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

1. A data engineer is tasked with optimizing query performance on a Snowflake table named 'SALES DATA, which currently has no clustering key defined. The table contains 'SALE (unique identifier), 'SALE DATE, 'PRODUCT CATEGORY, and 'SALE AMOUNT. The business analysts frequently run queries filtering on 'SALE DATE and then aggregating by 'PRODUCT CATEGORY'. Choosing the right clustering keys for the SALES DATA table is crucial for minimizing disk 1/0 and enhancing query speed. Which of the following clustering key strategies would be MOST effective for the specified query patterns, considering both performance and the potential impact on data loading and DML operations?

A) Clustering on followed by 'SALE_DATE'.
B) Clustering on 'SALE DATE followed by 'PRODUCT CATEGORY.
C) Clustering only on 'SALE DATE
D) Clustering only on PRODUCT_CATEGORY.
E) Creating separate tables for each 'PRODUCT CATEGORY.

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) Rely solely on user documentation and training to ensure data lineage is properly documented and maintained. Implement strict naming conventions for tables and columns.
B) 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).
C) Use Snowflake's INFORMATION SCHEMA views (TABLES, COLUMNS) and regularly audit user query history to manually reconstruct the data lineage.
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. A company is using Snowflake's web app interface to manage its data'. A data engineer needs to create a new table, load data into it from a CSV file stored in an internal stage, and then grant SELECT privileges on the table to a specific role using the web app. Which sequence of actions within the Snowflake web app represents the most efficient and secure way to accomplish this task?

A) 1. Use the SQL worksheet to execute CREATE TABLE statement. 2. Use the Data Load Data wizard to load the CSV file. 3. Use the SQL worksheet to execute GRANT SELECT ON TABLE statement.
B) 1. Use the Database Tables interface to create the new table using the table editor. 2. Use the Data Load Data wizard to load the CSV file. 3. Use the SQL worksheet to execute GRANT SELECT ON TABLE statement.
C) 1. Use the Database Tables interface to create the new table using the table editor. 2. Upload the CSV file directly to the table using the 'Load Data' option. 3. Use the SQL worksheet to execute GRANT SELECT ON TABLE statement.
D) 1. Use the Database Tables interface to create the new table using the table editor. 2. Use the Data Load Data wizard to load the CSV file. 3. Use the Database -> Tables interface, select the table, and use the 'Privileges' tab to grant SELECT privilege to the role.
E) 1. Use the SQL worksheet to execute CREATE TABLE statement. 2. Use the Database -> Tables interface, select the table, and use the 'Load Data' option to load the CSV file. 3. Use the Database -> Tables interface, select the table, and use the 'Privileges' tab to grant SELECT privilege to the role.

4. You are tasked with implementing data masking on a 'CUSTOMER' table. The requirement is to mask the 'EMAIL' column for all users except those with the 'DATA ADMIN' role. You have the following code snippet. What is wrong with it?

A) Without masking poliy code, it's impossible to determine if there is anything wrong.
B) The WITH clause is unneccessary.
C) The masking policy is applied to the wrong column. It should be applied to the ID column, not the EMAIL column.
D) The masking policy syntax is incorrect. It should use 'CASE WHEN IS_ROLE_IN_SESSION('DATA_ADMIN') THEN EMAIL ELSE '[email protected]' END'.
E) There is no code provided, so there is nothing wrong with it.

5. You have created a Snowflake Iceberg table that points to data in an AWS S3 bucket. After some initial data ingestion, you realize that the schema in the Iceberg table does not perfectly match the schema of the underlying Parquet files in S3. Specifically, one of the columns in the Iceberg table is defined as 'VARCHAR , while the corresponding column in the Parquet files is stored as 'INT. What will be the most likely behavior when you query this Iceberg table in Snowflake?

A) Snowflake will attempt to cast the data, and if a cast fails (e.g., 'INT' value is too large to fit in 'VARCHAR), the query will return an error only for those specific rows. Other rows will be processed correctly.
B) The query will fail with an error indicating a data type mismatch between the Iceberg table schema and the underlying Parquet file schema.
C) The query will succeed, but the result will be unpredictable and may vary depending on the specific data values in the Parquet files.
D) The query will succeed, but the 'VARCHAR column will contain 'NULL' values for all rows where the underlying Parquet files contain 'INT' values.
E) Snowflake will automatically cast the SINT' data in the Parquet files to 'VARCHAR during query execution, and the query will succeed without any errors or warnings.

Solutions: