10 Dana Incorporated SQL Interview Questions (Updated 2024)
[Dana Incorporated](https://www.Dana Incorporated.com) employees use SQL daily for analyzing manufacturing process data for efficiencies and querying databases to predict future parts inventory needs. For this reason Dana Incorporated frequently asks SQL query questions during interviews for Data Analytics, Data Science, and Data Engineering jobs.
Thus, to help you prep, we've curated 10 Dana Incorporated SQL interview questions – able to answer them all?
10 Dana Incorporated SQL Interview Questions
SQL Question 1: Calculate the Monthly Average Price for Each Product
As a business analyst at Dana Inc, you are tasked to analyze the monthly selling prices for each product over the past year to understand the price variation. Dana Inc is a worldwide supplier of drivetrain, sealing, and thermal-management technologies.
You have been given a dataset containing information regarding each product sold, such as , (the date when the product was sold), and (the price at which the product was sold).
Write a SQL query to calculate the monthly average selling price for each product. For simplification, assume that the is always the last day of the month when sale happened.
Example Input:
| sale_id | product_id | sale_date | sale_price |
|---|---|---|---|
| 111 | P1001 | 01/31/2022 | 120 |
| 222 | P1002 | 01/31/2022 | 150 |
| 333 | P1001 | 01/31/2022 | 130 |
| 444 | P1001 | 02/28/2022 | 110 |
| 555 | P1003 | 02/28/2022 | 180 |
| 666 | P1002 | 02/28/2022 | 145 |
| 777 | P1003 | 02/28/2022 | 190 |
| 888 | P1001 | 03/31/2022 | 125 |
| 999 | P1002 | 03/31/2022 | 155 |
Answer:
This query first extracts the month and year from the column. It then calculates the average selling price for each product (), broken down by month and year. This average is computed using a window function (), partitioned by , month and year. Finally, the result is sorted by year, month, and product_id to bring a clean view.
This SQL window function is particularly useful as it provides flexibility to analyze column's data based on a defined window frame - in this case, the product and the sale month and year.
Pro Tip: Window functions are a popular SQL interview topic, so practice all the window function problems on DataLemur
SQL Question 2: Top Three Salaries
Given a table of Dana Incorporated employee salary information, write a SQL query to find the top 3 highest earning employees within each department.
Dana Incorporated Example Input:
| employee_id | name | salary | department_id |
|---|---|---|---|
| 1 | Emma Thompson | 3800 | 1 |
| 2 | Daniel Rodriguez | 2230 | 1 |
| 3 | Olivia Smith | 2000 | 1 |
| 4 | Noah Johnson | 6800 | 2 |
| 5 | Sophia Martinez | 1750 | 1 |
| 8 | William Davis | 6800 | 2 |
| 10 | James Anderson | 4000 | 1 |
Example Input:
| department_id | department_name |
|---|---|
| 1 | Data Analytics |
| 2 | Data Science |
Example Output:
| department_name | name | salary |
|---|---|---|
| Data Analytics | James Anderson | 4000 |
| Data Analytics | Emma Thompson | 3800 |
| Data Analytics | Daniel Rodriguez | 2230 |
| Data Science | Noah Johnson | 6800 |
| Data Science | William Davis | 6800 |
Test your SQL query for this question interactively on DataLemur:
Answer:
We use the DENSE_RANK() window function to generate unique ranks for each employee's salary within their department, with higher salaries receiving lower ranks. Then, we wrap this up in a CTE and filter the employees with a ranking of 3 or lower.
If the solution above is confusing, you can find a detailed solution here: Top 3 Department Salaries.
SQL Question 3: What's the difference between and clause?
The clause is used to filter rows from the result set of a , , or statement. It allows you to specify a condition that must be met for a row to be included in the result set.
The clause is used to filter groups created by the clause. It is similar to the clause, but it is used to specify conditions on the groups created by the clause, rather than on the individual rows of the table.
Say you were working on a social media analytics project for Dana Incorporated.
Here is an example of a SQL query that you might write which uses both the WHERE and HAVING clauses:
This query retrieves the total impressions and average conversions for each platform in the table, the date of the campaign is in January 2023. The rows are grouped by platform and the clause filters the groups to include only those with more than 5000 impressions and an average conversion rate above 0.2.
SQL Question 4: Analyzing Production Facilities Efficiency
Dana Incorporated wants to evaluate their production facilities' efficiency. They have many factories worldwide that produce different parts for various vehicle manufacturers.
For each order, they record the product part, quantity, the factory that produced it, and the time it took to complete the production order. They now want to know which factories are the most efficient (have the lowest average production time per part) for each product type.
They have two databases: and .
The table records the product part, quantity, the factory, and production time.
Example Input:
| order_id | product | quantity | factory_id | production_time_hours |
|---|---|---|---|---|
| 105 | Part_A | 500 | 1 | 25 |
| 323 | Part_B | 200 | 2 | 40 |
| 918 | Part_C | 450 | 3 | 60 |
| 476 | Part_A | 300 | 1 | 15 |
| 798 | Part_B | 600 | 2 | 60 |
The table contains details about each factory.
Example Input:
| factory_id | factory_location |
|---|---|
| 1 | Ohio |
| 2 | Michigan |
| 3 | Pennsylvania |
Your task is to write a SQL query that returns a table with the product part, the most efficient factory location (with the lowest average hours per part), and the average production time per part for this factory.
Answer:
This query first calculates the production time per part for each order. It then groups the orders by product and factory, calculating the average production time per part for each group. The result is sorted to show the most efficient factories (with the lowest average production time per part) first.
SQL Question 5: When would you use a clustered index vs. a non-clustered index?
Here's an example of a clustered index on the column of a table of Dana Incorporated payments table:
This creates a clustered index on the column, which will determine the physical order of the data rows in the table.
Here is an example of a non-clustered index on the column of the same table:
This will create a non-clustered index on the column, which will not affect the physical order of the data rows in the table.
In terms of query performance, a clustered index is usually faster for searches that return a large number of records, while a non-clustered index is faster for searches that return a small number of records. However, updates to a clustered index are slower, as they require the data rows to be physically rearranged, while updates to a non-clustered index are faster, as they only require the index data structure to be updated.
SQL Question 6: Calculate Click-Through-Rate for Dana Incorporated
Dana Incorporated wants to analyze the click-through rates (CTR) of its product ads in the previous month. The CTR is calculated as the total number of user clicks on an ad divided by the total number of times the ad was shown (impressions), expressed as a percentage.
The database contains two tables: 'ads' and 'clicks'. The 'ads' table contains information about the id, product, and the time the ad was displayed. The 'clicks' table keeps track of when and what ads were clicked by each user.
Example Input:
| ad_id | product_id | display_time |
|---|---|---|
| 101 | 50001 | 06/01/2022 00:00:00 |
| 102 | 69852 | 06/02/2022 00:00:00 |
| 103 | 50001 | 06/03/2022 00:00:00 |
| 104 | 69852 | 06/04/2022 00:00:00 |
| 105 | 50001 | 06/05/2022 00:00:00 |
Example Input:
| click_id | user_id | ad_id | click_time |
|---|---|---|---|
| 2001 | 123 | 101 | 06/02/2022 00:00:00 |
| 2002 | 265 | 102 | 06/03/2022 00:00:00 |
| 2003 | 362 | 103 | 06/04/2022 00:00:00 |
| 2004 | 192 | 104 | 06/05/2022 00:00:00 |
| 2005 | 981 | 105 | 06/06/2022 00:00:00 |
Answer:
This query calculates the click-through rate for each product in the month of June. It first joins the 'ads' and 'clicks' tables on the 'ad_id' column, then filters the data for the specified time period. The CTR is calculated for each product by dividing the total number of clicks by the total number of impressions, and multiplying the result by 100 to get the rate in percentage. The GROUP BY statement groups the results per product_id.
To practice a related SQL problem on DataLemur's free interactive SQL code editor, attempt this Meta SQL interview question:
SQL Question 7: How can you select records without duplicates from a table?
"The clause in SQL allows you to select records that are unique, eliminating duplicates.
For a tangible example, say you had a table of Dana Incorporated employees:
| first_name | job_title |
|---|---|
| Akash | Data Analyst |
| Brittany | Data Scientist |
| Carlos | Data Engineer |
| Diego | Data Engineer |
| Eva | Data Analyst |
If you were doing an HR Analytics project and you wanted to get all the unique job titles that currently worked at the company, you would write the following SQL query:
The output would give you 3 distinct job titles at Dana Incorporated:
| job_title |
|---|
| Data Analyst |
| Data Scientist |
| Data Engineer |
SQL Question 8: Determine the Most Sold Product
Dana Incorporated is a world leader in supplying drivetrain, sealing, and thermal-management technologies. You've been provided with a dataset of their product sales for the last quarter. Write an SQL query to determine which product category had the highest total sales amount for the last quarter.
Example Input:
| sale_id | product_category | sale_date | unit_sold | price_per_unit |
|---|---|---|---|---|
| 10021 | Drivetrain | 2022-04-15 | 20 | 100 |
| 10132 | Sealing | 2022-04-20 | 50 | 80 |
| 10087 | Thermal-management | 2022-04-25 | 30 | 120 |
| 10065 | Drivetrain | 2022-05-10 | 45 | 100 |
| 10055 | Sealing | 2022-05-20 | 60 | 80 |
| 10075 | Thermal-management | 2022-06-10 | 30 | 120 |
| 10105 | Drivetrain | 2022-06-25 | 50 | 100 |
| 10015 | Sealing | 2022-06-30 | 70 | 80 |
Expected Output:
| product_category | total_sales |
|---|---|
| Drivetrain | 11500 |
| Sealing | 14400 |
| Thermal-management | 9000 |
Answer:
In the query above, we calculate the total_sales of each product_category by multiplying the numbers of units sold by their price. We then group by product_category to get total sales for each category. The WHERE clause is used to filter the sales that occurred in the last quarter, and finally we sort the result in descending order of total_sales.
SQL Question 9: Join and Analyze Customer and Purchase Data
Given two tables, one named that contains information about each customer and another named that contains information about each purchase by customer, write a SQL query to find out the total number of purchases and the total amount of money spent by each customer. Join the table with table to solve it.
The table has a structure as follows:
Example Input:
| customer_id | first_name | last_name |
|---|---|---|
| 1 | John | Doe |
| 2 | Jane | Smith |
| 3 | Bob | Johnson |
| 4 | Alice | Williams |
| 5 | Charlie | Brown |
And table has a structure as follows:
Example Input:
| purchase_id | customer_id | amount |
|---|---|---|
| 1 | 1 | 100.00 |
| 2 | 2 | 200.00 |
| 3 | 3 | 300.00 |
| 4 | 1 | 400.00 |
| 5 | 2 | 500.00 |
The expected output would provide the first name, last name, total number of purchases, and total amount spent by each customer.
Example Output:
| first_name | last_name | total_purchases | total_spent |
|---|---|---|---|
| John | Doe | 2 | 500.00 |
| Jane | Smith | 2 | 700.00 |
| Bob | Johnson | 1 | 300.00 |
| Alice | Williams | 0 | 0.00 |
| Charlie | Brown | 0 | 0.00 |
Answer:
Here's a PostgreSQL query that can be used to solve this problem:
This query uses a to combine the and tables. This ensures that all customers are included in the final output, even if they did not make any purchases. This is reflected in the number of total purchases and the total amount spent by each customer. If a customer did not make any purchases, the function is used to replace the NULL values with 0. This result set is then sorted by the first and last name of the customers.
Because join questions come up frequently during SQL interviews, practice this interactive Snapchat Join SQL question:
SQL Question 10: Can you explain the difference between the and window functions in SQL?
As the name implies, the window function ranks each row within your window/partition. If two rows have the same rank, the next number in the ranking will be the previous rank plus the number of duplicates. For example, if you've got three records tied for 5th place, they'll get the values 5, 6, and 7, and the next row after this 3-way tie will have a rank of 8.
The function assigns a distinct rank to each row within a partition based on the provided column value with no gaps. This function will assign the same rank to two rows if they have the same rank, and give the next row the next rank number. To make this more concrete, imagine you had three records at rank 5 – then, the next rank would be 6.
How To Prepare for the Dana Incorporated SQL Interview
The best way to prepare for a SQL interview, besides making sure you have strong SQL fundamentals, is to practice a ton of real SQL questions that were asked in recent job interviews.
Besides solving the above Dana Incorporated SQL interview questions, you should also solve the 200+ SQL questions from real Data Science & Analytics interviews which come from companies like Google, Facebook, Microsoft and Amazon.
Each exercise has hints to guide you, fully explained answers along with a discussion board to see how others solved it and best of all, there's an interactive SQL code editor so you can right in the browser run your SQL query and have it executed.
To prep for the Dana Incorporated SQL interview you can also be wise to practice interview questions from other automotive companies like:
In case your SQL coding skills are weak, don't worry about going right into solving questions – refresh your SQL knowledge with this DataLemur SQL tutorial.
This tutorial covers things like LEAD/LAG window functions and handling NULLs in SQL – both of which pop up frequently during Dana Incorporated interviews.
Dana Incorporated Data Science Interview Tips
What Do Dana Incorporated Data Science Interviews Cover?
Beyond writing SQL queries, the other types of questions to practice for the Dana Incorporated Data Science Interview include:
- Probability & Stats Questions
- Python or R Coding Questions
- Product Data Science Interview Questions
- ML Interview Questions
- Behavioral & Resume-Based Questions
How To Prepare for Dana Incorporated Data Science Interviews?
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