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

1. A data engineer accidentally truncated a critical table 'ORDERS' in the 'SALES DB' database. The table contained important historical order data, and the data retention period is set to the default. Which of the following options represents the MOST efficient and reliable way to recover the truncated table and its data, minimizing downtime and potential data loss?

A) Contact Snowflake support and request them to restore the table from a system-level backup.
B) Use Time Travel to create a clone of the truncated table from a point in time before the truncation. Then, swap the original table with the cloned table.
C) Use the UNDROP TABLE command to restore the table. If UNDROP fails, clone the entire SALES_DB database to a point in time before the truncation using Time Travel.
D) Create a new table 'ORDERS' and manually re-insert the data from the application's logs and backups.
E) Restore the entire Snowflake account to a previous point in time before the table was truncated.

2. Your company utilizes Snowflake Streams and Tasks for continuous data ingestion and transformation. A critical task, 'TRANSFORM DATA', consumes data from a stream 'RAW DATA STREAW on table 'RAW DATA' and loads it into a reporting table 'REPORTING TABLE. You observe that 'TRANSFORM DATA is failing intermittently with a 'Stream is stale' error. What steps can you take to diagnose and resolve this issue? Choose all that apply.

A) Drop and recreate the stream and task to reset their states.
B) Use the "AT' or 'BEFORE clause when querying the stream to explicitly specify a point in time to consume data from.
C) Increase the parameter at the database level to ensure Time Travel data is available for a longer period.
D) Ensure that the ' TRANSFORM DATA' task is consuming the stream data frequently enough to prevent the stream from becoming stale.
E) Modify the task definition to use the 'WHEN condition to prevent execution when the stream is empty.

3. You are designing a data pipeline to ingest streaming data from Kafka into Snowflake. The data contains nested JSON structures representing customer orders. You need to transform this data and load it into a flattened Snowflake table named 'ORDERS FLAT'. Given the complexities of real-time data processing and the need for custom logic to handle certain edge cases within the JSON payload, which approach provides the MOST efficient and maintainable solution for transforming and loading this streaming data into Snowflake?

A) Use Snowflake's Snowpipe with a COPY INTO statement that utilizes the 'STRIP OUTER ARRAY option to handle the JSON array, combined with a series of SQL queries with 'LATERAL FLATTEN' functions to extract the nested data after loading into a VARIANT column.
B) Create a Python UDF that calls 'json.loads()' to parse the JSON within Snowflake and then use SQL commands with 'LATERAL FLATTEN' to navigate and extract the desired fields into a staging table. Afterward, use a separate SQL script to insert from staging to the final table 'ORDERS FLAT
C) Implement a custom external function (UDF) written in Java to parse and transform the JSON data before loading it into Snowflake. Configure Snowpipe to call this UDF during the data ingestion process. This UDF will flatten the JSON structure and return a tabular format directly insertable into 'ORDERS FLAT.
D) Utilize a third-party ETL tool (like Apache Spark) to consume the data from Kafka, perform the JSON flattening and transformation logic, and then use the Snowflake connector to load the data into the 'ORDERS FLAT' table in batch mode.
E) Use Snowflake's built-in JSON parsing functions within a Snowpipe COPY INTO statement, combined with a 'CREATE VIEW' statement on top of the loaded data. The view will use 'LATERAL FLATTEN' to present the data in the desired flattened structure without physically transforming the underlying data.

4. A Snowflake data pipeline ingests data from multiple external sources into a RAW DATA table. A transformation process then moves the data to a ANALYTICS DATA table, applying several complex UDFs written in Java and Python for data cleansing and enrichment. Performance is significantly slower than expected. Which combination of techniques would BEST improve the performance of this transformation pipeline?

A) Reduce the number of UDF calls by consolidating them into a single, more complex UDF. Replace the transformation pipeline with a series of COPY INTO statements.
B) Increase the virtual warehouse size and re-cluster the ANALYTICS DATA table based on the most frequently filtered columns.
C) Rewrite the UDFs in SQL or Snowpark Python/Java for better integration with the Snowflake engine and leverage vectorization where possible; cache intermediate results using temporary tables.
D) Implement data partitioning in the RAW DATA table based on ingestion time and switch to using stored procedures instead of transformation pipelines.
E) Use external functions instead of UDFs to offload the processing to an external compute environment and configure auto-scaling for the virtual warehouse.

5. You have a Snowpark DataFrame 'df_products' with columns 'product id', 'category', and 'price'. You need to perform the following transformations in a single, optimized query using Snowpark Python: 1. Filter for products in the 'Electronics' or 'Clothing' categories. 2. Group the filtered data by category. 3. Calculate the average price for each category. 4. Rename the aggregated column to 'average_price'. Which of the following code snippets demonstrates the most efficient way to achieve this?

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

Solutions: