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

1. You are monitoring a Snowpipe pipeline that loads data from an external stage into a Snowflake table. You observe the following error messages in the PIPE ERRORS view: 'Invalid UTF-8 detected in string'. The data files on the stage are encoded in UTF-8. Which of the following actions, taken individually or in combination, are MOST likely to resolve this issue? (Select TWO)

A) Convert the problematic files to UTF-16 encoding before loading them into the stage.
B) Drop and recreate the external stage with 'TYPE = INTERNAL'.
C) Ensure the file format definition explicitly specifies 'ENCODING = 'UTF8".
D) Modify the COPY INTO statement to include the 'ON ERROR = 'SKIP_FILE" option.
E) Verify the data files on the stage are actually valid UTF-8 and contain no corrupted characters.

2. A data engineer is tasked with migrating data from a large on-premise Hadoop cluster to Snowflake using Spark. The Hadoop cluster contains nested JSON dat a. To optimize performance and minimize data transformation in Spark, what is the most efficient approach to read the JSON data into a Spark DataFrame and write it directly to a Snowflake table?

A) Read the JSON data as strings and utilize Snowflake's 'PARSE JSON' function within a Spark SQL query to transform and load the data into a variant column in Snowflake.
B) Define a schema manually in Spark, then read the JSON data into a Spark DataFrame. Use the Snowflake Spark connector to write the data to Snowflake, specifying the schema explicitly.
C) Use the 'STORAGE_INTEGRATION' feature in Snowflake to directly access the JSON files in Hadoop (via an external stage) and load the data without using Spark at all.
D) Use the Snowflake Spark connector with the 'inferSchema' option set to 'true' when reading the JSON data. This allows Spark to automatically infer the schema and write directly to Snowflake.
E) Read the JSON data as text files, then use Spark to parse and flatten the JSON structure before writing to Snowflake using the Snowflake JDBC connector.

3. You are using the Snowflake Spark connector to update records in a Snowflake table based on data from a Spark DataFrame. The Snowflake table 'CUSTOMER' has columns 'CUSTOMER ID' (primary key), 'NAME, and 'ADDRESS'. You have a Spark DataFrame with updated 'NAME and 'ADDRESS' values for some customers. To optimize performance and minimize data transfer, which of the following strategies can you combine with a temporary staging table to perform an efficient update?

A) Write the Spark DataFrame to a temporary table in Snowflake. Then, execute an 'UPDATE statement in Snowflake joining the temporary table with the 'CUSTOMER table using the 'CUSTOMER_ID to update the 'NAME and 'ADDRESS' columns. Finally, drop the temporary table.
B) Broadcast the Spark DataFrame to all executor nodes, then use a UDF to execute the 'UPDATE' statement for each row directly from Spark.
C) Use Spark's foreachPartition to batch update statements and execute on each partition. This will help with efficient data transfer and avoid single row based updates.
D) Write the Spark DataFrame to a temporary table in Snowflake using MERGE. Use the WHEN MATCHED clause for Update the target table based on updates from staging table and finally drop the staging table
E) Iterate through each row in the Spark DataFrame and execute an individual 'UPDATE statement against the 'CUSTOMER table in Snowflake. Use the 'CUSTOMER_ID in the 'WHERE clause.

4. You are tasked with creating a resilient data ingestion pipeline using Snowpipe and external tables on AWS S3. The data consists of JSON files, some of which may occasionally contain invalid JSON structures (e.g., missing closing brackets, incorrect data types). You want to ensure that even if some files are corrupted, the valid data is still ingested into your target Snowflake table, and the corrupted files are logged for later investigation. Which of the following steps would BEST achieve this?

A) Use Snowflake's => 'JSON', job_id => function against the external stage before ingesting data with Snowpipe to pre-validate files. Then ingest only validated files to your target table
B) Create a custom error handler using a Snowflake stored procedure that catches the 'JSON PARSER ERROR exception and logs the filename to a separate error table. Use the ERROR = 'CONTINUE" copy option in the Snowpipe definition.
C) Configure Snowpipe to use the 'ON ERROR = 'SKIP FILE" copy option and then create a separate task to query the 'VALIDATION MODE metadata column in the external table to identify and log the corrupted files.
D) Configure the external table definition with 'VALIDATION MODE = 'RETURN ERRORS" and then create a view on top of the external table that filters out rows where the 'METADATA$FILE ROW NUMBER column contains errors.
E) Set the 'ON ERROR option to 'ABORT STATEMENT in the Snowpipe definition. This will stop the entire Snowpipe process when a JSON error is detected, allowing you to manually investigate and fix the corrupted files before restarting the pipeline.

5. 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 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.
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 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.

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