9 GoDaddy SQL Interview Questions (Updated 2024)
At GoDaddy, SQL is used day-to-day for analyzing customer behavior patterns to optimize user experience and managing domain registration data to inform business strategies. Unsurprisingly this is why GoDaddy frequently asks SQL problems in interviews for Data Science, Analytics, and & Data Engineering jobs.
To help you practice for the GoDaddy SQL interview, this blog covers 9 GoDaddy SQL interview questions – can you solve them?
9 GoDaddy SQL Interview Questions
SQL Question 1: Calculate the Monthly Average Rating For Each Product
At GoDaddy, it's crucial to understand the customer feedback on different products to improve them continually and satisfy the customer's needs. For this task, you are given a 'reviews' table that contains product review data. Your task is to write a SQL query to calculate the monthly average rating (stars) for each product.
Note: The month is derived from the submit_date column and average on the stars column for each product.
Answer:
The following PostgreSQL query would solve the problem:
This query first creates a CTE to extract the month from the column. Then, it groups the result by month and , and applies the AVG function to the column to give the average rating per month for each product. The result is ordered by month and for clear visualization.
p.s. Window functions show up super frequently during SQL interviews, so practice the 27+ window function questions on DataLemur
SQL Question 2: Analyzing GoDaddy's Click-Through and Conversion Rates
GoDaddy is a digital platform that offers domain registration, website hosting, and related services. You've been given two datasets, and , that record events of users clicking on GoDaddy's digital ads and users adding a product to their cart after clicking the ad.
For a given period, find the Click-Through Rate (CTR) defined as the percentage of ad clicks that led to a user viewing a product, and the Click-Through Conversion Rate (CTCR) which is the percentage of ad clicks that led to a user adding a product to their cart.
Example Input:
| click_id | user_id | product_id | click_timestamp |
|---|---|---|---|
| 1001 | 101 | 6001 | 06/14/2022 11:00:00 |
| 1002 | 125 | 6001 | 06/14/2022 11:20:00 |
| 1003 | 130 | 7003 | 06/14/2022 11:30:00 |
| 1004 | 140 | 8004 | 06/14/2022 12:00:00 |
Example Input:
| cart_id | user_id | product_id | add_timestamp |
|---|---|---|---|
| 2001 | 101 | 6001 | 06/14/2022 11:10:00 |
| 2002 | 140 | 8004 | 06/14/2022 12:10:00 |
| 2003 | 150 | 9005 | 06/14/2022 12:30:00 |
Answer:
The query uses common table expressions(CTEs) to count the total clicks and views within the given time range, and then uses these counts to calculate the CTR and CTCR.
To practice another question about calculating rates, solve this TikTok SQL Interview Question within DataLemur's interactive SQL code editor:
SQL Question 3: Can you explain what SQL constraints are, and why they are useful?
In a database, constraints are rules that the DBMS (database management system) follows when performing operations such as inserting, updating, or deleting data.
For example, consider a table of employee records at GoDaddy. Some constraints that you might want to implement include:
- NOT NULL: This constraint ensures that certain columns, such as the employee's first and last name, cannot be NULL.
- UNIQUE: This constraint ensures that each employee has a unique employee ID.
- PRIMARY KEY: This constraint combines the NOT NULL and UNIQUE constraints to create a primary key for the employee table. The primary key is a column or set of columns that uniquely identifies each row in the table.
- FOREIGN KEY: This constraint establishes a relationship between the employee table and other tables in the database. For example, you could use a foreign key to link the employee ID to the department ID in a department table to track which department each employee belongs to.
- DEFAULT: This constraint specifies a default value for a column in case no value is specified when a new row is inserted into the table.
SQL Question 4: Average Revenue per Hosting Package Type
GoDaddy offers various types of web hosting packages like Shared Hosting, VPS Hosting, Dedicated Hosting, and Wordpress Hosting. Each hosting type can have different price points, and these are spread across multiple sales transactions across a year. The management wants to analyze the average revenue for each type of hosting package.
Given a table that logs all transactions at GoDaddy containing the fields , , , , and , write a PostgreSQL query to find the average sales price for each type of hosting package.
Example Input:
| transaction_id | customer_id | package_type | transaction_date | price |
|---|---|---|---|---|
| 35961 | 12345 | Shared Hosting | 2022-08-01 | 109.99 |
| 93582 | 67890 | Dedicated Hosting | 2022-08-02 | 299.99 |
| 21593 | 34567 | VPS Hosting | 2022-08-03 | 199.99 |
| 79682 | 98432 | Wordpress Hosting | 2022-08-04 | 149.99 |
| 61325 | 23789 | Shared Hosting | 2022-08-05 | 99.99 |
Example Output:
| package_type | average_price |
|---|---|
| Shared Hosting | 104.99 |
| Dedicated Hosting | 299.99 |
| VPS Hosting | 199.99 |
| Wordpress Hosting | 149.99 |
Answer:
In this query, we are using the clause to group rows that have the same values in the column. Then, for each group, we calculate the average using the function. This result set will give us the average for each .
SQL Question 5: 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 GoDaddy, 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 6: Joining and Analyzing Customer and Domain Registrations
GoDaddy is a company that provides domain registration and hosting services. Here we have a table that tracks basic customer information and a table that records all domain registrations. Your task is to write SQL to analyze the customer database and join it with the domain registration table to find the total number of domains registered by each user in the year 2021.
Example Input:
| customer_id | first_name | last_name | signup_date |
|---|---|---|---|
| 123 | John | Doe | 2018-03-15 |
| 456 | Jane | Doe | 2015-11-27 |
| 789 | Alice | Smith | 2017-07-22 |
| 321 | Bob | Johnson | 2019-02-13 |
| 654 | Charlie | Brown | 2020-08-28 |
Example Input:
| domain_id | customer_id | domain_name | registration_date |
|---|---|---|---|
| 1 | 123 | john-doe.com | 2021-03-15 |
| 2 | 456 | janedoe.net | 2021-01-27 |
| 3 | 789 | alicesmith.org | 2021-05-22 |
| 4 | 321 | bob-johnson.co | 2018-10-10 |
| 5 | 654 | charliebrown.com | 2020-10-28 |
Answer:
This PostgreSQL query joins the table with the table on the . It then filters results for domains registered in the year 2021. The final result is grouped by the and it gives a count of domains per user for the year 2021.
Since joins come up so often during SQL interviews, try an interactive Spotify JOIN SQL question:
SQL Question 7: What's the purpose of the constraint?
A is a field in a table that references the of another table. It creates a link between the two tables and ensures that the data in the field is valid.
For example, if you have a table of GoDaddy customers and an orders table, the customer_id column in the orders table could be a that references the id column (which is the primary key) in the GoDaddy customers table.
The constraint helps maintain the integrity of the data in the database by preventing the insertion of rows in the table that do not have corresponding entries in the table. It also enforces the relationship between the two tables and prevents data from being deleted from the table if it is still being referenced in the table. Sure, here's an SQL question that relies on mathematics quite heavily to provide the solution:
SQL Question 8: Sales Performance Analysis
Suppose you are a data analyst at GoDaddy and you've been given two tables, and , containing information about the sales team members and the sales they've made, respectively.
The table contains the following columns:
- : Identifies the team the sales person belongs to
- : Uniquely identifies each sales person
- : Area where the sales person operates
The table contains the following columns:
- : Unique identifier for each sale
- : Identifier for the sales person who made the sale
- : Type of product that was sold
- : Price of one unit of the product
- : Number of units sold in the transaction
- : The date when the sale was made
Your task is to write an SQL query that computes the total sales (in dollars), average unit price (rounded to two decimal places), and the standard deviation of units sold (rounded to two decimal places) for all sales in each region and product type during the last month (Assume the current date as the last day of the previous month).
Example Input:
| team_id | sales_person_id | region |
|---|---|---|
| 1 | 1 | 'North' |
| 2 | 2 | 'South' |
| 1 | 3 | 'North' |
| 3 | 4 | 'East' |
| 2 | 5 | 'South' |
Example Input:
| sales_id | sales_person_id | product_type | unit_price | units_sold | sale_date |
|---|---|---|---|---|---|
| 1 | 1 | 'Domain' | 10 | 4 | '02/01/2022' |
| 2 | 2 | 'Hosting' | 100 | 2 | '02/15/2022' |
| 3 | 1 | 'Domain' | 15 | 6 | '02/22/2022' |
| 4 | 3 | 'Domain' | 8 | 8 | '02/28/2022' |
| 5 | 4 | 'Security' | 200 | 1 | '01/10/2022' |
Answer:
Here's the SQL query to solve the task:
This query returns the region, product type, total sales, average unit price, and the standard deviation of units sold for each product type in each region during the last month. The function is used to calculate the standard deviation, while the and functions tally up the average unit price and total sales, respectively. The results are rounded to two decimal places for readability using the function.
To practice a very similar question try this interactive Amazon Highest-Grossing Items Question which is similar for involving sales data analysis or this Wayfair Y-on-Y Growth Rate Question which is similar for calculating sales growth.
SQL Question 9: Analyzing Monthly Website Domain Registrations
As a data analyst at GoDaddy, you are tasked with analyzing the pattern of user behavior when it comes to registering domains. GoDaddy would like to understand how the number of domain registrations varies month by month. Write an SQL query that will return the number of domains registered each month for the year 2022.
Example Input:
| domain_id | user_id | registration_date | domain_name |
|---|---|---|---|
| 101 | 1234 | 01/15/2022 00:00:00 | example1.com |
| 102 | 1256 | 01/20/2022 00:00:00 | example2.net |
| 103 | 1362 | 02/10/2022 00:00:00 | example3.org |
| 104 | 1453 | 02/18/2022 00:00:00 | example4.io |
| 105 | 1562 | 03/05/2022 00:00:00 | example5.com |
Example Output:
| month | numberOfDomainsRegistered |
|---|---|
| 1 | 2 |
| 2 | 2 |
| 3 | 1 |
Answer:
In this SQL query, we utilize the DATE_PART function to extract the month and year from the registration_date field. We filter for the year 2022, count the number of occurrences (i.e., registrations), and group by the month. The order by clause is used to sort the output in ascending order by month which makes the output easier to interpret.
How To Prepare for the GoDaddy 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.
Besides solving the earlier GoDaddy SQL interview questions, you should also solve the 200+ SQL questions on DataLemur which come from companies like Netflix, Google, and Amazon.
Each problem on DataLemur has hints to guide you, detailed solutions and most importantly, there's an online SQL code editor so you can instantly run your SQL query and have it executed.
To prep for the GoDaddy SQL interview you can also be a great idea to solve SQL questions from other tech companies like:
However, if your SQL coding skills are weak, forget about diving straight into solving questions – go learn SQL with this free SQL tutorial.
This tutorial covers SQL topics like finding NULLs and INTERCEPT/EXCEPT – both of which show up often in SQL job interviews at GoDaddy.
GoDaddy Data Science Interview Tips
What Do GoDaddy Data Science Interviews Cover?
Besides SQL interview questions, the other types of problems covered in the GoDaddy Data Science Interview are:
- Probability & Stats Questions
- Python or R Coding Questions
- Product Analytics Questions
- ML Interview Questions
- Resume-Based Behavioral Questions
How To Prepare for GoDaddy Data Science Interviews?
The best way to prepare for GoDaddy Data Science interviews is by reading Ace the Data Science Interview. The book's got:
- 201 Interview Questions from FAANG & startups
- A Crash Course on SQL, AB Testing & ML
- Amazing Reviews (900+ 5-star reviews on Amazon)