8 Land O'Lakes SQL Interview Questions (Updated 2024)
Data Scientists, Analysts, and Data Engineers at Land O'Lakes rely on SQL queries to analyze agricultural data, which helps them optimize crop yields and improve farming practices. They also manage dairy supply chain databases through SQL to enhance distribution efficiency, ensuring that products reach consumers quickly and reliably, this is the reason why Land O'Lakes often tests jobseekers with SQL interview problems.
To help prep you for the Land O'Lakes SQL interview, we've collected 8 Land O'Lakes SQL interview questions in this blog.
8 Land O'Lakes SQL Interview Questions
SQL Question 1: Dairy Production Statistical Analysis
Land O'Lakes is a huge cooperative that supplies a variety of dairy products distributed across several locations. Suppose you are given a dataset that records the amount of milk harvested by farmers spread across different states in each quarter of the year. The table contains data for several years, and your task is to write a SQL query to calculate the quarterly growth rate in the milk production for each state. The growth rate is defined as (Current Quarter production - Previous Quarter production) / (Previous Quarter production) * 100.
We will analyze the table in the following structure:
Example Input:
| id | state | year | quarter | production |
|---|---|---|---|---|
| 1 | Minnesota | 2019 | 1 | 5000.00 |
| 2 | Minnesota | 2019 | 2 | 5100.00 |
| 3 | Minnesota | 2019 | 3 | 5200.00 |
| 4 | Minnesota | 2019 | 4 | 5300.00 |
| 5 | Wisconsin | 2019 | 1 | 7000.00 |
| 6 | Wisconsin | 2019 | 2 | 7200.00 |
| 7 | Wisconsin | 2019 | 3 | 7400.00 |
| 8 | Wisconsin | 2019 | 4 | 7600.00 |
Let's calculate the growth rate for each quarter.
Answer:
This query calculates the production growth rate for each state on a quarterly basis. It uses a window function to obtain the value from the previous row (previous quarter), and subsequently calculates the growth rate. Since the window function is partitioned by , it calculates the growth rate correctly for each state independently. The sort order influences the outcome, so the data is sorted by , , and to reflect the exact chronological order of milk production.
For more window function practice, try this Uber SQL problem on DataLemur's interactive coding environment:
Explore Land O’Lakes' innovative approach to agriculture, where they discuss how technology is transforming the farming industry for a sustainable future! Understanding their strategies can provide valuable insights into how agricultural cooperatives are adapting to modern challenges and enhancing productivity.
SQL Question 2: Top 3 Salaries
Assume there was a table of Land O'Lakes employee salary data. Write a SQL query to find the top 3 highest earning employees within each department.
Land O'Lakes 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 |
Write a SQL query for this problem directly within the browser 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 code above is hard to understand, you can find a detailed solution with hints here: Top 3 Department Salaries.
SQL Question 3: What do stored procedures do, and when would you use one?
Stored procedures are a lot like functions in programming. They're used to encapsulate and organize business logic into one unit of code, and they can accept multiple input parameters and return multiple output values.
For a concrete example, suppose you were a Data Scientist on the Marketing Analytics team at Land O'Lakes. A task you would encounter freqently would be to calculate the conversion rate for Land O'Lakes's ads over a certain time period, for which you would write the following stored procedure:
To use this conversion rate stored procedure, you could call it like this:
SQL Question 4: Filter Customers Based on Multiple Conditions
Land O'Lakes, an agriculture company, aims to understand their customer base better. They are specifically interested in customers in Minnesota who have purchased goods in or after 2020 and have spent more than $200 overall. The records are stored in a table and a table. Can you write a SQL query to filter these customers from the databases?
Example Input:
| customer_id | name | state |
|---|---|---|
| 1 | John Smith | Minnesota |
| 2 | Jane Smith | Minnesota |
| 3 | David Johnson | Texas |
| 4 | Lucy Brown | Minnesota |
Example Input:
| transaction_id | customer_id | purchase_year | amount |
|---|---|---|---|
| 1001 | 1 | 2020 | 100.00 |
| 1002 | 1 | 2021 | 150.00 |
| 1003 | 2 | 2019 | 50.00 |
| 1004 | 2 | 2021 | 250.00 |
| 1005 | 3 | 2021 | 300.00 |
| 1006 | 4 | 2019 | 200.00 |
Example Output:
| customer_id | name |
|---|---|
| 1 | John Smith |
| 2 | Jane Smith |
Answer:
The query first joins the and tables using their common field. It then filters customers based on their state and the year they made their purchases. Finally, it groups the records by customer and checks if their total spending is over 200 in total.
SQL Question 5: What's the difference between an inner and a full outer join?
An inner join only includes rows from both tables that satisfy the join condition, whereas a full outer join includes all rows from both tables, regardless of whether they match the join condition or not.
To demonstrate each kind, Imagine you were working on a Advertising Analytics project at Land O'Lakes and had two database tables: an table that contains data on Google Ads keywords and their bid amounts, and a table with information on product sales and the Google Ads keywords that drove those sales.
An retrieves rows from both tables where there is a match in the shared key or keys. For example, an INNER JOIN between the table and the table could be performed using the keyword column as the shared key. This would retrieve only the rows where the keyword in the table matches the keyword in the table.
A retrieves all rows from both tables, regardless of whether there is a match in the shared key or keys. If there is no match, values will be returned for the columns of the non-matching table.
SQL Question 6: Join and Analyze Customer and Product Tables
Land O'Lakes is an agricultural company which has a database of customers and the products they purchase. There are two tables, and . The table have the following fields: , , , , and . The table has the following fields: , , , and .
The task is to write a SQL query to count the number of customers for each product category, order the result by the count in descending order. The output columns should be and .
Example Input:
| customer_id | first_name | last_name | product_id | |
|---|---|---|---|---|
| 1 | John | Doe | john.doe@email.com | 1 |
| 2 | Jane | Smith | jane.smith@email.com | 2 |
| 3 | Mary | Johnson | mary.johnson@email.com | 3 |
| 4 | James | Brown | james.brown@email.com | 1 |
| 5 | Emily | Davis | emily.davis@email.com | 2 |
Example Input:
| product_id | name | category | price |
|---|---|---|---|
| 1 | Butter | Dairy | 5.99 |
| 2 | Cheese | Dairy | 3.99 |
| 3 | Spinach | Vegetable | 1.29 |
| 4 | Chicken | Meat | 7.49 |
| 5 | Bread | Grains | 2.49 |
Example Output:
| category | count |
|---|---|
| Dairy | 3 |
| Vegetable | 1 |
Answer:
Here is the SQL query that will give us the required result.
This query will first join and tables on the column . The grouping is then performed on the basis of in the table. The will provide the number of customers associated with each category. Ordering the result in descending order of count will show the categories with the highest number of customers at the top.
Because join questions come up routinely during SQL interviews, practice this SQL join question from Spotify:
SQL Question 7: In SQL, what's the primary difference between the 'BETWEEN' and 'IN' operators?
The operator is used to select rows that fall within a certain range of values, while the operator is used to select rows that match values in a specified list.
For example, suppose you are a data analyst at Land O'Lakes and have a table of advertising campaign data. To find campaigns with a spend between 5k, you could use :
To find advertising campaigns that were video and image based (as opposed to text or billboard ads), you could use the operator:
SQL Question 8: Calculating Production Costs of Dairy Products
Land O'Lakes is a large company that produces a variety of dairy products. They need an SQL query that can calculate the total production cost of each product per batch, rounded to the nearest dollar. The production cost is calculated using the formula:
In addition, they want to know the square root of the processing cost for each product and the absolute difference between processing cost and total production cost.
Suppose they have two tables: and
Example Input:
| product_id | product_name | milk_quantity(liters) | price_per_liter | additive_quantity(kg) | price_per_kg |
|---|---|---|---|---|---|
| 1001 | Butter | 2500 | 1 | 50 | 10 |
| 1002 | Cheese | 2000 | 1 | 40 | 15 |
| 1003 | Yoghurt | 1500 | 1 | 30 | 10 |
Example Input:
| product_id | processing_cost |
|---|---|
| 1001 | 500 |
| 1002 | 300 |
| 1003 | 200 |
Answer:
This query joins the table with the table on the . It calculates the total production cost for each product per batch rounding to the nearest dollar. The query also computes the square root of the processing cost and the absolute difference between the processing cost and the calculated production cost.
To practice a very similar question try this interactive Alibaba Compressed Mean Question which is similar for calculating means or this Mckinsey 3-Topping Pizzas Question which is similar for calculating costs.
How To Prepare for the Land O'Lakes SQL Interview
The key to acing a Land O'Lakes SQL interview is to practice, practice, and then practice some more! Beyond just solving the earlier Land O'Lakes SQL interview questions, you should also solve the 200+ DataLemur SQL Interview Questions which come from companies like Microsoft, Google, Amazon, and tech startups.
Each DataLemur SQL question has hints to guide you, full answers and best of all, there is an interactive coding environment so you can easily right in the browser your SQL query and have it executed.
To prep for the Land O'Lakes SQL interview it is also helpful to solve interview questions from other food and facilities companies like:
However, if your SQL coding skills are weak, forget about jumping right into solving questions – improve your SQL foundations with this SQL tutorial for Data Scientists & Analysts.
This tutorial covers SQL concepts such as creating summary stats with GROUP BY and SUM/AVG window functions – both of these show up frequently in SQL job interviews at Land O'Lakes.
Land O'Lakes Data Science Interview Tips
What Do Land O'Lakes Data Science Interviews Cover?
Beyond writing SQL queries, the other question categories to prepare for the Land O'Lakes Data Science Interview are:
- Probability & Statistics Questions
- Python or R Coding Questions
- Product Metrics Interview Questions
- ML Modelling Questions
- Behavioral Questions focussed on Land O'Lakes company values
How To Prepare for Land O'Lakes Data Science Interviews?
To prepare for Land O'Lakes Data Science interviews read the book Ace the Data Science Interview because it's got:
- 201 interview questions sourced from FAANG & startups
- a crash course on SQL, Product-Sense & ML
- over 1000+ reviews on Amazon & 4.5-star rating
Don't forget about the behavioral interview – prep for it using this list of common Data Scientist behavioral interview questions.