10 Sprout Social SQL Interview Questions (Updated 2024)

At Sprout Social, SQL is used all the damn time for analyzing social media data for insights and managing user data for personalized client experiences. That's why Sprout Social typically asks SQL query questions in interviews for Data Science and Data Engineering positions.

Thus, to help you ace the Sprout Social SQL interview, here’s 10 Sprout Social SQL interview questions – able to solve them?

10 Sprout Social SQL Interview Questions

SQL Question 1: Identifying Power Users in Sprout Social's Database

Sprout Social, as a social media management platform, would likely define a "power user" or "VIP user" as someone who is a frequent and engaged user of the platform. Therefore, a useful metric for identifying power users might be those who have posted the most social media updates via their platform.

Assume you have the following two tables in the Sprout Social database:

Example Input:

Example Input:

The question is, write a PostgreSQL query to find the top 5 users who have posted the most social media updates in the last 6 months. Including the total number of posts, the user’s username, and the user's plan type in the output.

Answer:

This SQL query first joins the table with the table using the in both tables. It then selects the and fields from the table and calculates the total number of updates posted by each user in the table. It filters the updates considering only those posted within the last 6 months. The clause groups the result by user, and the and clauses select the top 5 users with the most updates.

To practice a super-customer analysis question on DataLemur's free interactive coding environment, try this Microsoft SQL Interview problem:

SQL Question 2: Analyzing Campaign Performance

Sprout Social is a social media management and optimization platform and naturally, they run various marketing campaigns on different sectors. They want to analyze the success of their campaigns by calculating different metrics such as average, max, min, and total likes, comments, and shares for each of their campaigns.

Suppose the and tables look like the following:

Example Input:

Example Input:

They want to calculate the aforementioned metrics for all campaigns. This is where you'd use window functions to generate aggregates without reducing the number of rows by grouping.

Answer:

The following PostgreSQL query will calculate the required metrics for each campaign:

This SQL query uses window functions partitioned by to calculate the average, max, min, and total likes, comments, and shares for each campaign on different sectors without reducing the rows by groups. It gives them insights into the performance of their campaigns. This can be useful for making decisions about where to invest more for future campaigns.

p.s. Window functions show up super frequently during SQL interviews, so practice the 27+ window function questions on DataLemur

SQL Question 3: Can you explain the distinction between cross join and natural join?

A cross join is a type of JOIN operation in SQL that creates a new table by combining each row from the first table with every row from the second table. It is also known as a cartesian join.

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

A natural join, on the other hand, is a type of JOIN that combines rows from two or more tables based on their common columns. It is called a "natural" join because it is based on the natural relationship that exists between the common columns in the tables being joined.

For an example of each one, say you had sales data exported from Sprout Social's Salesforce CRM stored in a datawarehouse which had two tables: and .

An (which is a type of natural join) combines the two tables on the common

This query will return rows from the sales and tables that have matching customer id values. Only rows with matching values will be included in the results.

One main difference between cross joins and natural joins is that cross joins do not require any common columns between the tables being joined, while natural joins do. Another difference is that cross joins can create very large tables if the input tables have a large number of rows, while natural joins will only return a table with the number of rows equal to the number of matching rows in the input tables.

SQL Question 4: Filter Customers List based on Subscription and Activation Dates

Given a database of that contains , (the date when the customer joined), and (indicating if the customer is subscribed to a Sprout Social premium plan), write a query to filter out the list of customers who have a subscription and whose activation dates are not older than a year.

For this specific situation, today's date will be considered as .

Example Input:

Answer:

This PostgreSQL query retrieves the rows from the table where the 'subscription' is and the 'activation_date' is later than (indicating that they have been customers for less than a year). This filters the original customers list down to a subset of customers who are currently subscribed and have joined less than a year ago.

Example Output:

SQL Question 5: What does / SQL commands do?

The / operator is used to remove to return all rows from the first SELECT statement that are not returned by the second SELECT statement.

Note that is available in PostgreSQL and SQL Server, while MINUS is available in MySQL and Oracle (but don't worry about knowing which DBMS supports which exact commands since Sprout Social interviewers aren't trying to trip you up on memorizing SQL syntax).

For a tangible example of in PostgreSQL, suppose you were doing an HR Analytics project for Sprout Social, and had access to Sprout Social'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 operator to find all contractors who never were a employee using this query:

SQL Question 6: Analysing Click-Through-Rates for Digital Ads on Sprout Social

Sprout Social runs digital ads to attract leads to its products. These ads have a two-step conversion process; first, leads click on the ads (click-through), and then they add a product to their cart (conversion). We need to calculate the click-through-conversion rate, which is the number of product added to the cart, divided by the total number of ad click-through.

Assuming we have two tables: and where,

• captures the , , and timestamp for each click
• captures the , , , and timestamp for each product addition

Example Input

Example Input

Answer:

This SQL query starts by joining the table with the table on the field. It then divides the count of unique from the table, by the count of unique from the table, to obtain the click-through-conversion rate. Note, this query assumes that a customer can only add an item to the cart once, and that an ad click is related to, or occurs before, a cart addition.

To solve a similar problem about calculating rates, try this TikTok SQL question on DataLemur's interactive coding environment:

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

The keyword removes duplicates from a query.

Suppose you had a table of Sprout Social customers, and wanted to figure out which cities the customers lived in, but didn't want duplicate results.

table:

You could write a query like this to filter out the repeated cities:

Your result would be:

SQL Question 8: Analyze customer engagements data with relevant user data

For the social media management company, Sprout Social, understanding customer engagements is crucial. Write a SQL query that will analyze the engagements table and join it with the users table to provide information about the engagements per user grouped by different user's countries. Assuming we have an engagements table where each record represents a unique social media engagement a user has had (e.g., likes, shares) and a users table with the details of each user of the platform.

Example Input:

Example Input:

Answer:

This Postgres SQL query is combining the engagements and users tables using an INNER JOIN on the user_id. It then counts the number of engagements for each user, grouped by their country.

Because join questions come up frequently during SQL interviews, try this Snapchat SQL Interview question using JOINS:

SQL Question 9: Analyzing Ad Campaign Performance

Sprout Social wants to analyse the performance of a number of ad campaigns based on the number of impressions, click through rates, and overall costs. Each ad campaign has a specific product attached to it.

You are tasked to find out for each ad campaign the following:

• Total impressions (the number of times the ad was displayed),
• Total click throughs (the number of times the ad was clicked),
• Click through rate (the ratio of click throughs to impressions),
• Total cost (the amount spent on the ad campaign), and
• Cost per impression (the cost divided by the number of impressions).

Ad campaigns where the total impressions is zero should be excluded from the result. Please return the results rounded to 3 decimal places.

Example Input:

Example Input:

Example Output:

Answer:

This query first joins the ad campaigns data with the products data based on the product id. It then calculates the click through rate by dividing the total click throughs by the total impressions and then converting it to a float to allow decimal calculations. This result is then rounded to three decimal places. The cost per impression is calculated in a similar manner. The WHERE clause is used to exclude ad campaigns where the total impressions is zero.

SQL Question 10: What are the similarities and difference between relational and NoSQL databases?

While both types of databases are used to store data (no duh!), relational databases and non-relational (also known as NoSQL databases) differ in a few important ways:

Data model: Relational databases use a data model consisting of tables and rows, while NoSQL databases use a variety of data models, including document, key-value, columnar, and graph storage formats.

Data integrity: Relational databases use structured query language (SQL) and enforce strict data integrity rules through the use of foreign keys and transactions. NoSQL databases may not use SQL and may have more relaxed data integrity rules.

Structure: Relational databases store data in a fixed, structured format, while NoSQL databases allow for more flexibility in terms of data structure.

ACID compliance: Relational databases are typically into shrooms and are ACID-compliant (atomic, consistent, isolated, and durable), while NoSQL databases may not be fully ACID-compliant (but they try their best... and it's effort that counts...or at least that's what my gym teacher told me!)

Sprout Social SQL Interview Tips

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

Each problem on DataLemur has hints to guide you, fully explained answers along with a discussion board to see how others solved it and crucially, there is an interactive coding environment so you can right online code up your SQL query and have it graded.

To prep for the Sprout Social SQL interview it is also a great idea to practice SQL problems from other tech companies like:

• Sprinklr SQL questions

• HubSpot SQL questions

• Hootsuite SQL questions

• Salesforce SQL questions

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

This tutorial covers things like filtering groups with HAVING and math functions like CEIL()/FLOOR() – both of these come up often in SQL job interviews at Sprout Social.

Sprout Social Data Science Interview Tips

What Do Sprout Social Data Science Interviews Cover?

Beyond writing SQL queries, the other types of problems covered in the Sprout Social Data Science Interview are:

• Statistics and AB Testing Questions
• Coding Questions in Python or R
• Product-Sense and Business Case Questions
• ML Modelling Questions
• Behavioral Interview Questions

How To Prepare for Sprout Social Data Science Interviews?

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

• 201 interview questions taken from tech companies like Google & Microsoft
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