# 11 Loews SQL Interview Questions (Updated 2024)

Updated on

August 11, 2024

Loews employees write SQL queries to extract and analyze customer data, including demographics and purchase history, for targeted marketing efforts. It is also used to manage room reservations and hotel inventory databases, such as optimizing room allocation and pricing, which is why Loews includes SQL problems during interviews for Data Science and Data Engineering positions.

So, to help you study for the Loews SQL interview, here’s 11 Loews SQL interview questions – scroll down to start solving them!

## 11 Loews SQL Interview Questions

### SQL Question 1: Identify Power Users for Loews

Loews Corporation has a database table named 'transactions' which captures all the transactions made by its users. The structure of the table is as follows:

##### Example Input:
transaction_iduser_idtransaction_dateamount
8234267501/08/2022682.5
5126924501/12/202249.2
9761267502/08/2022920.35
8312638502/15/2022175.6
3719267502/26/2022721.2

The management of Loews wants to identify its power users. Loews defines a power user as a user who has made transactions worth more than \$1500 in a month, for at least 3 months in the year.

Write a SQL query to identify the user_ids of these power users.

This query first aggregates transactions on a per month basis for each user and then identifies users who have more than \$1500 in transactions for any month. It then takes this intermediate result and counts how many such 'power months' each user has and finally returns the user_ids of those users who have at least 3 'power months'.

To practice another SQL customer analytics question where you can solve it interactively and have your SQL code instantly executed, try this Walmart Labs SQL Interview Question:

### SQL Question 2: Department Salaries

You're given a table of Loews employee and department salaries. Write a SQL query to find departments where the departments average salary is higher than the company's average salary.

Code your solution to this interview question 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: Could you describe a self-join and provide a scenario in which it would be used?

A self-join is a type of JOIN where a table is joined to itself. To execute a self-join, you must include the table name twice in the FROM clause and assign a different alias to each instance. You can then join the two copies of the table using a JOIN clause, and use a WHERE clause to specify the relationship between the rows.

For instance, suppose you had website visitor data for Loews, exported from the company's Google Analytics account. To assist the web-dev team, you needed to analyze pairs of pages for UX or navigational issues. As part of that analysis, you wanted to generate all pairs of URLs, but needed to exclude pairs where both URLs were the same since that is not a valid pair."

You could use the following self-join:

This query returns the url of each page () along with the url of the page that referred to it (). The self-join is performed using the field, which specifies the id of the page that referred the visitor to the current page, and avoids any pages that referred themself (aka data anomalies).

### SQL Question 4: Calculate Monthly Revenue Per Category

We are given a sales dataset, where each row represents a transaction for a specific product sold by Loews. Each product belongs to a category. The dataset includes the product price, quantity sold, sale date and category for each product sold.

Write a SQL query to calculate the total revenue (price * quantity) per category grouped by month for the year 2022. Furthermore, write the query in such way that it also computes the percentage change in total revenue from the previous month for each category.

##### Sample Input:
sale_idproduct_idcategorypricequantitysale_date
1011001Electronics\$500.00201/05/2022
1021002Furniture\$200.00101/15/2022
1031003Electronics\$300.00302/10/2022
1041004Furniture\$400.00102/25/2022
1051001Electronics\$500.00503/20/2022
##### Example Output:
monthcategorytotal_revenuepercentage_change
1Electronics\$1000.00NULL
1Furniture\$200.00NULL
2Electronics\$900.00-10%
2Furniture\$400.00100%
3Electronics\$2500.00178%
3Furniture\$0.00-100%

This query first calculates the total revenue per category per month. Then, it uses the clause with the function to compute the total revenue of the previous month for each category. Finally, it calculates the percentage change in total revenue from the previous month. It handles the first month per category by returning NULL for the , since there's no previous month to compare to.

For more window function practice, solve this Uber SQL problem within DataLemur's online SQL coding environment:

### SQL Question 5: Can you describe a cross-join and its purpose?

A cross-join, also known as a cartesian join, is a type of join that returns the cross-product of two tables. In a cross-join, each row from the first table is paired with every row from the second table, resulting in a new table that has a row for each possible combination of rows from the two input tables.

For example, say you worked on the Marketing Analytics team at Loews, 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:

You could this query to generate all possible combinations of ad copy and ad creative to help you create more effective ads for Loews. Just be careful: if you had 1,000 rows of ad copy and 1,000 different image creatives, the resulting cross-join would have 1 million rows!

### SQL Question 6: Room Reservation Analysis

Loews Hotels is interested in gaining insights into how their room reservations are being made. Specifically, they want to know:

1. Which type of room is most popular?
2. What is the average duration of stays for each room type?
3. How does duration of stay varies with respect to different months?

For this purpose we keep two tables, and . The table stores information about all the different types of rooms available at Loews Hotels, and contains logs of customer reservations.

room_idroom_type
1Standard Room
2Deluxe Suite
3Luxury Suite
##### Example Input:
reservation_idroom_idcustomer_idcheck_in_datecheck_out_date
101130101/01/202301/03/2023
102230201/02/202301/06/2023
103330301/03/202301/06/2023
104130401/04/202301/07/2023
105230501/05/202301/06/2023

This query joins the and tables on , and then groups the data by and month of . It counts the total number of reservations per room type and calculates the average stay duration. The results are ordered by number of reservations in descending order, and then by month.

The final output table will provide information needed for Loews Hotel's requested analysis.

### SQL Question 7: What are database views, and when would you use them?

Database views are created to provide customized, read-only versions of your data that you can query just like a regular table. So why even use one if they're so similar to a regular table?

Views are advantageous for several reasons:

• views allow you to create a simpler versions of your data for specific users (such as hiding extraneous columns/rows from business analysts since they're relics of the Data Engineering pipelines setup)
• views help you comply with data security requirements by hiding sensitive data from certain users (important for regulated industries like govermnet and healthcare!)
• views can improve performance for complicated queries by pre-computing the results and caching them in a view (which is often faster than re-executing the original query)

### SQL Question 8: Find the Average Movie Ratings

As an analyst at Loews, a company renowned for its movie theaters, can you write an SQL query to determine the average movie rating for each movie shown in their theaters based on user submissions? For simplification, assume that each movie has at least one rating and ratings are on a scale of 1-5.

movie_idtitle
1Inception
2The Dark Knight
3Interstellar

#### Table Example Input:

rating_iduser_idmovie_idstars
101111115
102111224
103111314
104111415
105111533

#### Example Output:

titleaverage_rating
Inception4.67
The Dark Knight4.00
Interstellar3.00

Your PostgreSQL query would look something like this:

The query is joining the table with based on the common column. For each distinct movie title, it calculates the average star rating from all the ratings the movie received. The function in SQL calculates the mean of the given set, which in this case, is the ratings for each movie. The result displays each movie title and its corresponding average star rating.

To practice a very similar question try this interactive Amazon Average Review Ratings Question which is similar for calculating average ratings or this Facebook App Click-through Rate (CTR) Question which is similar for dealing with user ratings data.

### SQL Question 9: Analysis of Click-Through-Conversions

Given the tables and you need to calculate the click-through-conversion rate for each hotel each day. The click-through-conversion is defined by the number of users who added a room to their cart after viewing the hotel page, divided by the number of users who viewed the hotel page.

A user is identified as having added a room to their cart after viewing the hotel page if there is an entry in prior to the for the same user and the same date.

##### Example Input:
view_iduser_idview_datehotel_id
417112306/08/2022 00:00:0055001
780226506/10/2022 00:00:0065002
123136206/10/2022 00:00:0055001
835212306/09/2022 00:00:0055001
451798107/05/2022 00:00:0065002
##### Example Input:
action_iduser_idaction_datehotel_id
217112306/09/2022 12:00:0055001
730226506/11/2022 11:00:0065002
228136206/12/2022 01:00:0055001
908219207/26/2022 09:00:0065002
761798107/05/2022 08:00:0065002

This query first groups both page views and add_to_cart actions by date and hotel, counting the number of distinct users for each grouping. Then it joins these results for each hotel on each day, and finds the rate of users who viewed the hotel and added a room to their cart after viewing the hotel page (click-through-conversion rate) by dividing the count of distinct users who add to cart by the count of users who viewed the page.

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's the operator do, and can you give an example?

is used to combine the output of multiple statements into one big result!

For a concrete example, say you were a Data Analyst supporting the Sales Analytics team at Loews, and data on potential sales leads lived in both Salesforce CRM and Hubspot. To write a query to analyze leads created after 2023 started, across both CRMs, you could use in the following way:

### SQL Question 11: Calculate the Total Revenue of Each Movie

Loews is a well-known company in the entertainment industry that sells movie tickets. Suppose you are given a table named , where each row represents a ticket sold for a movie. The table contains the following columns:

• (integer): A unique identifier for the ticket.
• (integer): The identifier of the customer who purchased the ticket.
• (integer): The identifier of the movie for which the ticket was purchased.
• (date): The date the ticket was purchased.
• (float): The price of the ticket.

Write a SQL query that calculates the total revenue generated by each movie based on the table.

##### Example Input:
ticket_idcustomer_idpurchase_datemovie_idticket_price
10120106/08/202230112.99
10220506/09/202230214.99
10320806/18/202230112.99
10421407/26/202230214.99
10521507/05/202230315.99
##### Example Output:
movie_idtotal_revenue
30125.98
30229.98
30315.99

This query groups rows by (each unique movie) and calculates the total revenue made from each movie by summing up the ticket prices. The function adds up all values for each group. The statement categorizes the rows by before the aggregate function is applied.

### Preparing For The Loews 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. Beyond just solving the above Loews SQL interview questions, you should also solve the 200+ FAANG SQL Questions on DataLemur which come from companies like Meta, Google and insurance companies like Loews.

Each problem on DataLemur has multiple hints, full answers and most importantly, there is an online SQL coding environment so you can right online code up your SQL query answer and have it checked.

To prep for the Loews SQL interview it is also helpful to practice SQL questions from other insurance companies like:

Explore the latest news and press releases from Loews and discover what's driving their success!

However, if your SQL query skills are weak, forget about jumping right into solving questions – strengthen your SQL foundations with this DataLemur SQL tutorial.

This tutorial covers SQL concepts such as aggregate functions like SUM()/COUNT()/AVG() and aggregate window functions – both of these show up routinely in Loews SQL interviews.

### Loews Data Science Interview Tips

#### What Do Loews Data Science Interviews Cover?

Beyond writing SQL queries, the other question categories to practice for the Loews Data Science Interview include:

#### How To Prepare for Loews Data Science Interviews?

I think the best way to study for Loews Data Science interviews is to read the book I wrote: Ace the Data Science Interview.

It solves 201 data interview questions taken from FAANG tech companies. The book's also got a refresher covering SQL, Product-Sense & ML. And finally it's vouched for by the data community, which is why it's earned 1000+ reviews on Amazon and a 4.5-star rating.

While the book is more technical in nature, it's also important to prepare for the Loews behavioral interview. A good place to start is by reading the company's cultural values.