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

1. Snowpark DataFrame 'employee_df' contains employee data, including 'employee_id', 'department', and 'salary'. You need to calculate the average salary for each department and also retrieve all the employee details along with the department average salary.
Which of the following approaches is the MOST efficient way to achieve this?

A) Use a correlated subquery within the SELECT statement to calculate the average salary for each department for each employee.
B) Use 'groupBV to get a dataframe containing average salary by department and then use a Python UDF to iterate through the 'employee_df and add the value to each row
C) Create a separate DataFrame with average salaries per department, then join it back to the original DataFrame.
D) Create a temporary table with average salaries per department, then join it back to the original DataFrame.
E) Use the 'window' function with 'avg' to compute the average salary per department and include it as a new column in the original DataFrame.

2. A retail company wants to store product data in a Snowflake VARIANT column. The product data is currently in a relational table called 'PRODUCTS' with columns 'PRODUCT ID', 'PRODUCT NAME, 'CATEGORY, 'PRICE, and 'DISCOUNT. They want to create a JSON structure where each product is represented as a JSON object, and the entire result set is a JSON array. Which of the following SQL statements will achieve this transformation most efficiently?

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

3. Which of the following statements are true regarding data masking policies in Snowflake? (Select all that apply)

A) Once a masking policy is applied to a column, the original data is permanently altered.
B) Data masking policies can be applied to both tables and views.
C) Different masking policies cannot be applied to different columns within the same table.
D) Data masking policies are supported on external tables.
E) The 'CURRENT_ROLE()' function can be used within a masking policy to implement role-based data masking.

4. You are tasked with implementing row-level filtering on a 'customers' table in Snowflake. You need to restrict access to customer data based on the user's region. The 'customers' table has a 'region' column, and you have a mapping table 'user regions' that associates users with the regions they are allowed to access. The 'user_regions' table has columns 'username' and 'region'. Which of the following SQL statements correctly creates and applies a row access policy to achieve this, minimizing complexity and maximizing performance? Select all that apply:

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

5. You are responsible for monitoring data quality in a Snowflake data warehouse. Your team has identified a critical table, 'CUSTOMER DATA, where the 'EMAIL' column is frequently missing or contains invalid entries. You need to implement a solution that automatically detects and flags these anomalies. Which of the following approaches, or combination of approaches, would be MOST effective in proactively monitoring the data quality of the 'EMAIL' column?

A) Use Snowflake's Data Quality features (if available) to define data quality rules for the 'EMAILS column, specifying acceptable formats and thresholds for missing values. Configure alerts to be triggered when these rules are violated.
B) Schedule a daily full refresh of the 'CUSTOMER DATA' table from the source system, overwriting any potentially corrupted data.
C) Create a Snowflake Task that executes a SQL query to count NULL 'EMAIL' values and invalid 'EMAIL' formats (using regular expressions). The task logs the results to a separate monitoring table and alerts the team if the count exceeds a predefined threshold.
D) Utilize an external data quality tool (e.g., Great Expectations, Deequ) to define and run data quality checks on the 'CUSTOMER DATA' table, integrating the results back into Snowflake for reporting and alerting.
E) Implement a Streamlit application connected to Snowflake that visualizes the percentage of NULL and invalid 'EMAIL' values over time, allowing the team to manually monitor trends.

Solutions: