Data Science, Data Engineering and Data Analytics employees at Winnebago write SQL queries all the time as part of their job. They use SQL for analyzing customer purchase data for trend analysis and managing inventory databases to aid in effective production planning. That's why Winnebago typically asks folks interviewing for data jobs SQL interview questions.
Thus, to help you prep, here’s 9 Winnebago SQL interview questions – able to answer them all?
Winnebago, a renowned company for manufacturing motorhomes and towable camping trailers, considers its VIP or whale customers as those who buy their products more frequently. This serves as an important activity that drives the business. Given this, can you write a SQL query to identify these power users?
The criteria for identification of these power users are as follows:
You are given two tables and .
buyer_id | first_name | last_name |
---|---|---|
101 | Micheal | Smith |
102 | John | Doe |
103 | Jerry | Andrew |
104 | Andy | Clarke |
105 | Sylvia | Plath |
purchase_id | buyer_id | product_id | purchase_date |
---|---|---|---|
1 | 101 | 201 | 01/09/2022 |
2 | 101 | 203 | 01/10/2022 |
3 | 101 | 204 | 01/12/2022 |
4 | 102 | 202 | 01/15/2022 |
5 | 101 | 205 | 01/18/2022 |
6 | 101 | 206 | 01/25/2022 |
7 | 103 | 201 | 02/06/2022 |
8 | 104 | 202 | 02/08/2022 |
9 | 105 | 203 | 02/12/2022 |
The above PostgreSQL query first creates a CTE (Common Table Expressions) that groups buyer purchases on a monthly basis. It then joins this CTE with the table to get the remaining buyer details. The clause filters out only those who purchased 5 times or more each month, and the clause ensures that the power users from the most recent month are listed first.
To work on a similar customer analytics SQL question where you can solve it right in the browser and have your SQL code instantly graded, try this Walmart SQL Interview Question:
Suppose there was a table of Winnebago employee salary data. Write a SQL query to find the 2nd highest salary amongst all the .
employee_id | salary |
---|---|
1 | 2500 |
2 | 800 |
3 | 1000 |
4 | 1200 |
second_highest_salary |
---|
1200 |
Write a SQL query for this problem directly within the browser on DataLemur:
You can find a step-by-step solution with hints here: 2nd Highest Salary.
Check out the Winnebago career page and see where you might fit best!
To find records in one table that aren't in another, you can use a and check for values in the right-side table.
Here is an example using two tables, Winnebago employees and Winnebago managers:
This will return all rows from Winnebago employees where there is no matching row in managers based on the column.
You can also use the operator in PostgreSQL and Microsoft SQL Server to return the records that are in the first table but not in the second. Here is an example:
This will retrieve all rows from employees that do not appear in managers. The operator works by retreivingthe rows that are returned by the first query, but not by the second.
Please note that is not supported by all DBMS systems, such as MySQL and Oracle (however, you can use the operator to achieve a similar outcome).
Winnebago, a major manufacturer of recreational vehicles (RVs), wants to track customer satisfaction for their products. They've asked you to write a SQL query that calculates the monthly average of the review ratings for each product.
Winnebago collects customer reviews that include a star rating (1-5) and the date the review was submitted. They would like to see each product's average review score, broken down by month.
review_id | user_id | submit_date | product_id | stars |
---|---|---|---|---|
6171 | 123 | 2022-06-08 | 50001 | 4 |
7802 | 265 | 2022-06-10 | 69852 | 4 |
5293 | 362 | 2022-06-18 | 50001 | 3 |
6352 | 192 | 2022-07-26 | 69852 | 3 |
4517 | 981 | 2022-07-05 | 69852 | 2 |
month | product_id | avg_stars |
---|---|---|
6 | 50001 | 3.50 |
6 | 69852 | 4.00 |
7 | 69852 | 2.50 |
In this query, we're using PostgreSQL's EXTRACT function to get the month from the column. Then we're grouping by month and to calculate the average rating () for each product each month. Finally, we're ordering the results by month and to easily see how each product's ratings evolve over time.
To practice a similar window function question on DataLemur's free online SQL coding environment, solve this Google SQL Interview Question:
The keyword added to a statement can be used to get records without duplicates.
For example, say you had a table of Winnebago customers:
name | city |
---|---|
Akash | SF |
Brittany | NYC |
Carlos | NYC |
Diego | Seattle |
Eva | SF |
Faye | Seattle |
Suppose you wanted to figure out which cities the customers lived in, but didn't want duplicate results, you could write a query like this:
Your result would be:
city |
---|
SF |
NYC |
Seattle |
As a data analyst at Winnebago, you're tasked with determining the performance of different product models in terms of sales revenue. You have two tables – and . The table logs every sale made, including the product model sold, the sale price, and the sale date. The table contains details on each model, including a unique model id and model name.
Calculate the average sales revenue per Winnebago model for the year 2022.
Here are some example inputs:
sale_id | sale_date | product_model_id | sale_price |
---|---|---|---|
111 | 2022-01-10 | 1 | 80000 |
222 | 2022-02-15 | 2 | 85000 |
333 | 2022-03-20 | 1 | 79000 |
444 | 2022-04-25 | 3 | 92000 |
555 | 2022-05-30 | 2 | 88000 |
product_model_id | model_name |
---|---|
1 | Winnebago Minnie |
2 | Winnebago Micro Minnie |
3 | Winnebago Voyage |
This PostgreSQL query uses inner join to combine and on , and the WHERE clause is used to filter sales only from the year 2022. The AVG function calculates the average for each model (grouped by ).
model_name | average_sales_2022 |
---|---|
Winnebago Minnie | 79500 |
Winnebago Micro Minnie | 86500 |
Winnebago Voyage | 92000 |
First things first, figure out why the query is slow! You can use and commands in PostgreSQL to identify any performance bottlenecks. You might discover that your query is inefficient, or that there are many database writes at the same time you are doing a read, or maybe too many people are concurrently running queries on the same database server.
For Data Analyst and Data Science positions, knowing the ins-and-outs of SQL performance tuning is out-of-scope for the SQL interview round. However, knowing that joins are expensive, and indexes can speed up queries, is generally enough of an answer for Winnebago SQL interviews.
Given the customer database for Winnebago, you have to identify all customers with their first or last names starting with 'A'. In addition, include the city in which these customers live for further segregation of the output.
customer_id | first_name | last_name | city |
---|---|---|---|
1624 | Aaron | James | Los Angeles |
1502 | Abigail | Bennet | Chicago |
2435 | Anna | Davids | New York |
3715 | Jennifer | Arnold | Los Angeles |
7846 | Alfred | Branning | Seattle |
customer_id | first_name | last_name | city |
---|---|---|---|
1624 | Aaron | James | Los Angeles |
1502 | Abigail | Bennet | Chicago |
2435 | Anna | Davids | New York |
7846 | Alfred | Branning | Seattle |
In this query, we are asking the PostgreSQL server to fetch all columns from the 'customers' table where the first_name or the last_name starts with 'A'. The 'LIKE' keyword is used in conjunction with the '%' wildcard to identify patterns in the text data. In this case, the search pattern is any text that starts with 'A', signified by the '%' symbol following 'A'.
Winnebago wants to calculate the average fuel efficiency for all of their models. The fuel efficiency is calculated by taking the sum of trip distances divided by the total fuel consumed for each car model. The company wants rounded values of the calculated average fuel efficiency.
They would also like to know the absolute difference in efficiencies of two of their models - 'Journey' and 'Minnie Winnie', given they have the same number of trips.
Keep in mind that the units for are in miles and the units for are in gallons.
Use the table for this problem.
trip_id | model | trip_distance | fuel_consumed |
---|---|---|---|
345 | Journey | 100 | 10 |
346 | Minnie Winnie | 200 | 25 |
347 | Journey | 300 | 30 |
348 | Minnie Winnie | 150 | 18 |
349 | Journey | 250 | 25 |
In the above query,
The first part of the query calculates the average fuel efficiency for each model of the car and rounds it off to the nearest whole number using the ROUND() function.
The next part of the query selects the average fuel efficiencies of 'Journey' and 'Minnie Winnie' models.
Finally, the UNION ALL clause combines these results with the absolute difference in efficiencies of these two models calculated using the ABS() function.
The MAX(avg_efficiency) OVER () and MIN(avg_efficiency) OVER () functions fetch the maximum and minimum efficiencies from the result set to calculate the absolute difference.
To practice a very similar question try this interactive Alibaba Compressed Mean Question which is similar for calculating averages of performance metrics or this Amazon Average Review Ratings Question which is similar for the need to average performance over a set of products.
The key to acing a Winnebago SQL interview is to practice, practice, and then practice some more! Beyond just solving the above Winnebago SQL interview questions, you should also solve the 200+ tricky sql questions on DataLemur which come from companies like Netflix, Airbnb, and Amazon.
Each problem on DataLemur has hints to guide you, detailed solutions and best of all, there is an interactive coding environment so you can instantly run your query and have it graded.
To prep for the Winnebago SQL interview you can also be useful to practice SQL questions from other automotive companies like:
However, if your SQL foundations are weak, don't worry about diving straight into solving questions – refresh your SQL knowledge with this SQL interview tutorial.
This tutorial covers things like joining multiple tables and filtering data with boolean operators – both of which come up frequently in Winnebago SQL assessments.
In addition to SQL interview questions, the other types of problems covered in the Winnebago Data Science Interview are:
The best way to prepare for Winnebago Data Science interviews is by reading Ace the Data Science Interview. The book's got: