10 N-Able SQL Interview Questions (Updated 2024)

Updated on

March 3, 2024

At N-Able, SQL is often used for analyzing customer usage patterns for predictive maintenance, and managing databases for efficient cybersecurity threat detection. Unsurprisingly this is why N-Able frequently asks SQL problems during interviews for Data Analytics, Data Science, and Data Engineering jobs.

So, to help you study for the N-Able SQL interview, here’s 10 N-Able SQL interview questions – can you solve them?

10 N-Able SQL Interview Questions

SQL Question 1: Identify the Most Active Purchasing Power Users for N-Able Company

Suppose N-Able is an e-commerce company and you have a table that logs all the purchasing activities of each customer per item. A customer is considered to be a "power user" if they consistently purchase more than 10 items per month across all product categories.

Generate an SQL query that identifies these types of users over the past year. The key fields to consider would be , , , and .

Sort the result set by the total quantity purchased in descending order.

Your main objective in this query would be - From the purchases table, you need to find out the 'user_id's which have purchased more than 10 items per month for the last 12 months.

Example Input:


Unnamed users would be represented by their user IDs. The following PostgreSQL query would achieve the desired result:

In this query, we first filter our data to only include purchases made within the last year. Next, we group the records based on the user ID and sum up the quantity of items purchased by each user in this time period. We then only keep records where the sum of the quantity is more than 120, which is the equivalent of an average of at least 10 purchases per month over a 12-month period. The remaining records are then ordered by in descending order.

This query will list the user IDs of power users at N-Able who have made more than 10 purchases per month for 1 year. It also shows the total quantity of items that they have purchased.

To solve a related super-user data analysis question on DataLemur's free interactive SQL code editor, try this Microsoft Teams Power User SQL Interview Question: Microsoft SQL Interview Question: Teams Super User

SQL Question 2: Average Monthly Software Sales

As an analyst at N-able, you are given a task to analyze the sales data. You are specifically required to calculate the average monthly sales for each software product. In order to simplify the calculations, consider only the quantity of software sold for the estimation of average monthly sales. The company has a table that records all sales transactions.

Example Input:

In order to make the analysis thorough, include the table that lists all software products.


Your task is to write a SQL query that calculates the average monthly sales for each software product.

Example Output:


In the abovementioned PostgreSQL script, the function is used to get the month from the date. After that, the function is used along with to calculate the average monthly sales for each software product. Finally, the results are ordered by month and product name for ease of reading.

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

Uber SQL problem

SQL Question 3: What does the clause do vs. the clause?

The clause serves as a filter for the groups created by the clause, similar to how the clause filters rows. However, is applied to groups rather than individual rows.

For example, say you were a data analyst at N-Able trying to understand how sales differed by region:

This query retrieves the total sales for all products in each region, and uses the clause to only sales made after January 1, 2023. The rows are then grouped by region and the clause filters the groups to include only those with total sales greater than $400,000.

N-Able SQL Interview Questions

SQL Question 4: Customer Subscription Status Filter

You are provided with a database of customers for N-Able. The database records information such as the customer's ID, their country of residence, the date they joined, and their subscription status (active or inactive). Your task is to write a SQL query that filters down the customers who are active, live in the US, and have been users for at least a year.

Example Input:
Example Output:


This SQL query filters the customers on the conditions specified. The clause is used to filter the records and combined with to apply three conditions: the customer is active, the customer is from the US, and the customer has been a user for at least 1 year (from the current date). If the join_date is less or equal 1 year in the past, it means that this customer joined at least 1 year ago.

SQL Question 5: In SQL, are blank spaces the same as a NULL?

NULLs are NOT the same as zero or blank spaces in SQL. NULLs are used to represent a missing value or the abscence of a value, whereas zero and blank space are legitimate values.

It's important to handle NULLs carefully, because they can mess up your analysis very easily. For example, if you compare a NULL value using the = operator, the result will always be NULL (because just like Drake, nothing be dared compared to NULL). That's why many data analysis in SQL start with removing NULLs using the function.

SQL Question 6: Find the Average Successful Login Attempts per User

Assuming "N-Able" is a tech company that has a user login system, we want you to find the average number of successful login attempts per user for each day.

We have table where each row represents a single login attempt, and has a boolean that indicates whether or not the login attempt was successful.

Example Input:
1617100708/10/2022 00:00:001
2780290108/10/2022 00:00:000
3529362308/11/2022 00:00:001
4635200708/11/2022 00:00:001
4517890108/11/2022 00:00:000

We would like the answer to be formatted as such (sample output):

Example Output:


Here is the PostgreSQL query to solve this:

In this query, we first convert the column to a date type and then group by the date. Then, we use the function on the column to get the average number of successful logins per day. This AVG function aggregates the column for each date and calculates the average successful login attempts.

To practice a very similar question try this interactive Twitter Histogram of Tweets Question which is similar for "aggregating user activity data" or this Twitter Tweets' Rolling Averages Question which is similar for "calculating user behavior trends over time".

SQL Question 7: How does differ from ?

The operator merges the output of two or more statements into a single result set. It ignores duplicated rows, and makes sure each row in the result set is unique.

For a concrete example, say you were a Data Analyst at N-Able working on a Marketing Analytics project. If you needed to get the combined result set of both N-Able's Google and Facebook ads you could execute this SQL query:

The operator works in a similar way to combine data from multiple statements, but it differs from the operator when it comes to handling duplicate rows. Whereas filters out duplicates (so if the same ad_name is run on both Facebook and Google, it only shows up once), outputs duplicate rows.

SQL Question 8: Evaluating Click-through Conversion Rates for N-Able's Digital Products

N-Able had completed a marketing initiative where they displayed digital ads for their products to potential customers. Each ad had a link to the product page where users could then add the product to their carts. N-Able wants to evaluate the effectiveness of these ads in converting views into purchases.

Here is the task: Write a SQL query that calculates the conversion rate for each product, defined as the number of times the product was added to the cart after an ad click, divided by the total number of views on the product’s ad.

Example Input:
100150008/01/2022 00:00:00603215
100226508/03/2022 00:00:00603215
100362808/05/2022 00:00:00452232
100483908/07/2022 00:00:00452232
100576808/09/2022 00:00:00603215
Example Input:
200150008/01/2022 00:00:00603215
200283908/07/2022 00:00:00452232
200376808/09/2022 00:00:00603215


This query calculates the conversion rate for each product by dividing the number of times a product was added to the cart () by the number of times the product's ad was viewed (). The ensures that all products in are included in the result, even if there were no corresponding rows in . makes sure that the division operation doesn't fail because of NULL values (which could occur if a product had clicks but no add-to-cart events). The 1.0 multiplier is used to force the conversion to a float, so division results in a decimal number rather than integer division.

To solve a similar SQL problem on DataLemur's free online SQL code editor, try this Facebook SQL Interview question: SQL interview question asked by Facebook

SQL Question 9: Get the Average Sales from Different Regions

You are a data analyst at N-Able, a company that provides software solutions around the globe. Your manager wants to know the average revenue per sales representative in different regions, using data from the last quarter (Q2 2022).

Given a table named and another named , create an SQL query to return the region, sales representative id, and the average sales they made in Q2 2022.

Please note, sales can be calculated as .

Example Input:
101EMEAJohn Doe
102AmericasJane Smith
103APACMichael Lam
Example Input:


This query first joins the and tables on the . Then, it filters the sales data to only include sales from Q2 2022. It finally groups the data by region and sales representative id and calculates the average sales per sales representative for each region.

SQL Question 10: What's a database view, and what's it used for?

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!

Preparing For The N-Able SQL Interview

The best way to prepare for a N-Able SQL interview is to practice, practice, practice. In addition to solving the above N-Able SQL interview questions, you should also solve the 200+ FAANG SQL Questions on DataLemur which come from companies like Facebook, Google and unicorn tech startups. DataLemur SQL and Data Science Interview Questions

Each SQL question has hints to guide you, fully explained answers along with a discussion board to see how others solved it and most importantly, there's an interactive coding environment so you can easily right in the browser your SQL query and have it graded.

To prep for the N-Able SQL interview you can also be useful to solve SQL problems from other tech companies like:

In case your SQL foundations are weak, forget about jumping right into solving questions – refresh your SQL knowledge with this free SQL tutorial.

SQL tutorial for Data Analytics

This tutorial covers topics including advantages of CTEs vs. subqueries and Self-Joins – both of these come up routinely in N-Able SQL interviews.

N-Able Data Science Interview Tips

What Do N-Able Data Science Interviews Cover?

Besides SQL interview questions, the other types of problems tested in the N-Able Data Science Interview are:

N-Able Data Scientist

How To Prepare for N-Able Data Science Interviews?

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