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

1. A data engineer is working with a Snowpark DataFrame 'sales df containing sales data with columns 'product id', 'sale_date', and 'sale amount'. The engineer needs to calculate the cumulative sales amount for each product over time. Which of the following code snippets using window functions correctly calculates the cumulative sales amount, ordered by 'sale date'?

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

2. You have a Snowflake view that joins three large tables: ORDERS, CUSTOMERS, and PRODUCTS. The query accessing this view is frequently used but performs poorly. You suspect inefficient join processing and potential skew in the data'. Which of the following strategies can be used to optimize the view's performance? (Select all that apply)

A) Analyze the query profile to identify bottlenecks and potential data skew issues, and then re-cluster the underlying tables based on the most frequently used join keys.
B) Use JOIN hints, such as BROADCAST or MERGE, to guide the query optimizer on join strategies.
C) Partition the underlying tables based on the join keys to improve data locality.
D) Increase the virtual warehouse size to provide more resources for query processing. Convert the view into a table using CREATE TABLE AS SELECT (CTAS).
E) Replace the view with a materialized view to precompute and store the results.

3. You have a Snowflake task that executes a complex stored procedure. This stored procedure performs several UPDATE statements on a large table. After enabling the 'QUERY TAG' parameter, you notice that the task history in Snowflake shows frequent suspensions due to exceeding warehouse resource limits. The warehouse is already scaled to the largest size. Which combination of the following strategies would BEST address this issue and minimize task suspensions, assuming you CANNOT further scale the warehouse?

A) Implement a retry mechanism within the task's SQL code to automatically retry failed UPDATE statements after a short delay.
B) Increase the 'ERROR ON N parameter for the task to allow for more consecutive timeouts before the task is suspended.
C) Break down the stored procedure into smaller, more manageable transactions and commit changes more frequently. Consider utilizing batch processing techniques.
D) Optimize the UPDATE statements in the stored procedure to reduce resource consumption by using techniques such as clustering keys, partitioning and avoiding full table scans.
E) Set a lower 'SUSPEND TASK AFTER N FAILURES' value to proactively suspend the task before it consumes excessive resources.

4. You have a table 'EMPLOYEE DATA' containing Personally Identifiable Information (PII), including 'salary' and 'email'. You need to implement column-level security such that: 1) The 'salary' column is only visible to users in the 'FINANCE ROLE. 2) The 'email' column is masked with a SHA256 hash for all users except those in the 'HR ROLE. You create the following masking policies:

Which of the following SQL statements correctly applies these masking policies to the 'EMPLOYEE DATA table?

A) ALTER TABLE EMPLOYEE_DATAAPPLY MASKING POLICY mask_salary ON COLUMN salary; ALTER TABLE EMPLOYEE_DATAAPPLY MASKING POLICY mask email ON COLUMN email;
B) ALTER TABLE EMPLOYEE_DATAALTER COLUMN salary SET MASKING POLICY mask_salary; ALTER TABLE EMPLOYEE_DATAALTER COLUMN email SET MASKING POLICY mask email;
C) ALTER TABLE EMPLOYEE_DATA MODIFY COLUMN salary SET MASKING POLICY mask_salary; ALTER TABLE EMPLOYEE_DATA MODIFY COLUMN email SET MASKING POLICY mask email;
D) CREATE OR REPLACE TAG employee_data.salary VALUE 'mask_salary'; CREATE OR REPLACE TAG employee_data.email VALUE 'mask_email';
E) ALTER TABLE EMPLOYEE_DATA MODIFY COLUMN salary SET MASKING POLICY = mask_salary; ALTER TABLE EMPLOYEE_DATA MODIFY COLUMN email SET MASKING POLICY = mask email;

5. You are responsible for ensuring data consistency across multiple Snowflake tables involved in a financial reporting system. You've noticed discrepancies in aggregate calculations between a 'TRANSACTIONS" table and a summary table 'MONTHLY REPORTS'. The 'TRANSACTIONS' table is frequently updated via streams and tasks. Which combination of the following strategies would be MOST effective in identifying and resolving these inconsistencies in near real-time?

A) Implement a Snowflake task that periodically recalculates the 'MONTHLY_REPORTS' table from the 'TRANSACTIONS table and compares the results with the existing data, logging any discrepancies. Use a smaller warehouse size to minimize cost.
B) Use Snowflake's row access policies to restrict access to the 'TRANSACTIONS' table, forcing users to only access the 'MONTHLY REPORTS table.
C) Utilize Snowflake's Time Travel feature to compare the ' TRANSACTIONS' table and 'MONTHLY _ REPORTS' table at a specific point in time and identify the changes that led to the discrepancies.
D) Create a Snowflake alert that triggers when the difference in the total 'SALE_AMOUNT between the 'TRANSACTIONS' table and 'MONTHLY REPORTS' exceeds a predefined threshold within a specified time window.
E) Implement data validation checks within the data pipeline (streams and tasks) that update the 'TRANSACTIONS' table to reject transactions that violate predefined business rules.

Solutions: