11 Corteva SQL Interview Questions (Updated 2024)

Updated on

October 31, 2024

Data Analysts and Data Scientists at Corteva rely on SQL to analyze agricultural data for crop yield predictions, enabling farmers to make informed decisions about planting and harvesting. They also use it to manage data on bioinformatics, supporting genetic research that can lead to more resilient crops, which is why, Corteva asks interviewees SQL interview questions.

As such, to help you practice for the Corteva SQL interview, we've curated 11 Corteva SQL interview questions – scroll down to start solving them!

Corteva SQL Interview Questions

11 Corteva SQL Interview Questions

SQL Question 1: Identify Power Users in Corteva's database

Corteva is a company that is engaged in the business of agriculture and thus growing seasonal crops is an important activity for them. In the context of Corteva, a "Power User" can be defined as a farmer who buys a significant amount of seeds and/or farm equipment from them consistently. For this case, let's assume the significant amount is defined as greater than 100 units per month.

You're given the table which logs every single purchase a user makes.

Example Input:

purchase_iduser_idpurchase_dateitem_idquantity
100111002/05/20226200195
106512302/14/202262002105
115411002/28/202262003110
117942003/02/20226200180
120811003/05/202262002120

Your task is to write a SQL query that identifies these power users. The output should be a list of user IDs who have bought more than 100 units per month during the year 2022. Each user must be listed only once.

Answer:


This query first creates a temporary table where it groups purchases by user and month, and then it selects the distinct users from this table where the total purchases are more than 100 units in any given month.

To practice a similar power-user data analysis problem question on DataLemur's free interactive coding environment, try this recently asked Microsoft SQL interview question:

Microsoft SQL Interview Question: Teams Super User

SQL Question 2: Highly-Paid Employees

Imagine there was a table of Corteva employee salary data. Write a SQL query to find the employees who earn more than their own manager.

Corteva Example Input:

employee_idnamesalarydepartment_idmanager_id
1Emma Thompson38001
2Daniel Rodriguez2230110
3Olivia Smith800018
4Noah Johnson680028
5Sophia Martinez1750110
8William Davis70002NULL
10James Anderson40001NULL

Example Output:

employee_idemployee_name
3Olivia Smith

This is the output because Olivia Smith earns $8,000, surpassing her manager, William Davis who earns 7,800.

Check your SQL query for this interview question interactively on DataLemur:

Employees Earning More Than Their Manager

Answer:

First, we perform a SELF-JOIN where we treat the first table () as the managers' table and the second table () as the employees' table. Then we use a clause to filter the results, ensuring we only get employees whose salaries are higher than their manager's salary.


If the solution above is tough, you can find a detailed solution here: Employee Salaries Higher Than Their Manager.

SQL Question 3: When would you use denormalization?

Denormalization is the process of modifying a database schema in a way that deviates from the typical rules of normalization (1NF, 2NF, 3NF, etc.). There's a few reasons to denormalize a database:

Improved performance: Joins are slow AF, especially when dealing with the massive datasets that are typically used at Corteva. Denormalization can improve the performance of a database by reducing the number of joins that are required to retrieve data. This can be particularly useful when the database is being used for querying and reporting purposes, as joins can be expensive and slow.

Scalability: By reducing the amount of data that needs to be read and processed to execute a query, denormalization can enhance the scalability of a database. This can be useful when the database is anticipated to handle a large number of read-only queries (such as in OLAP use cases).

Ease of use: Denormalization can also make it easier for users to work with a database by providing them with a more intuitive and straightforward data model.

Because denormalization can create more complex update and delete operations, and pose potential data integrity issues, consider denormalization only if joins are causing performance bottlenecks.

Corteva SQL Interview Questions

SQL Question 4: Calculate the Average Monthly Yield per Crop

Corteva Agriscience is a major agriscience company that produces a variety of seeds. Write a SQL query that calculates the average monthly yield for each of their crops across multiple years. Use window functions to achieve this outcome.

Example Input:

IDCropYearMonthYield
1001Corn20200645.6
1002Soybean20200639.2
1003Corn20200751.3
1004Soybean20200741.7
1005Corn20210647.8
1006Soybean20210640.0
1007Corn20210754.1
1008Soybean20210742.8

Example Output:

CropYearMonthAvg_Yield
Corn20200645.6
Soybean20200639.2
Corn20200751.3
Soybean20200741.7
Corn20210646.7
Soybean20210639.6
Corn20210752.7
Soybean20210742.25

Answer:


This SQL query calculates the average yield for each crop by year and month. It does this by using a window function to calculate the average yield for each grouping of crop, year, and month.

p.s. Window functions show up super often during SQL interviews, so practice the 27+ window function questions on DataLemur

DataLemur SQL Questions

SQL Question 5: What is the difference between cross join and natural join?

A cross join is a type of JOIN operation in SQL that creates a new table by combining each row from the first table with every row from the second table. It is also known as a cartesian join.

For example, say you worked on the Marketing Analytics team at Corteva, and needed to understand what advertising copy (text) goes best with what advertising creative (the photo that gets posted with the advertisement copy).

Here is an example of a cross-join between a table of ad_copy and a table of ad_creative:


A natural join, on the other hand, is a type of JOIN that combines rows from two or more tables based on their common columns. It is called a "natural" join because it is based on the natural relationship that exists between the common columns in the tables being joined.

For an example of each one, say you had sales data exported from Corteva's Salesforce CRM stored in a datawarehouse which had two tables: and .

An (which is a type of natural join) combines the two tables on the common


This query will return rows from the sales and tables that have matching customer id values. Only rows with matching values will be included in the results.

One main difference between cross joins and natural joins is that cross joins do not require any common columns between the tables being joined, while natural joins do. Another difference is that cross joins can create very large tables if the input tables have a large number of rows, while natural joins will only return a table with the number of rows equal to the number of matching rows in the input tables.

SQL Question 6: Database Design for Crop Performance Management

As a database engineer working at Corteva, you are tasked with managing data related to the performance of various crop varieties in different farms. The business problem at hand is to keep track of the performance of each crop variety in each farm and determine which varieties are most successful in which locations.

You have two tables:

table stores information about each unique farm.

Example Input:

farm_idfarm_location
101Texas
202California
303Iowa
404Nebraska
505Ohio

table keeps track of the performance of each crop variety on each farm. Performance is rated between 1 (poor yield) to 5 (high yield).

Example Input:

idfarm_idcrop_varietyperformance_rating
1101Corn_1014
2202Corn_1013
3101Wheat_2025
4303Corn_1012
5404Wheat_2024
6505Corn_1013

The task is to write a SQL query that gives you the highest performing crop variety for each location.

Answer:


This query joins the two tables, and based on the . It then groups the data by and , and selects the highest for each group, hence identifying the highest performing crop variety for each farm location.

SQL Question 7: How can you determine which records in one table are not present in another?

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, Corteva employees and Corteva managers:


This will return all rows from Corteva 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).

SQL Question 8: Average Yield per Crop Type

Corteva, an agricultural company, wishes to analyze the yields of their various crop types. They have a database that includes a table tracking crop yields over several years. Can you write a SQL query to find the average yield for each crop type?

Example Input:

yield_idcrop_typeharvest_yearyield_in_bushels
1Corn2019180
2Corn2020190
3Soybeans201950
4Soybeans202055
5Wheat201940
6Wheat202042

Answer:


This SQL query groups the entries in the table by , and then calculates the average for each group. The function in PostgreSQL is used to compute the average of the specified numeric column.

To practice a very similar question try this interactive Amazon Highest-Grossing Items Question which is similar for grouping and calculating statistics or this Facebook Average Post Hiatus (Part 1) Question which is similar for dealing with time series data.

SQL Question 9: Find the average seeds yield per crop type for each farm.

Corteva is a major company in the agriculture industry. Let's say they want to understand the productivity of different crop types across their farms. The goal is to write a query that will retrieve the average seeds yield (in lbs) per crop type for each farm.

Example Input:

farm_idfarm_namelocation
1Green AcresIowa
2Sunny FieldCalifornia
3Dusty PlainsTexas

Example Input:

crop_idcrop_typefarm_idseeds_yield_per_acre
101Corn1500
102Soybeans1400
103Corn2550
104Soybeans3350
105Wheat3600

Answer:


This query joins the table and the table on the column. The clause groups the data by and . The function is then used to calculate the average seeds yield per acre for each group (i.e, each combination of farm and crop type). The result is a list of farms, crops, and their corresponding average yields, which can be very useful for analyzing the productivity of different crops across different farms.

SQL Question 10: Can you explain the purpose of the constraint?

The constraint is used to establish a relationship between two tables in a database. This ensures the referential integrity of the data in the database.

For example, if you have a table of Corteva and an table, the column in the orders table could be a that references the id column (which is the primary key) in the Corteva customers table.

SQL Question 11: Filter Customer Records Using LIKE

Corteva is an agroscience company providing seed and crop protection products to farmers. As such, their customer database holds information about farming sectors. Each sector is assigned a code with a specific naming convention: it begins with "AGRO," followed by a dash, two letters representing the sector, another dash, and finally an integer. For example, "AGRO-FR-58" or "AGRO-VG-12."

Your task is to filter through the customer records' database using the SQL keyword and extract all customer records belong to the Fruit (FR) & Veg (VG) sectors.

The table contains the following columns: . Make sure your result includes all fields from the table.

Example Input:

customer_idcustomer_namesector_code
1011Farmer JohnAGRO-FR-58
1012Farmer JaneAGRO-VG-12
1013Farmer AliceAGRO-FR-95
1014Farmer BobOTH-AN-52
1015Farmer CharlesOTH-CN-76

Example Output:

customer_idcustomer_namesector_code
1011Farmer JohnAGRO-FR-58
1012Farmer JaneAGRO-VG-12
1013Farmer AliceAGRO-FR-95

Answer:


In this query, we use the keyword to search for specific text patterns in the column. The '%' symbol is a wildcard that can match any sequence of characters, so 'AGRO-FR-%' and 'AGRO-VG-%' will match any starting with "AGRO-FR-" or "AGRO-VG-", respectively. This query returns all records where the matches these patterns.

Preparing For The Corteva SQL Interview

The key to acing a Corteva SQL interview is to practice, practice, and then practice some more! Besides solving the earlier Corteva SQL interview questions, you should also solve the 200+ SQL exercises on DataLemur which come from companies like Amazon, Microsoft, Meta, and smaller tech companies.

DataLemur SQL and Data Science Interview Questions

Each exercise has multiple hints, step-by-step solutions and crucially, there's an interactive SQL code editor so you can right online code up your SQL query answer and have it graded.

To prep for the Corteva SQL interview you can also be helpful to solve SQL problems from other food and facilities companies like:

However, if your SQL skills are weak, don't worry about jumping right into solving questions – improve your SQL foundations with this DataLemur SQL tutorial.

DataLemur SQL tutorial

This tutorial covers SQL topics like aggreage functions like MIN()/MAX() and grouping by multiple columns – both of these pop up routinely in SQL job interviews at Corteva.

Corteva Data Science Interview Tips

What Do Corteva Data Science Interviews Cover?

Beyond writing SQL queries, the other topics to prepare for the Corteva Data Science Interview include:

  • Probability & Statistics Questions
  • Python Pandas or R Coding Questions
  • Business Sense and Product-Sense Questions
  • ML Modelling Questions
  • Behavioral Interview Questions

Corteva Data Scientist

How To Prepare for Corteva Data Science Interviews?

I believe the best way to prep for Corteva Data Science interviews is to read the book Ace the Data Science Interview.

It solves 201 interview questions taken from FAANG, tech startups, and Wall Street. The book's also got a crash course on Stats, SQL & ML. And finally it's helped a TON of people, which is why it's got over 1000+ 5-star reviews on Amazon.

Nick Singh author of the book Ace the Data Science Interview

While the book is more technical, it's also important to prepare for the Corteva behavioral interview. Start by reading the company's unique cultural values.

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