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

1. You are tasked with managing a large Snowflake table called 'TRANSACTIONS'. Due to compliance requirements, you need to archive data older than one year to long-term storage (AWS S3) while ensuring the queries against the current 'TRANSACTIONS' table remain performant. What is the MOST efficient strategy using Snowflake features and considering minimal impact on query performance?

A) Create a new table 'TRANSACTIONS_ARCHIVE in Snowflake, copy the historical data, and then delete the archived data from the 'TRANSACTIONS table.
B) Create an external table pointing to S3. Then create new table named 'TRANSACTIONS_ARCHIVE in Snowflake, copy the historical data from 'TRANSACTIONS' table into 'TRANSACTIONS ARCHIVE, and then delete the archived data from the 'TRANSACTIONS' table.
C) Export the historical data to S3 using COPY INTO, truncate the 'TRANSACTIONS' table, and then create an external table pointing to the archived data in S3.
D) Use Time Travel to clone the "TRANSACTIONS' table to a point in time one year ago. Then, export the cloned table to S3 and drop the cloned table. Delete the archived data from the 'TRANSACTIONS table.
E) Partition the 'TRANSACTIONS table by date. Export the old partitions of the 'TRANSACTIONS' table to S3 using COPY INTO. Then, drop the old partitions from the 'TRANSACTIONS table and create an external table that points to the data in S3.

2. Consider a scenario where you have a Snowflake external table 'ext_logs' pointing to log files in an S3 bucket. The log files are continuously being updated, and new files are added frequently. You want to ensure that your external table always reflects the latest data available in S3. Which of the following actions and configurations are required or recommended to keep the external table synchronized with the underlying data source? (Select all that apply)

A) Configure an event notification service (e.g., AWS SQS) to trigger an external table refresh whenever new files are added to S3.
B) Implement a stored procedure that periodically executes a query on the external table to force a metadata refresh.
C) Enable auto-refresh on the external table using the 'AUTO_REFRESH = TRUE parameter during creation.
D) Create a Snowpipe that continuously loads data from the S3 bucket into a Snowflake table instead of using an external table.
E) Periodically execute the 'ALTER EXTERNAL TABLE ext_logs REFRESH' command to update the metadata about the files in S3.

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

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

5. You are designing a data pipeline that requires applying a complex scoring algorithm to customer data in Snowflake. This algorithm involves multiple steps, including feature engineering, model loading, and prediction. You want to encapsulate this logic within a reusable component and apply it to incoming data streams efficiently. Which of the following approaches is most suitable and scalable for implementing this scoring logic as a UDF/UDTF, considering real-time data processing and low latency requirements?

A) A JavaScript UDF that uses basic JavaScript functions to perform the entire scoring algorithm without external dependencies.
B) A Python UDF that loads a pre-trained machine learning model (e.g., using scikit-learn) and performs predictions on the input data.
C) A Java UDTF that leverages a custom Java library for feature engineering and model prediction, deployed as a JAR file to Snowflake's internal stage.
D) A SQL UDF containing a series of nested CASE statements to implement the entire scoring algorithm.
E) A Python UDTF using Snowpark, leveraging external libraries like 'torch' for accelerated calculations and ML model inference by GPU.

Solutions: