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

1. You are designing a CI/CD pipeline for your Snowflake data transformations. One stage involves testing a new stored procedure that modifies several tables in your data warehouse. To ensure data integrity and proper rollback capabilities during testing in your development environment, you want to use a combination of cloning and Tme Travel. Select the option that represents the most robust strategy for testing with the ability to revert to the original state in case of failures. Choose all that apply.

A) Immediately after executing the stored procedure, execute 'UNDROP TABLE' on all the affected tables. If there were errors, it would restore data from the point of the procedure execution.
B) Leverage Snowflake's Time Travel feature to create a named snapshot of each affected table immediately before running the stored procedure. Use the 'AT(TIMESTAMP ...)' or 'BEFORE(STATEMENT => ...)' syntax within your testing framework, for easy reversion.
C) Create a clone of the schema containing all the tables affected by the stored procedure before executing the procedure in the development environment. This will create point in time backup to rollback at schema level.
D) Before executing the stored procedure in development, create clones of all affected tables and store the clone names in a configuration file for rollback if needed.
E) Create a new database by cloning the existing database before the transformations. Use the cloned database for all development and testing.

2. You are designing a data governance strategy for a Snowflake data warehouse. You need to track data lineage for compliance purposes. Specifically, you need to identify all downstream tables that depend on a specific column in a source table. Which combination of Snowflake features and techniques would you use to achieve this goal effectively?

A) Use Snowflake's ACCOUNT USAGE views related to query history and object dependencies, combined with a custom script to recursively trace data lineage based on SQL operations (e.g., INSERT INTO ... SELECT).
B) Use Snowflake's INFORMATION SCHEMA views (TABLES, COLUMNS) and regularly audit user query history to manually reconstruct the data lineage.
C) Rely solely on user documentation and training to ensure data lineage is properly documented and maintained. Implement strict naming conventions for tables and columns.
D) Utilize Snowflake's data lineage feature in conjunction with object tagging. Tag relevant columns and tables, then query the lineage views to trace dependencies.
E) Implement a custom data lineage tracking system by parsing all SQL queries executed in the Snowflake environment and storing the dependencies in a separate metadata database.

3. 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 SQL UDF containing a series of nested CASE statements to implement the entire scoring algorithm.
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 JavaScript UDF that uses basic JavaScript functions to perform the entire scoring algorithm without external dependencies.
D) 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.
E) A Python UDTF using Snowpark, leveraging external libraries like 'torch' for accelerated calculations and ML model inference by GPU.

4. You are tasked with implementing a data recovery strategy for a critical table 'SALES DATA' in Snowflake. The table is frequently updated, and you need to ensure you can recover to a specific point in time in case of accidental data corruption. Which approach provides the most efficient and granular recovery option, minimizing downtime and data loss? Consider performance and storage implications of each method.

A) Relying solely on Snowflake's Time Travel feature with the default data retention period.
B) Creating a Snowflake Stream on 'SALES_DATR and capturing all DML changes for point-in-time recovery.
C) Regularly creating full clones of the 'SALES_DATR table to a separate database.
D) Create a scheduled task that takes a snapshot of the sales data and store it to an external staging location.
E) Using the 'UNDROP TABLE command in conjunction with the 'AT' clause to revert the table to a previous state.

5. You are tasked with building a data pipeline using Snowpark to process sensor data from IoT devices. The data arrives in near real-time as JSON payloads, and you need to transform and load it into a Snowflake table named 'SENSOR DATA'. The transformation logic involves extracting specific fields, converting data types, and filtering out records based on a timestamp. Consider performance optimization for large data volumes. Which of the following approaches, in combination, would be MOST efficient for this scenario?

A) Leveraging Snowflake's native JSON parsing functions within a SQL transformation step implemented as a Snowpark DataFrame operation, combined with a Snowpipe for initial data ingestion into a staging table.
B) Employing Snowpipe to ingest the raw JSON data into a VARIANT column in a staging table, followed by a Snowpark DataFrame operation using 'functions.get' to extract and transform the data, and finally loading into 'SENSOR DATA'
C) Creating an external table pointing to the JSON data in cloud storage and using Snowpark DataFrames to read the external table, apply transformations, and load the result into 'SENSOR DATA'.
D) Using a stored procedure written in Java to parse the JSON data and insert directly into the "SENSOR DATA' table.
E) Using a Snowpark Python UDF to parse JSON and perform transformations, loading the result into a temporary table, and then merging into 'SENSOR DATA'.

Solutions: