10 Archer Daniels Midland SQL Interview Questions (Updated 2024)

At Archer Daniels Midland, SQL plays a big role in analyzing agricultural trends across different geographic regions, helping the company understand how various factors affect crop production and pricing. They also use SQL to predict grain market behavior based on past sales data, allowing them to make smart decisions about inventory and sales strategies, which is why Archer Daniels Midland often asks SQL query questions in interviews for Data Analyst, Data Science, and BI jobs.

Thus, to help you prepare for the Archer Daniels Midland SQL interview, here's 10 Archer Daniels Midland SQL interview questions – can you solve them?

10 Archer Daniels Midland SQL Interview Questions

SQL Question 1: Identify the top buying customers for Archer Daniels Midland

Archer Daniels Midland is a global food processing company. Assume you are given two tables, orders and customers. The orders table records all orders placed by customers, and customers table contains information about all Archer Daniels Midland customers. We define a power buyer as a customer who has made large total purchases. For simplicity, create a SQL query to identify the 10 customers who have made the highest total purchases in 2021.

orders Example Input:

customers Example Input:

SQL Query:

SELECT c.customer_name, SUM(o.order_total) as total_purchases
FROM orders o
JOIN customers c ON o.customer_id = c.customer_id
WHERE DATE_PART('year', o.order_date) = 2021
GROUP BY c.customer_name
ORDER BY total_purchases DESC
LIMIT 10;

This SQL query first joins the orders table with the customers, and then filters to include only the rows where the year of the order_date is 2021. It then groups the data by customer_name, and computes the sum of order_total for each customer. Finally, it orders the result in descending order, and limits the output to the top 10 customers with the highest total purchases in 2021.

To work on a similar customer analytics SQL question where you can code right in the browser and have your SQL code instantly executed, try this Walmart Labs SQL Interview Question:

Dive into Archer Daniels Midland's latest stories to see how they are leveraging data science to tackle today's challenges and prepare for the future! Learning about ADM's data-driven strategies can help you appreciate the role of analytics in driving success in the food sector.

SQL Question 2: 2nd Largest Salary

Assume there was a table of Archer Daniels Midland employee salary data. Write a SQL query to find the 2nd highest salary at the company.

Archer Daniels Midland employees Example Input:

Example Output:

Test your SQL query for this interview question and run your code right in the browser:

Answer:

SELECT MAX(salary) AS second_highest_salary
FROM employee
WHERE salary < (
    SELECT MAX(salary)
    FROM employee
);

You can find a detailed solution here: 2nd Highest Salary.

SQL Question 3: What's the difference between WHERE and HAVING clause?

The HAVING clause is used to filter the groups created by the GROUP BY clause. It's similar to the WHERE clause, but it is used to specify conditions on the groups created by the GROUP BY clause, rather than on the individual rows of the table.

For example, say you were analyzing salaries for analytics employees at Archer Daniels Midland:

SELECT department, SUM(salary)
FROM archer_daniels_midland_employees
WHERE department LIKE '%Analytics%'
GROUP BY department
HAVING SUM(salary) > 1000000;

This query retrieves the total salary for each Analytics department at Archer Daniels Midland and groups the rows by the specific department (i.e. "Marketing Analytics", "Business Analytics", "Sales Analytics" teams).

The HAVING clause then filters the groups to include only Archer Daniels Midland departments where the total salary is greater than $1 million

SQL Question 4: Calculate Average Monthly Price of each Commodity

Archer Daniels Midland is one of the largest commodity trading companies in the world. For this exercise, imagine you are working with a dataset that contains information on commodity prices for each transaction that happened. Your task is to write a SQL query to calculate the average monthly price for each commodity using window functions.

Here is the structure of data you are given:

transactions Example Input:

We want to get a result like this:

Example Output:

Answer:

Here is a PostgreSQL query that solves this problem:

SELECT
  TO_CHAR(transaction_date, 'YYYY-MM') AS year_month,
  commodity,
  AVG(price) OVER (PARTITION BY TO_CHAR(transaction_date, 'YYYY-MM'), commodity) AS avg_price
FROM transactions
ORDER BY year_month, commodity;

This query uses TO_CHAR(transaction_date, 'YYYY-MM') to convert transaction_date to 'Year-Month' format. The window function is used to calculate the average price for each commodity for each month. It uses PARTITION BY TO_CHAR(transaction_date, 'YYYY-MM'), commodity to calculate the average separately for each month and each commodity. Then it sorts the result by year_month and commodity.

To solve another window function question on DataLemur's free online SQL coding environment, solve this Amazon BI Engineer interview question:

SQL Question 5: What is denormalization?

Denormalization is the practice of altering a database schema in a way that breaks the normalization rules (1st, 2nd, 3rd normal forms).

Denormalization is done to improve the performance of the database when it is being used for online analytics processing (OLAP), rather than online transaction processing (OLTP) use cases.

For these OLAP use cases, you're bottleneck frequently is joining multiple tables, but de-normalizing your database cuts these outs.

Denormalization can improve query performance by reducing the number of expensive joins required to retrieve data, but it comes with its own drawbacks too. By adding redundant columns, you incur more data storage costs, and there's need for more complex update and delete operations in order to maintain data integrity across all the duplicated data. Thus, it's important to carefully consider the trade-offs involved before implementing denormalization.

SQL Question 6: Average Quantity of Grain Produced

Archer Daniels Midland (ADM) is a global food processing and commodities trading corporation. One of their major products is grain.

Given a table grain_production with fields farm_id, harvest_date, and quantity, where quantity is the quantity of grains produced in kilogram, can you write a SQL query to find the average quantity of grain produced in each quarter of the year 2022?

grain_production Example Input:

Example Output:

Answer:

SELECT 
    CASE
       WHEN DATE_PART('month',harvest_date) BETWEEN 1 AND 3 THEN 'Q1'
       WHEN DATE_PART('month',harvest_date) BETWEEN 4 AND 6 THEN 'Q2'
       WHEN DATE_PART('month',harvest_date) BETWEEN 7 AND 9 THEN 'Q3'
       WHEN DATE_PART('month',harvest_date) BETWEEN 10 AND 12 THEN 'Q4'
    END AS quarter,
    AVG(quantity) as average_quantity
FROM 
    grain_production
WHERE 
    DATE_PART('year', harvest_date) = 2022 
GROUP BY 
    quarter
ORDER BY 
    quarter;

This query groups the data in grain_production by quarter of the year 2022 and calculates the average quantity of grain produced in each quarter. The DATE_PART function is used to extract the month and year from harvest_date, and the CASE statement is used to classify the months into quarters. Finally, the AVG function calculates the average quantity produced for each of the quarters, and the ORDER BY clause sorts the output by quarter.

To practice a very similar question try this interactive Facebook Active User Retention Question which is similar for querying specific date ranges or this Amazon Average Review Ratings Question which is similar for calculating averages grouped by time periods.

SQL Question 7: What does UNION do in a SQL query?

In SQL, the UNION operator combines the result of two or more SELECT statements into a single result set. Note that each SELECT statement within the UNION must have the same number of columns and the columns must have similar data types. The UNION operator removes duplicates from the final result set. If you want to include duplicates in the final result set, you can use the UNION ALL operator instead.

Here's an example of using the UNION operator to combine the results of two SELECT statements that retrieve data from tables of Archer Daniels Midland's Facebook ads and their Google ads:

SELECT ad_id, ad_name, ad_type, impressions, clicks, spend
FROM archer_daniels_midland_facebook_ads
WHERE spend > 500

UNION

SELECT ad_id, ad_name, ad_type, impressions, clicks, spend
FROM archer_daniels_midland_google_ads
WHERE spend > 500;

This SELECT statement would return a combined result set of both Facebook and Google ads that have a spend greater than $500.

SQL Question 8: Calculate the Click-Through-Rate (CTR) for Different Categories of Products

Archer Daniels Midland, a major food processing and commodities trading corporation, has an online platform they use to market their different categories of products. They want to calculate the Click-Through-Rate (CTR) for different categories of products based on impressions (number of times an ad is shown) and clicks (number of time the ad is clicked).

ad_impressions Example Input:

ad_clicks Example Input:

Answer:

SELECT ad_impressions.category_id,
       COUNT(DISTINCT ad_impressions.impression_id) AS impressions,
       COUNT(DISTINCT ad_clicks.click_id) AS clicks,
       (COUNT(DISTINCT ad_clicks.click_id) * 1.0 / COUNT(DISTINCT ad_impressions.impression_id)) AS ctr
FROM ad_impressions
LEFT JOIN ad_clicks
ON ad_impressions.impression_id = ad_clicks.impression_id
GROUP BY ad_impressions.category_id;

This query will return the Click-Through-Rate (CTR) for different categories of products. It first joins the two tables on the impression_id as it is common to both tables. Then it groups by the category_id to calculate the metrics: number of impressions, number of clicks, and Click-Through-Rate (CTR), which is calculated by dividing total clicks by total impressions. A LEFT JOIN is used to ensure all ad impressions are included, even if they didn't receive any clicks.

To practice a similar problem about calculating rates, try this SQL interview question from TikTok on DataLemur's interactive coding environment:

SQL Question 9: Calculate Average Sales

Archer Daniels Midland (ADM) is a company engaged in food processing and commodities trading. Let's say you are working with a database that contains all sales made by the company. The SQL question is to write a query that calculates the total sales and average sales price by product category for the year 2020.

sales Example Input:

Example Output:

Answer:

The query for this would look something like:

SELECT 
    product_category, 
    COUNT(*) AS total_sales, 
    AVG(price) AS avg_sales_price 
FROM 
    sales 
WHERE 
    EXTRACT(YEAR FROM sale_date) = 2020 
GROUP BY 
    product_category;

This SQL query calculates the total number of sales (COUNT(*)) and the average sale price (AVG(price)) for each product category (GROUP BY product_category) in the year 2020.

SQL Question 10: When it comes to database normalization, what's the difference between 1NF, 2NF, and 3NF?

There are several normal forms that define the rules for normalizing a database:

A database is in first normal form (1NF) if it meets the following criteria:

• Each column in a table contains a single value (no lists or containers of data)
• Each column should contain the same type of data (no mixing strings vs. integers)
• Each row in the table is unique

A database is in second normal form (2NF) if it meets the following criteria:

• It is in first normal form.
• All non-key attributes in a table are fully dependent on the primary key.

Said another way, to achieve 2NF, besides following all the rules from 1NF all the columns in a given table should be dependent only on that table's primary key.

A database is in third normal form (3NF) if it meets the following criteria:

• It is in second normal form.
• There are no transitive dependencies in the table.

A transitive dependency means that a piece of data in one column is derived from another column. For example, it wouldn't make sense to keep a column called "user's age" and "user's birthdate" (because age can be derived from birthdate.

While there's also a 4th and 5th normal form, it's too pedantic and hence out-of-scope to know for the Archer Daniels Midland SQL interview.

How To Prepare for the Archer Daniels Midland SQL Interview

Assuming that you've already got basic SQL skills, the next best tip we have to prepare for the Archer Daniels Midland SQL interview is to solve as many practice SQL interview questions as you can! In addition to solving the earlier Archer Daniels Midland SQL interview questions, you should also solve the 200+ FAANG SQL Questions on DataLemur which come from companies like Amazon, Microsoft, Meta, and smaller tech companies.

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 coding environment so you can instantly run your SQL query and have it checked.

To prep for the Archer Daniels Midland SQL interview you can also be a great idea to practice SQL questions from other food and facilities companies like:

• Brown-Forman SQL questions
• Lamb Weston Holdings SQL questions
• Flowers Foods SQL questions
• TreeHouse Foods SQL questions

In case your SQL skills are weak, forget about going right into solving questions – go learn SQL with this SQL tutorial for Data Scientists & Analysts.

This tutorial covers things like AND/OR/NOT and creating summary stats with GROUP BY – both of these show up often in Archer Daniels Midland SQL interviews.

Archer Daniels Midland Data Science Interview Tips

What Do Archer Daniels Midland Data Science Interviews Cover?

In addition to SQL interview questions, the other types of problems to prepare for the Archer Daniels Midland Data Science Interview are:

• Probability & Stats Questions
• Python or R Programming Questions
• Product Data Science Interview Questions
• ML Modelling Questions
• Behavioral Questions focussed on Archer Daniels Midland values & principles

How To Prepare for Archer Daniels Midland Data Science Interviews?

To prepare for Archer Daniels Midland Data Science interviews read the book Ace the Data Science Interview because it's got:

• 201 interview questions taken from FAANG (FB, Apple, Amazon, Netflix, Google)
• a refresher covering 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 Behavioral Interview Guide for Data Scientists.