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

1. 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) 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.
C) 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
D) 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.
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.

2. You are building a data pipeline to ingest JSON data from an external API using the Snowflake SQL API. The API returns nested JSON structures, and you need to extract specific fields and load them into a Snowflake table with a flattened schem a. You also need to handle potential schema variations and missing fields in the JSON data. Which approach provides the MOST robust and flexible solution for this scenario, maximizing data quality and minimizing manual intervention?

A) Parse the JSON data in your client application (e.g., Python) using a library like 'json' or , transform the data into a tabular format, and then use the Snowflake Connector for Python to load the data into Snowflake.
B) Utilize Snowflake's schema detection feature during the COPY INTO process. This will automatically infer the schema from the JSON data and create the table accordingly.
C) Load the raw JSON data into a VARIANT column in Snowflake. Create a series of views on top of the VARIANT column to extract the required fields and handle schema variations using 'TRY TO ' functions.
D) Use a stored procedure with dynamic SQL to parse the JSON, create new tables based on the current schema, and load data. Maintain metadata on table versions.
E) Use the 'JSON TABLE function in a Snowflake SQL query executed via the SQLAPI to flatten the JSON data and extract the required fields. Handle missing fields by using 'DEFAULT values in the table schema.

3. You are tasked with creating a development environment from a production database named 'PROD DB'. This database contains sensitive data, and you need to mask the data in the development environment. You decide to use cloning and a transformation function during the cloning process. What is the MOST efficient approach to clone 'PROD DB' into a development database 'DEV DB' and mask sensitive data in the process?

A) Create a clone of 'PROD named 'DEV DB'. Create a warehouse for running masking policies. Then apply masking policies to the tables in 'DEV DB' Cloning masks the underlying data directly.
B) Create a clone of 'PROD named 'DEV DB'. Create stored procedures on 'DEV DB' which apply masking at the query level. Cloning databases does not preserve masking policies from the Source database
C) Create a clone of 'PROD named 'DEV DB', then create views on 'DEV DB' using masking policies. Cloning the Views from 'PROD will automatically copy the masking policies.
D) Clone 'PROD to ' DEV DB'. Export the data from 'DEV DB', transform it using a scripting language (e.g., Python), and then load the transformed data back into replacing the original data.
E) Create a clone of 'PROD named 'DEV DB'. Define masking policies on the columns in 'PROD DB' before cloning. These policies will be automatically applied to the cloned tables in "DEV_DB' ensuring all data is masked during query time in the DEV environment.

4. You are tasked with implementing row-level filtering on a 'customers' table in Snowflake. You need to restrict access to customer data based on the user's region. The 'customers' table has a 'region' column, and you have a mapping table 'user regions' that associates users with the regions they are allowed to access. The 'user_regions' table has columns 'username' and 'region'. Which of the following SQL statements correctly creates and applies a row access policy to achieve this, minimizing complexity and maximizing performance? Select all that apply:

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

5. A data engineering team has created a Snowflake Listing to share their company's sales data'. They want to allow consumers to access the Listing programmatically. The consumers need to know when new versions of the Listing are available. What is the MOST efficient method for consumers to be notified about new Listing versions without continuously polling Snowflake?

A) The data consumer implements a custom webhook endpoint that the data provider calls whenever a new Listing version is published. The data provider will need to maintain a list of all consumers.
B) Snowflake automatically notifies consumers via an event notification service (e.g., AWS SNS, Azure Event Grid) integrated with the Data Marketplace when a new version is available.
C) The consumer sets up a scheduled task in Snowflake to periodically query the 'SHOW VERSIONS command for the Listing.
D) The data provider manually sends email notifications to each consumer whenever a new version is created.

Solutions: