10 Sanderson Farms SQL Interview Questions (Updated 2025)

Data Science, Data Engineering, and Data Analytics employees at Sanderson Farms rely on SQL to analyze the poultry production data, helping them track everything from feed efficiency to growth rates. They also use SQL to manage inventory, ensuring that their supply chain runs smoothly and efficiently, this is why Sanderson Farms asks jobseekers SQL interview problems.

Thus, to help you study, here’s 10 Sanderson Farms SQL interview questions – how many can you solve?

10 Sanderson Farms SQL Interview Questions

SQL Question 1: Identify the VIP Customers for Sanderson Farms

In Sanderson Farms, a VIP customer is identified as a customer who has bought more than 1000 units of any products in a single transaction more than once in a year. Write a query to understand and analyze the customer database to find these VIP customers.

Here's what the relevant tables might look like for this question.

products:

transactions:

Answer:

SELECT user_id, COUNT(transaction_id) AS num_transactions
FROM transactions
WHERE units_purchased > 1000
GROUP BY user_id
HAVING COUNT(transaction_id) > 1

This query first filters the transactions table for all rows where more than 1000 units were purchased in a transaction. It then groups these rows by user ID, counting the number of transactions for each user. Finally, it filters the groups for those where the number of transactions is greater than 1. The resulting table will list the user IDs of all VIP customers, together with the number of qualifying transactions they have made.

To practice a similar power-user data analysis problem question on DataLemur's free interactive coding environment, try this Microsoft Teams Power User SQL Interview Question:

Discover how Sanderson Farms is leading the way with their groundbreaking artificial intelligence chicken chatbot tool, setting a new standard in the poultry industry! This innovative approach highlights the importance of technology in improving customer engagement and operational efficiency in food production.

SQL Question 2: Top 3 Salaries

Given a table of Sanderson Farms employee salaries, write a SQL query to find the top 3 highest earning employees in each department.

Sanderson Farms employees Example Input:

department Example Input:

Example Output:

Check your SQL query for this interview question 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.

WITH ranked_salary AS (
  SELECT
    name,
    salary,
    department_id,
    DENSE_RANK() OVER (
      PARTITION BY department_id ORDER BY salary DESC) AS ranking
  FROM employee
)


SELECT
  d.department_name,
  rs.name,
  rs.salary
FROM ranked_salary AS rs
INNER JOIN department AS d
   ON rs.department_id = d.department_id
WHERE rs.ranking <= 3
ORDER BY d.department_id, rs.salary DESC, rs.name ASC;

If the solution above is confusing, you can find a detailed solution with hints here: Top 3 Department Salaries.

SQL Question 3: 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 Sanderson Farms's Facebook ads and their Google ads:

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

UNION

SELECT ad_id, ad_name, ad_type, impressions, clicks, spend
FROM sanderson_farms_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 4: Analyzing Chicken Production Per Period and Region

You are provided with production data for Sanderson Farms. The data set includes the quantity of chicken produced (in tons), the production date, and the region where the production occurred. The goal is to write a SQL query using window functions that calculates the average chicken production per month per region.

The production data is formatted as follows:

production Example Input:

We want our answer in this format:

Example Output:

Answer:

SELECT
   EXTRACT(MONTH FROM date) AS month,
   region,
   AVG(quantity) OVER (PARTITION BY EXTRACT(MONTH FROM date), region) AS avg_quantity
FROM
   production
ORDER BY
   month,
   region;

This query uses the AVG window function to calculate the average quantity of chicken produced per month per region. The window function is partitioned by both the month and the region columns from the production table. The EXTRACT function is used to pull out the month portion of the date field. The ORDER BY clause ensures the output is sorted by month and then by region.

To practice a similar window function question on DataLemur's free online SQL coding environment, solve this Google SQL Interview Question:

SQL Question 5: How can you determine which records in one table are not present in another?

To identify records in one table that do not appear in another, you can use a LEFT JOIN and examine NULL values in the right-side table.

Say for example you had exported Sanderson Farms's CRM (Customer Relationship Management) database into PostgreSQL, and had a table of sales leads, and a second table of companies.

Here's an example of how a LEFT JOIN query could find all sales leads that are not associated with a company:

SELECT *
FROM sales_leads
LEFT JOIN companies ON sales_leads.company_id = companies.id
WHERE companies.id IS NULL;

This query brings back all rows from the sales leads table, along with any matching rows from the companies table. If there is no matching row in the companies table, NULL values will be returned for all of the right table's columns.

We then filter out out any rows where the companies.id column is NULL, leaving only the sales leads that are NOT associated with a company.

SQL Question 6: Poultry Production Analysis

Sanderson Farms is one of the largest poultry producers in the United States. As an analyst, you are required to provide insights into its production and sales data. Given the tables production and sales, design a PostgreSQL query to find out the total production and total sales of various poultry products for the year 2021, sorted by product type.

production Example Input:

sales Example Input:

Answer:

SELECT
  p.product_type,
  SUM(p.quantity_produced) AS total_produced,
  SUM(s.quantity_sold) AS total_sold
FROM
  production p
JOIN
  sales s ON p.product_type = s.product_type
WHERE
  EXTRACT(YEAR FROM p.production_date) = 2021
  AND EXTRACT(YEAR FROM s.sales_date) = 2021
GROUP BY
  p.product_type
ORDER BY
  p.product_type;

This query first joins the production and sales tables on the product type. It then extracts records where the production and sales happened in 2021. Using the GROUP BY clause, it calculates the total quantity produced and sold for each product type. The final result is organized by product type. Please note that the dates are assumed to be in 'DD/MM/YYYY' format. The date format in your database may vary and you should adjust the query accordingly.

SQL Question 7: How can you select records without duplicates from a table?

The DISTINCT clause in SQL allows you to select records that are unique, eliminating duplicates.

For example, if you had a table of Sanderson Farms employees, and wanted to see what are all the unique job titles that currently worked at the company, you could write the following query:

SELECT DISTINCT job_title FROM sanderson_farms_employees;

SQL Question 8: Average Daily bird feed consumption

As a Data analyst at Sanderson Farms, one of your major tasks is to monitor the livestock's health and efficiency. You have been given a task to determine the average daily feed consumption for the past week to check if it's within the normal expectation range. Deviations from norms could indicate health issues which need immediate addressing. Create an SQL query to solve this issue.

feed_consumption Example Input:

Answer:

SELECT
    feed_type,
    AVG(feed_consumed) as avg_daily_feed_consumption
FROM
    feed_consumption
WHERE
    day BETWEEN '2022-07-01' AND '2022-07-07'
GROUP BY
    feed_type;

This SQL query will take the feed type and the quantity consumed by each chicken for each day in the past week, and then it will group these by the feed type. It will compute the average of the feed consumed for each feed type over the past week, resulting in the average daily feed consumption sorted by feed type.

To practice a very similar question try this interactive Amazon Server Utilization Time Question which is similar for dealing with calculations over a given time period or this Alibaba Compressed Mean Question which is similar for requiring calculation of an average.

SQL Question 9: Calculating Click-Through Conversion Rate for Sanderson Farms

A crucial part of Sanderson Farms' marketing strategy is their email campaigns. You've been given two tables:

1. The emails_sent table which represents the emails sent to customers containing a link to Sanderson Farms' special chicken products.

2. The product_views table which shows the number of product views following a click-through from the email and whether or not they added a product to their cart.

Your task is to calculate the click-through conversion rate, which is the ratio of the number of times a product was added to the cart to the number of emails sent.

emails_sent Example Input:

product_views Example Input:

Answer:

SELECT 
    e.product_id,
    COUNT(distinct p.view_id) FILTER (WHERE p.add_to_cart = TRUE)::float / COUNT(distinct e.email_id)::float AS click_through_conversion_rate
FROM 
    emails_sent e
LEFT JOIN 
    product_views p
ON 
    e.email_id = p.email_id
GROUP BY e.product_id;

The above query works by first joining the emails_sent table with the product_views table on the email_id column. For each product_id, it then counts the number of times the 'add_to_cart' column is true and divides that by the number of sent emails. The FILTER (WHERE p.add_to_cart = TRUE) clause is used to count only rows where 'add_to_cart' is TRUE. This provides the click-through conversion rate.

To practice another question about calculating rates, solve this TikTok SQL question on DataLemur's online SQL code editor:

SQL Question 10: What are the similarities and differences between a clustered index and non-clustered index?

A clustered index is an index where the order of the rows in the database corresponds to the order of the rows in the index. Because of this, a table can only have one clustered index, but it can have multiple non-clustered indexes.

The main difference between the two is that the database tries to keep the data in the same order as the corresponding keys in the clustered index. This can improve the performance of most query operations, as it provides a linear-access path to the data stored in the database.

Sanderson Farms SQL Interview Tips

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 Sanderson Farms SQL interview questions, you should also solve the 200+ SQL exercises on DataLemur which come from companies like Microsoft, Google, and Meta.

Each interview question has multiple hints, step-by-step solutions and best of all, there is an online SQL coding environment so you can easily right in the browser your SQL query answer and have it graded.

To prep for the Sanderson Farms SQL interview you can also be a great idea to solve interview questions from other food and facilities companies like:

• Tyson Foods SQL questions
• Hormel Foods SQL questions
• Post Holdings SQL questions
• McCormick SQL questions

But if your SQL query skills are weak, don't worry about going right into solving questions – go learn SQL with this DataLemur SQL Tutorial.

This tutorial covers SQL concepts such as functions like SUM()/COUNT()/AVG() and joining a table to itself – both of these come up routinely during Sanderson Farms SQL interviews.

Sanderson Farms Data Science Interview Tips

What Do Sanderson Farms Data Science Interviews Cover?

Besides SQL interview questions, the other types of questions covered in the Sanderson Farms Data Science Interview are:

• Statistics & AB Testing Questions
• Python Pandas or R Coding Questions
• Product-Sense Questions
• Machine Learning Questions
• Behavioral Questions centered on Sanderson Farms culture and values

How To Prepare for Sanderson Farms Data Science Interviews?

To prepare for Sanderson Farms Data Science interviews read the book Ace the Data Science Interview because it's got:

• 201 interview questions sourced from FAANG, tech startups, and Wall Street
• 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 that with this guide on behavioral interview questions.