Databricks Certified Data Engineer - Associate
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As a Data Engineer at a multinational corporation, you are tasked with integrating and analyzing employee performance data to support HR decision-making. The data is stored in two distinct formats within your Databricks environment: a JSON string containing employee details ('id', 'name', 'department', 'salary') and a table named 'performance_reviews' with fields ('employee_id', 'review_date', 'performance_rating'). Your objective is to parse the JSON string into a structured table and join it with the 'performance_reviews' table for comprehensive analysis. Given the importance of accuracy and efficiency in your analysis, and considering the need to include all employees in the results, even those without performance reviews, which of the following Spark SQL queries would you use? Choose the two most correct options from the five provided._
A
SELECT * FROM employees e JOIN performance_reviews p ON e.id = p.employee_id*
B
SELECT e.id, e.name, e.department, e.salary, p.review_date, p.performance_rating FROM employees e JOIN performance_reviews p ON e.id = p.employee_id
C
SELECT e.id, e.name, e.department, e.salary, p.review_date, p.performance_rating FROM employees e JOIN performance_reviews p ON e.employee_id = p.employee_id_
D
SELECT e.id, e.name, e.department, e.salary, p.review_date, p.performance_rating FROM employees e JOIN performance_reviews p ON e.id = p.employee_id WHERE e.department = 'Sales'
E
SELECT e.id, e.name, e.department, e.salary, p.review_date, p.performance_rating FROM employees e LEFT JOIN performance_reviews p ON e.id = p.employee_id