9 CSG Systems SQL Interview Questions (Updated 2024)

At CSG International, SQL is used across the company for analyzing customer behavior patterns and optimizing telecom billing data management. So, it shouldn't surprise you that CSG Systems often tests SQL questions in interviews for Data Science and Data Engineering positions.

Thus, to help you prepare, we've curated 9 CSG International SQL interview questions – able to answer them all?

9 CSG International SQL Interview Questions

SQL Question 1: Analyze Monthly Average Rating of CSG Systems' Products

Design a SQL query to analyze one aspect of CSG Systems' business, specifically, the monthly average rating of each product based on user reviews. Use SQL window functions to calculate these monthly averages.

Suppose we have a reviews table formatted as follows:

reviews Example Input:

And the output of our query should look like this:

Example Output:

Answer:

Here's the PostgreSQL query that will generate this output:

WITH monthly_reviews AS (
 SELECT 
   EXTRACT(MONTH FROM submit_date) AS month,
   product_id, 
   AVG(stars) OVER(PARTITION BY EXTRACT(MONTH FROM submit_date), product_id) as average_rating
 FROM reviews
)

SELECT DISTINCT month, product_id, average_rating
FROM monthly_reviews
ORDER BY month, product_id
;

This query first creates a Common Table Expression (CTE) where it calculates the average ratings by using the window function AVG() OVER(PARTITION BY ...) for each product within each month. Then it selects distinct rows from this result set and displays the monthly average product ratings.

Pro Tip: Window functions are a frequent SQL interview topic, so practice every window function problem on DataLemur

SQL Question 2: 2nd Highest Salary

Given a table of CSG Systems employee salary information, write a SQL query to find the 2nd highest salary amongst all the employees.

CSG Systems employees Example Input:

Example Output:

Write a SQL query for this problem 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 with hints here: 2nd Highest Salary.

SQL Question 3: How do RANK() and DENSE_RANK() differ when it comes to ranking rows in a result set?

While both RANK() and DENSE_RANK() are used to rank rows, the key difference is in how they deal with ties.

RANK(): When there's a tie, RANK() leaves a gap in the ranking. For example, if three rows are tied for 2nd place, the RANK() function will assign a rank of 2 to the first of these rows, a rank of 3 to the 2nd row in the tie, and a rank of 4 to the the 3rd tie.

DENSE_RANK(): For ties, DENSE_RANK() does not leave a gap in the ranking. Instead, it assigns the same rank to all tied rows, and then makes the next row 1 bigger. Confusing, I know, but here's an example to make it more clear: if three rows are tied for 3rd place, the DENSE_RANK() function will assign a rank of 3 to all three rows, and then assign a rank of 4 to the next row.

Suppose we had data on how many deals different salespeople at CSG Systems:

WITH data AS (
  SELECT 'Akash' AS name, 50 AS deals_closed UNION ALL
  SELECT 'Brittany', 50 UNION ALL
  SELECT 'Carlos', 40 UNION ALL
  SELECT 'Dave', 30 UNION ALL
  SELECT 'Eve', 30 UNION ALL
  SELECT 'Farhad', 10
)

To rank these salespeople, we could execute the following query:

SELECT name, deals_closed,
  RANK() OVER (ORDER BY deals_closed DESC) as rank,
  DENSE_RANK() OVER (ORDER BY deals_closed DESC) as dense_rank
FROM csg_systems_sales;

The result of this query would be:

As you can see, the RANK() function assigns a rank of 1 to the first row (Akash), a rank of 2 to the second row (Brittany), and so on. In contrast, the DENSE_RANK(), gives both Akash and Brittany a rank of 1, since they are tied, then moves on to rank 2 for Carlos.

SQL Question 4: Calculate Average Monthly Revenue

As a data analyst at CSG Systems, a leading provider of business support solutions, you are tasked to calculate the average monthly revenue for each service offering. CSG Systems provides various services each having multiple subscription types. Use the data in the subscriptions and services tables.

subscriptions Example Input:

services Example Input:

Answer:

SELECT 
    EXTRACT(MONTH FROM s.subscription_date) AS month,
    sr.service_name, 
    AVG(s.subscription_fee) AS avg_monthly_revenue
FROM 
    subscriptions s
INNER JOIN 
    services sr ON s.service_id = sr.service_id
GROUP BY 
    month, sr.service_name;

This query first extracts the month from the subscription_date in the subscriptions table and joins it with the services table on service_id. It then groups the data by month and service name and calculates the average subscription fee for each group, representing the average monthly revenue for each service.

To practice a very similar question try this interactive Amazon Highest-Grossing Items Question which is similar for using SQL to analyze revenue data or this Amazon Average Review Ratings Question which is similar for calculating monthly averages.

SQL Question 5: Can you explain what an index is and the various types of indexes?

A database index is a way to optimize the performance of a database by reducing the amount of data that needs to be searched to retrieve a record.

There are several types of indexes:

• unique & non-inuqie indexes
• primary & composite indexes
• clustered & non-clustered indexes

SQL Question 6: Analyzing Click-Through-Rates for Marketing campaigns

CSG Systems, a software company, conducted multiple online marketing campaigns to promote its products. The data of these campaigns are stored in two key tables. The campaigns table collects metadata about each campaign. The clicks table logs each time a user clicks on the product's link during a campaign. Through this data, we want to determine the click-through-rate (CTR) of these campaigns.

campaigns Example Input:

|campaign_id|product_id|campaign_date|| |:----|:----|:----| |2021|105|07/02/2022 00:00:00| |2051|209|07/10/2022 00:00:00| |2552|305|07/18/2022 00:00:00| |2771|102|06/26/2022 00:00:00| |2881|109|06/05/2022 00:00:00|

clicks Example Input:

Each record in the "clicks" table corresponds to a single click.

Find the click-through rate (CTR) for each campaign.

CTR is calculated as (Total clicks / Total campaigns) * 100%.

Answer:

SELECT c.campaign_id, 
       p.product_id,
       COUNT(cl.click_id) AS total_clicks,
       ROUND((COUNT(cl.click_id)*1.0 / COUNT(DISTINCT c.campaign_id)) * 100, 2) AS ctr
FROM campaigns c 
JOIN clicks cl 
ON c.campaign_id = cl.campaign_id
GROUP BY c.campaign_id, p.product_id
ORDER BY ctr DESC;

This SQL query joins the "campaigns" table with the "clicks" table based on campaign_id to find the number of clicks for each campaign. The CTR is then calculated as the total number of clicks divided by the total number of campaigns, all multiplied by 100 to get it as a percentage. The result provides the click-through rate for each campaign, allowing us to understand which campaigns are more effective in terms of engaging users to the product's link.

To practice another question about calculating rates, solve this SQL interview question from TikTok within DataLemur's interactive coding environment:

SQL Question 7: What's a database view?

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 Clients Matching Specific Pattern

As a data analyst at CSG Systems, you are often required to filter through the client database to find valuable information. For this task, you are required to write an SQL query to find all clients whose names start with the letter 'A' from the clients table.

clients Example Input:

Your output should return a list of client_name and location of clients whose names start with 'A'.

Example Output:

Answer:

SELECT client_name, location
FROM clients
WHERE client_name LIKE 'A%';

This query checks the client_name field for any entries starting with the letter 'A'. It does this using the LIKE keyword and a wildcard pattern 'A%'. The '%' in the pattern is a wildcard character that matches any sequence of character, so 'A%' matches any string that starts with 'A'. If a client_name matches this pattern, it is added to the result set for the query.

SQL Question 9: Calculate Average Data Usage

Problem Statement

CSG Systems runs an internet service and would like to do an analysis on the average monthly data usage per user. The analysis would involve using some advanced math functions in SQL. We need to calculate the monthly average data usage of each user, rounded to the nearest whole number, and also calculate the absolute difference between the average and maximal monthly data usage per user.

data_usage Example Input:

Example Output:

Answer:

SELECT 
    user_id, 
    month,
    year, 
    ROUND(AVG(data_used_gb)) AS avg_data_usage_gb,
    ABS(MAX(data_used_gb) - ROUND(AVG(data_used_gb))) AS data_difference_gb
FROM 
    data_usage
GROUP BY 
    user_id, 
    month, 
    year;

The above PostgreSQL query uses ROUND(), AVG(), MAX() and ABS() functions to solve this question. It first groups the data by user_id, month and year. For each group, it calculates the average and maximum data usage. Then it calculates the absolute difference between the average and maximum data usage.

To practice a very similar question try this interactive Twitter Tweets' Rolling Averages Question which is similar for averaging user actions over time or this Amazon Average Review Ratings Question which is similar for calculating average and round it.

How To Prepare for the CSG Systems SQL Interview

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

Each interview question has multiple hints, step-by-step solutions and most importantly, there's an interactive coding environment so you can instantly run your SQL query answer and have it checked.

To prep for the CSG Systems SQL interview you can also be wise to practice SQL questions from other tech companies like:

• Amdocs SQL questions

• Netcracker SQL questions

• Optiva SQL questions

• MIND CTI SQL questions

However, if your SQL coding skills are weak, don't worry about diving straight into solving questions – improve your SQL foundations with this interactive SQL tutorial.

This tutorial covers SQL concepts such as HAVING and sorting data with ORDER BY – both of which show up routinely during SQL job interviews at CSG Systems.

CSG International Data Science Interview Tips

What Do CSG Systems Data Science Interviews Cover?

Beyond writing SQL queries, the other types of questions to prepare for the CSG Systems Data Science Interview are:

• Probability & Statistics Questions
• Python or R Coding Questions
• Open-Ended Data Case Studies
• ML Interview Questions
• Behavioral & Resume-Based Questions

How To Prepare for CSG Systems Data Science Interviews?

I'm sort of biased, but I believe the optimal way to prep for CSG Systems Data Science interviews is to read my book Ace the Data Science Interview.

The book covers 201 data interview questions sourced from tech companies like Netflix, Google, & Airbnb. It also has a refresher covering SQL, Product-Sense & ML. And finally it's helped thousands of people land their dream job in data, which is why it's earned 1000+ reviews on Amazon and a 4.5-star rating.