API calls.
E. Disable query result caching in Snowflake to ensure that the pipeline always retrieves the latest data.

問題 #4
Consider the following scenario: You have a Snowflake task that refreshes a materialized view. The materialized view is based on a large base table that is constantly being updated. The refresh operation is taking longer than expected, impacting downstream reporting. Which of the following actions will likely NOT improve the performance of the materialized view refresh?

A. Analyzing the Query Profile of the Materialized View Refresh query to identify Bottlenecks and Optimization areas.
B. Using the 'AUTO_REFRESH' property of the materialized view when appropriate and feasible.
C. Adding more columns to the materialized view, even if they are not immediately required for reporting.
D. Increasing the warehouse size used by the task executing the refresh.
E. Ensuring the base table used by the materialized view has an appropriate clustering key.

問題 #5
You are tasked with building a Snowpipe to ingest JSON data from an AWS S3 bucket into a Snowflake table named 'SALES DATA'. The data is landing in the bucket frequently, and you want to use Snowpipe's auto-ingest feature. However, you are observing significant latency in data appearing in your Snowflake table after it lands in S3, despite verifying that S3 event notifications are correctly configured and the Snowflake event queue is receiving them. You've already checked that the pipe is enabled and has the necessary permissions. The Snowflake Pipe definition is as follows:

What is the MOST LIKELY reason for this delay, and what steps can you take to further troubleshoot?

A. Snowpipe auto-ingest only supports CSV files. Convert your JSON data to CSV format before loading.
B. There is a backlog of files in the internal Snowflake queue waiting to be processed. Monitor the 'SYSTEM$PIPE STATUS' function and consider increasing the 'MAX CONCURRENCY' parameter (if applicable, based on underlying infrastructure considerations) on the pipe definition.
C. Snowflake's internal metadata cache is out of sync. Run 'ALTER PIPE SALES PIPE to refresh the cache.
D. The S3 bucket is not in the same region as the Snowflake account. Ensure the S3 bucket and Snowflake account are in the same region to reduce network latency.
E. The Snowflake virtual warehouse associated with the pipe is undersized. Increase the warehouse size to improve ingestion performance.

解説：