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Snowflake DEA-C02 Exam Syllabus Topics:

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

1. You have a Snowflake table called 'RAW ORDERS that contains semi-structured JSON data in a column named 'ORDER DETAILS. You need to extract specific fields from the JSON data, perform some data type conversions, and then load the transformed data into a relational table named 'CLEAN ORDERS'. Your requirements are as follows: 1. Extract the (STRING) from the JSON and store it as 'ORDER ID (NUMBER). 2. Extract the (STRING) from the JSON and store it as 'CUSTOMER ID (NUMBER). 3. Extract the 'order_date' (STRING) from the JSON and store it as 'ORDER DATE' (DATE). 4. Extract (STRING) from the JSON and store it as 'TOTAL AMOUNT' (FLOAT). Which of the following Snowpark Python code snippets correctly transforms the data and loads it into the 'CLEAN ORDERS table using a combination of Snowpark DataFrame operations and SQL? Assume that session 'sp' is already initialized.

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

2. A critical database, 'PRODUCTION DB', in your Snowflake account was accidentally dropped. You need to restore it as quickly as possible, but you're unsure if Time Travel retention is sufficient. Which method guarantees restoration of the database even if it falls outside the Time Travel window?

A) Utilize the data cloning feature: 'CREATE DATABASE CLONE PRODUCTION_DB BEFORE (STATEMENT 'DROP DATABASE PRODUCTION_DB');'
B) Use the 'UNDROP DATABASE PRODUCTION command.
C) Fail-safe cannot be directly accessed by the user for restoration purposes; it is only used by Snowflake Support in extreme disaster recovery scenarios.
D) Contact Snowflake Support and request restoration from Fail-safe.
E) Restore from a Snowflake-managed backup using the 'CREATE DATABASE ... FROM BACKUP' command. Specify the timestamp before the drop occurred.

3. You are designing a complex data pipeline in Snowflake that involves multiple interdependent Tasks. Several of these Tasks need to access sensitive customer data, and you want to ensure that the least privilege principle is followed. How should you configure the Tasks and their associated roles to minimize the risk of unauthorized data access while maintaining the functionality of the pipeline? (Select TWO)

A) Create separate, specific roles for each Task or group of related Tasks. Grant each role only the minimum necessary privileges to access the specific tables and functions required by that Task. Assign each Task to the appropriate role using the 'EXECUTE AS OWNER clause.o
B) Grant 'SELECT privilege on all tables containing sensitive data to the 'PUBLIC' role. Tasks will inherit these privileges and can access the data without explicit role assignments.
C) Use stored procedures executed with 'EXECUTE AS CALLER to encapsulate the sensitive data access logic. The stored procedure owner (who should have appropriate privileges) grants execute privilege to the Task's role, but the Task itself does not directly interact with the sensitive data.
D) Create a single role with broad data access privileges and grant this role to all Tasks. This simplifies role management and ensures that no Task encounters permission errors during execution.
E) Grant the role to the user that owns all the Tasks. This ensures that all Tasks have the necessary privileges to access any data within the Snowflake account.

4. A data engineer is implementing a data governance policy that requires masking PII data in non-production environments. They have identified a column 'CUSTOMER EMAIL' that needs to be masked. They want to use dynamic data masking in Snowflake, but the 'CUSTOMER EMAIL' column is referenced in several views. Which of the following approaches is MOST appropriate and avoids breaking the existing views?

A) Create a masking policy on the base table, but exclude the role used by the views from the policy's condition. This will prevent masking for those specific views.
B) Create a masking policy directly on the 'CUSTOMER EMAIL' column in the base table. This will automatically apply the masking to all views referencing the column.
C) Create masking policies on each of the individual views that reference the 'CUSTOMER EMAIL' column, using the same masking function.
D) Create a separate view that applies the masking function to the 'CUSTOMER EMAIL' column. Replace all existing views with the new masked view.
E) Create a masking policy on the base table but use a context function in the masking policy condition to check the database name. Mask the data only when the database name is the non-production database.

5. You have a Snowflake table 'CUSTOMER DATA' with the following schema:

Your downstream application requires a new column 'PHONE NUMBER VARCHAR(20)' in the 'CUSTOMER DATA' table. You need to add this column without causing any downtime or disruption to the application. What is the MOST appropriate approach?

A) create a temporary table which is a copy of CUSTOMER_DATA but with the additional column. swap the CUSTOMER_DATA and CUSTOMER DATA TEMP tables.
B) Create a view 'CUSTOMER on top of 'CUSTOMER DATA' that includes the new 'PHONE NUMBER column with a default value. Update the application to use the view instead of the table.
C) Create a new table 'CUSTOMER DATA NEW' with the new column, copy data from 'CUSTOMER DATA' to 'CUSTOMER DATA NEW, drop "CUSTOMER DATA', and rename 'CUSTOMER DATA NEW' to 'CUSTOMER DATA'.
D) Create a new stream on the table 'CUSTOMER DATA'. Then, create a new table 'CUSTOMER DATA NEW' with the extra PHONE NUMBER column as part of the DDL. Use the stream and table to update data from the old to new table, Then, drop "CUSTOMER_DATA' , and rename to 'CUSTOMER DATA'.
E) Execute 'ALTER TABLE CUSTOMER DATAADD COLUMN PHONE NUMBER VARCHAR(20);'. This will add the column with a NULL value for existing rows.

Solutions: