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

1. Consider the following Python Snowpark stored procedure designed to enrich customer data:

What are potential drawbacks or limitations of this approach, and what improvements can be made to enhance its scalability and maintainability? (Select all that apply)

A) Fetching data from an external API inside a UDF can introduce performance bottlenecks and potential network issues. Snowflake recommends minimizing external dependencies within UDFs.
B) The operation retrieves all rows into the client's memory, which can be inefficient for large datasets. Consider using 'write' operation to store results to table.
C) Hardcoding the SQL query within the stored procedure makes it inflexible. Using Snowpark DataFrame operations instead would improve maintainability.
D) Defining a UDF within the stored procedure creates unnecessary overhead as the UDF is recompiled every time the stored procedure runs. It's more efficient to define the UDF separately and call it from the procedure.
E) The code is perfectly fine and doesn't require any improvements, as it effectively enriches customer data using a UDF and Snowpark DataFrame operations.

2. Your company utilizes Snowflake Streams and Tasks for continuous data ingestion and transformation. A critical task, 'TRANSFORM DATA', consumes data from a stream 'RAW DATA STREAW on table 'RAW DATA' and loads it into a reporting table 'REPORTING TABLE. You observe that 'TRANSFORM DATA is failing intermittently with a 'Stream is stale' error. What steps can you take to diagnose and resolve this issue? Choose all that apply.

A) Modify the task definition to use the 'WHEN condition to prevent execution when the stream is empty.
B) Ensure that the ' TRANSFORM DATA' task is consuming the stream data frequently enough to prevent the stream from becoming stale.
C) Increase the parameter at the database level to ensure Time Travel data is available for a longer period.
D) Drop and recreate the stream and task to reset their states.
E) Use the "AT' or 'BEFORE clause when querying the stream to explicitly specify a point in time to consume data from.

3. You're building a data product on the Snowflake Marketplace that includes a view that aggregates data from a table containing Personally Identifiable Information (PII). You need to ensure that consumers of your data product CANNOT directly access the underlying PII data but can only see the aggregated results from the view. What is the MOST secure and recommended approach to achieve this?

A) Create a stored procedure that returns the aggregated data, and grant EXECUTE privilege on the stored procedure to the share. The stored procedure SELECTs from the PII table.
B) Grant the 'SELECT privilege only on the to the share used for the Marketplace listing. Do not grant any privileges on the underlying PII table.
C) Grant the 'SELECT privilege directly on the underlying PII table to the share used for the Marketplace listing, along with the 'SELECT privilege on 'sensitive data view'.
D) Grant USAGE privilege on the database containing the PII table and to the share.
E) Grant 'READ privilege on the internal stage containing the data files backing the PII table.

4. A financial services company stores sensitive customer data, including credit card numbers, in a Snowflake table called 'CUSTOMER DATA. You need to implement dynamic data masking on the 'CREDIT CARD NUMBER column. You want to ensure that only users with the FINANCE ADMIN' role can view the unmasked credit card numbers. All other users should see a masked version of the data'. Which of the following set of commands is the MOST efficient and secure way to achieve this?

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

5. A healthcare provider stores patient data in Snowflake, including 'PATIENT ID', 'NAME, 'MEDICAL HISTORY , and 'INSURANCE ID. They need to comply with HIPAA regulations. As a data engineer, you need to ensure that PHI (Protected Health Information) is masked appropriately based on user roles. Which of the following steps are NECESSARY to achieve this using Snowflake's data masking features and RBAC? (Select all that apply)

A) Create custom roles representing different user groups within the organization (e.g., 'DOCTOR, 'NURSE, 'ADMIN') and grant them the necessary privileges to access the data, including 'SELECT on the tables and views containing patient data.
B) Apply the created masking policies to the corresponding columns in the patient data tables, ensuring that the masking policies are designed to reveal only the necessary information based on the user's role (e.g., doctors see full medical history, nurses see limited medical history, admins see de-identified data).
C) Identify the columns containing PHI and create appropriate masking policies for each column (e.g., masking 'NAME, 'MEDICAL HISTORY, INSURANCE_ID).
D) Grant the 'OWNERSHIP privilege on the 'PATIENT table to the 'ACCOUNTADMIN' role, ensuring complete control and management of the data by the administrator.
E) Enforce multi-factor authentication (MFA) for all users accessing the Snowflake environment to enhance security and prevent unauthorized access to sensitive data.

Solutions: