10 Insight Enterprises SQL Interview Questions (Updated 2024)

Data Analysts and Data Engineers at Insight Enterprises use SQL to analyze sales data, including product category performance, customer purchase history, and sales channel effectiveness, to find ways to optimize business strategies and boost revenue growth. They also maintain and manage client databases with data on customer preferences, behavior, and demographics to support personalized marketing campaigns, resulting Insight Enterprises to evaluate job seekers with SQL coding interview questions.

So, to help you practice, here’s 10 Insight Enterprises SQL interview questions – how many can you solve?

10 Insight Enterprises SQL Interview Questions

SQL Question 1: Identify Insight Enterprises' power users based on high-frequency purchase

Insight Enterprises is an industry-leading provider of computer hardware, software, cloud solutions and IT services. It is crucial to identify the users who contribute significantly to the business by frequently purchasing products. This type of users, often referred to as "power users" are key to sustaining the company's profitability.

Assuming we have access to a transactional database that keeps a log of all user's purchase activities. This database contains a table named purchases with the following columns:

• purchase_id: a unique identifier for each purchase
• user_id: the id of the user who made the purchase
• product_id: the id of the product purchased
• purchase_date: the date on which the purchase was made
• amount: the total dollar amount of the purchase

Create a SQL query that returns the users who made purchases more than a certain threshold e.g., 100 times in the last year.

purchases Example Input:

Answer:

SELECT 
    user_id,
    COUNT(purchase_id) AS purchase_count
FROM
    purchases 
WHERE 
    purchase_date BETWEEN '01-01-2021' AND '12-31-2021'
GROUP BY 
    user_id 
HAVING 
    COUNT(purchase_id) > 100

This query groups all purchases by user id within the specified date range. The COUNT function is used to calculate the total number of purchases each user made during this period. The HAVING clause then filters out all users who made more than 100 purchases, identifying them as power users.

To practice a related customer analytics question on DataLemur's free interactive SQL code editor, try this Microsoft Teams Power User SQL Interview Question:

SQL Question 2: Department vs. Company Salary

Suppose there was a table of Insight Enterprises employee salary data, along with which department they were in. Write a query to compare the average salary of employees in each department to the company's average salary. Return the comparison result as 'higher', 'lower', or 'same' for each department. Display the department ID, payment month (in MM-YYYY format), and the comparison.

Write a SQL query for this problem directly within the browser on DataLemur:

The answer is LONG – 30+ lines of SQL. You can find a detailed solution here: Department Salaries.

SQL Question 3: What's a database view?

A database view is a virtual table that is created based on the results of a SELECT statement, and provides you a customized, read-only version of your data that you can query just like a regular table.

You'd want to use a view for a few reasons:

• views allow you to create a simpler versions of your data based on whose running the query (such as hiding un-important columns/rows from business analysts if they're just random noisy artifricats of your Data Infrastructure pipelines)
• views can help you comply with information security requirements by hiding sensitive data from certain users (important for regulated industries like govermnet and healthcare!)
• views often improve performance for complicated queries by pre-computing the results and saving them in a view (which is often faster than re-executing the original query)... just be careful since static views don't update if the underlying data changes!

SQL Question 4: Calculate Monthly Average Rating for Each Product

Insight Enterprises, being a B2B IT solutions provider, wants to track their product performance in terms of customer reviews. As a Data Analyst, you are asked to determine the average product ratings each month using SQL.

In this hypothetical scenario, you have a reviews table that contains reviews submitted by the users about the products. The table has following structure:

reviews Table:

review_id is the unique identifier of each review, user_id represents the user who submitted the review, submit_date indicates the date when the review was submitted, product_id represents the product, and stars show the number of stars granted by the user to the product.

The task is to write a SQL query that returns a new table with columns mth, product_id, and avg_stars, where mth is the month number when reviews were submitted, product_id is the product identifier, and avg_stars is the average of the star ratings of a product in that month.

Answer:

You can use the PostgreSQL DATE_PART function to extract the month from submit_date and then use the AVG and GROUP BY SQL commands to calculate the average stars for each product in each month.

SELECT DATE_PART('month', submit_date) AS mth,
       product_id,
       AVG(stars) AS avg_stars
FROM reviews
GROUP BY DATE_PART('month', submit_date), product_id;

This query groups the data by month and product ID and then computes the average stars for each group, thus giving us the average star rating for each product in each month.

To solve a related window function SQL problem on DataLemur's free interactive SQL code editor, try this Google SQL Interview Question:

SQL Question 5: What is the difference between SQL operators ‘BETWEEN’ and ‘IN’?

While both the BETWEEN and IN operators are used to filter data based on some criteria, BETWEEN selects for values within a given range, whereas for IN it checks if the value is in a given list of values.

For example, say you had a table called insight_enterprises_employees, which had the salary of the employee, along with the country in which they reside.

To find all employees who made between $80k and$120k, you could use the BETWEEN operator:

SELECT * 
FROM insight_enterprises_employees 
WHERE salary BETWEEN 80000 AND 120000;

To find all employees that reside in the US or Canada, you could use the IN operator:

SELECT * 
FROM insight_enterprises_employees 
WHERE country IN ("USA", "Canada");

SQL Question 6: Average Sales Revenue by Product Category

Insight Enterprises is a Fortune 500 company that provides B2B IT services including hardware, software and service solutions to business, government, education and healthcare clients. Assume you work as a data analyst in this company and have been tasked with understanding the average sales revenue by product category for each month. This will help the sales team to identify trending product categories and focus their efforts accordingly.

Considering the company’s focus, the most relevant tables could be "sales" and "products". Here's how the tables might look:

sales Example Input:

products Example Input:

Answer:

Here is a possible PostgreSQL query to answer this task:

SELECT 
    EXTRACT(MONTH FROM s.sale_date) AS month, 
    p.product_category, 
    AVG(s.quantity * s.sale_price) AS avg_revenue

FROM 
    sales s

JOIN 
    products p 
ON 
    s.product_id = p.product_id 

GROUP BY 
    month, 
    p.product_category 
ORDER BY 
    month, 
    avg_revenue DESC;

This query first joins the sales and products tables on product_id. Then it computes the average revenue per month for each product category by multiplying quantity and sale_price. The results will contain columns for the month, the product category, and the average revenue, with rows sorted by month and then average revenue in descending order. This will quickly tell us the performance of product categories month over month.

SQL Question 7: Do UNION ALL and a FULL OUTER JOIN typically produce equivalent results?

For all practical purposes, UNION ALL and FULL OUTER JOIN do NOT produce the same results.

While both are similar in that they combine two tables, you can think of joins as increasing the width of the resulting table (you'll have more columns in the result set for a left/inner/right join), whereas a union is used to combine rows which increases the height of the result set but keeps the column count the same.

If your familiar with set operations, you can think of a UNION as set addition, whereas a JOIN is set multiplication.

SQL Question 8: Filter Customers Based on Purchase History and Location

Insight Enterprises is interested in identifying customers who have made more than two purchases over $1000 in the technology category in the past year and are located in California. Write a SQL query to return a list of these customers.

Assume the company has the following two tables - purchases and customers:

purchases Example Input:

customers Example Input:

Answer:

SELECT 
    c.customer_name,
    COUNT(p.purchase_id) AS number_of_purchases,
    SUM(p.purchase_amount) AS total_purchase_value
FROM 
    purchases p
INNER JOIN 
    customers c ON p.customer_id = c.customer_id
WHERE 
    p.product_category = 'Technology' AND 
    p.purchase_amount > 1000 AND 
    c.location = 'California' AND 
    p.purchase_date > CURRENT_DATE - INTERVAL '1 year'
GROUP BY 
    c.customer_name
HAVING 
    COUNT(p.purchase_id) > 2;

This SQL query filters customers based on their purchase history (more than two purchases over $1000 in the technology category in the past year) and location (California) from the purchases and customers tables. It then counts the number of such purchases and calculates the total purchase value for each of these customers.

SQL Question 9: Compute the Click-through Conversion Rate

At Insight Enterprises, a leading global technology Solutions Company, there is a focus on efficient digital marketing. One of the key metrics used to evaluate the effectiveness of this is the Click-Through Conversion Rate. This rate is a measure of how many of the customers who view a product actually go ahead to add it to their cart.

Let's consider a scenario where we have two tables views and carts. The views table logs every instance a user views an item and the carts table logs when a user adds an item to their cart.

views Example Input:

carts Example Input:

Your task is to write a query that computes the Click-through Conversion Rate per product. The Click-through Conversion Rate is calculated as the number of times a product is added to the cart divided by the number of times it's been viewed.

Answer:

SELECT v.product_id,
       CAST(count(DISTINCT c.user_id) AS DECIMAL) / CAST(count(DISTINCT v.user_id) AS DECIMAL) AS click_through_conversion_rate
FROM   views v
       LEFT JOIN carts c
              ON v.product_id = c.product_id
                 AND v.user_id = c.user_id
GROUP  BY v.product_id;

This SQL query first combines the 'views' and 'carts' tables on the 'product_id' and 'user_id' fields. It then calculates the Click-through Conversion Rate in the SELECT statement. The CAST function is used to ensure proper division (as counting functions return integers by default in SQL). Finally, because we want to compute by product, it uses GROUP BY to group all view and cart records by product_id.

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

SQL Question 10: What does EXCEPT / MINUS SQL commands do?

For a tangible example of EXCEPT in PostgreSQL, suppose you were doing an HR Analytics project for Insight Enterprises, and had access to Insight Enterprises's contractors and employees data. Assume that some employees were previously contractors, and vice versa, and thus would show up in both tables.

You could use EXCEPT operator to find all contractors who never were a employee using this query:

SELECT first_name, last_name
FROM insight_enterprises_contractors

EXCEPT

SELECT first_name, last_name
FROM insight_enterprises_employees

Note that EXCEPT is available in PostgreSQL and SQL Server, while MINUS is the equivalent operator which is available in MySQL and Oracle (but don't worry about knowing which RDBMS supports which exact commands since Insight Enterprises interviewers aren't trying to trip you up on memorizing SQL syntax).

How To Prepare for the Insight Enterprises SQL Interview

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. In addition to solving the earlier Insight Enterprises SQL interview questions, you should also solve the 200+ SQL questions on DataLemur which come from companies like Amazon, JP Morgan, and consulting and professional service companies like Insight Enterprises.

Each DataLemur SQL question has multiple hints, fully explained answers along with a discussion board to see how others solved it and best of all, there is an online SQL coding environment so you can easily right in the browser your query and have it graded.

To prep for the Insight Enterprises SQL interview you can also be useful to practice SQL problems from other consulting and professional service companies like:

• FTI Consulting SQL questions
• Accenture SQL questions
• Maximus SQL questions
• Concentrix SQL questions

Dive into the world of data and AI with Insight and discover how they're helping businesses make smarter decisions!

In case your SQL skills are weak, don't worry about going right into solving questions – go learn SQL with this interactive SQL tutorial.

This tutorial covers SQL concepts such as how window functions work and aggregate functions like SUM()/COUNT()/AVG() – both of which pop up often in Insight Enterprises SQL assessments.

Insight Enterprises Data Science Interview Tips

What Do Insight Enterprises Data Science Interviews Cover?

In addition to SQL query questions, the other topics tested in the Insight Enterprises Data Science Interview include:

• Statistics and Probability Questions
• Python Pandas or R Coding Questions
• Product-Sense Questions
• ML Modelling Questions
• Behavioral Interview Questions centered on Insight Enterprises company values

How To Prepare for Insight Enterprises Data Science Interviews?

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

• 201 interview questions taken from Google, Microsoft & tech startups
• a crash course on Python, SQL & ML
• over 1000+ reviews on Amazon & 4.5-star rating

Don't ignore the behavioral interview – prepare for it with this Behavioral Interview Guide for Data Scientists.