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

1. You are using Snowpark to perform a complex join operation between two large tables: 'ORDERS (1 OOGB) and 'CUSTOMER (50GB). The join is performed on 'ORDERS.CUSTOMER ID = CUSTOMER.ID. The query is running slower than expected. You have already confirmed that the warehouse size is adequate. Which of the following strategies, applied in combination , would most likely improve the join performance within a Snowpark context?

A) Analyze the query profile in Snowflake's web UI to identify the specific bottleneck (e.g., excessive data spilling, high CPU utilization) and address it directly.
B) Use 'session.add_import to add external JAR dependencies. This would enable use of external libraries and improve performance.
C) Increase the 'AUTO RESIZE' setting on the warehouse to automatically scale up the warehouse size when the load increases.
D) Use Snowpark's 'hint function to force a broadcast join, assuming the 'CUSTOMER table can fit into memory on the worker nodes.
E) Ensure both tables are clustered on the join keys CORDERS.CUSTOMER_ID' and 'CUSTOMER.ID').

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

3. You are designing a system to monitor data access patterns in Snowflake. You want to capture detailed information about all queries executed, including the user, query text, execution time, and any potential data access violations based on security policies. Which of the following approaches, used in combination, would provide the MOST comprehensive and scalable solution for this monitoring requirement? (Select TWO)

A) Configure the 'SNOWFLAKE database's audit logs and stream them to an external security information and event management (SIEM) system.
B) Create a stored procedure to intercept all SQL commands before execution, log them, and then execute them using 'EXECUTE IMMEDIAT
C) Implement Snowflake's Event Tables and configure them to capture security-related events, such as data access policy violations.
D) Enable query tagging and insert custom tags into each SQL statement indicating sensitive data access. Then, query 'QUERY HISTORY filtering on these tags.
E) Enable the 'QUERY_HISTORY view in the 'ACCOUNT_USAGE' schema and periodically query it using a scheduled task.

4. You are developing a data pipeline that uses Snowpipe Streaming to ingest JSON data into a Snowflake table. Some JSON documents contain nested arrays and complex structures. You need to flatten the JSON structure during ingestion to simplify querying. Consider the following JSON document: { "order id": 123, "customer": { "id": "cust123", "name": "John Doe", "address": { "street": "123 Main St", "city": "Anytown" } }, "items": [ {"product_id": "prodl", "quantity": 2}, {"product_id": "prod2", "quantity": 1} ] } Which approach would you use within the 'COPY INTO' statement of your Snowpipe to flatten this JSON structure during ingestion?

A) Create a separate transformation pipeline using Snowflake Tasks to flatten the data after it is ingested into the table.
B) Use the ' FLATTEN()' table function directly within the 'COPY INTO' statement to expand the 'items' array and extract nested fields. For nested objects, use dot notation directly in the SELECT list (e.g., 'customer.name').
C) Snowpipe and the 'COPY INTO' command automatically flattens JSON data during ingestion; no additional steps are required.
D) Use JavaScript UDFs within the 'COPY INTO' statement to recursively flatten the JSON structure.
E) Pre-process the JSON documents before loading them into the stage using a custom script to flatten the structure.

5. You have a Snowflake Task that is designed to transform and load data into a target table. The task relies on a Stream to detect changes in a source table. However, you notice that the task is intermittently failing with a 'Stream STALE' error, even though the data in the source table is continuously updated. What are the most likely root causes and the best combination of solutions to prevent this issue? (Select TWO)

A) The Stream is not configured with 'SHOW INITIAL ROWS = TRUE, causing initial changes to be missed and eventually leading to staleness. Recreate the stream with this parameter set to TRUE.
B) The source table is being modified with DDL operations (e.g., ALTER TABLE ADD COLUMN), which are not supported by Streams. Use Table History to track schema changes and manually adjust the Stream's query if needed. Use 'COPY GRANTS' during the DDL.
C) DML operations (e.g., UPDATE, DELETE) being performed on the source table are affecting rows older than the Stream's retention period. Reduce the stream's 'DATA RETENTION TIME IN DAYS' to match the oldest DML operation on the source table.
D) The Task is not running frequently enough, causing the Stream to accumulate too many changes before being consumed, exceeding its retention period. Increase the task's execution frequency or increase the stream's 'DATA RETENTION TIME IN DAYS
E) The Stream has reached its maximum age (default 14 days) and expired. There is no way to recover data from an expired Stream. You need to recreate the Stream and reload the source table.

Solutions: