9 Sage Group SQL Interview Questions (Updated 2024)
At Sage Group, SQL is used all the damn time for analyzing customer usage data to improve their cloud accounting, payroll, and HR.That's why Sage Group almost always evaluates jobseekers on SQL problems during interviews for Data Analytics, Data Science, and Data Engineering jobs.
As such, to help you prepare for the Sage Group SQL interview, this blog covers 9 Sage Group SQL interview questions – how many can you solve?
9 Sage Group SQL Interview Questions
SQL Question 1: Identify Whale Users in Sage Group
Sage Group is an international enterprise software supplier. They have a range of software solutions and services with customers around the globe. To be able to better provide service and improve their products, they need to identify 'whale users' - those users who bring in significant revenue through frequent purchase transactions.
Assuming we have a sample transactions database as follows:
Example Input:
| transaction_id | user_id | purchase_date | software_id | price |
|---|---|---|---|---|
| 001 | 12345 | 01/01/2022 | 001 | 500 |
| 002 | 23456 | 01/02/2022 | 002 | 450 |
| 003 | 12345 | 01/03/2022 | 003 | 550 |
| 004 | 34567 | 01/05/2022 | 004 | 600 |
| 005 | 12345 | 02/01/2022 | 003 | 550 |
A 'whale user' in the context of Sage Group could be defined as a user who makes purchases with total value above a certain threshold in a given period of time (e.g. a month). Let's set the threshold at 1000 for this example.
Answer:
To solve this issue, we can write a SQL query to sum the price for each user_id in a given range of time (in this case, we're assuming a month), and then only select users where the total is above 1000. Here's an example PostgreSQL query for this:
This will return a list of userid's, the month of their purchases and the total amount of purchases for users that are considered "whale users".
To practice a similar customer analytics SQL question where you can code right in the browser and have your SQL solution automatically checked, try this Walmart Labs SQL Interview Question:
SQL Question 2: Analyze Sales Over Time
The Sage group is interested in analyzing their sales data for their various products over time. For each month, they want to know the total sales and the average sale price per product.
Using the table, as provided below, write a SQL query to calculate the total sales and average sales price for each product per month.
Example Input:
| sales_id | product_id | sale_date | sale_price |
|---|---|---|---|
| 1 | 101 | 2022-03-01 | 200 |
| 2 | 101 | 2022-03-15 | 220 |
| 3 | 101 | 2022-04-05 | 210 |
| 4 | 102 | 2022-03-03 | 150 |
| 5 | 102 | 2022-03-10 | 120 |
| 6 | 103 | 2022-03-20 | 300 |
| 7 | 103 | 2022-04-08 | 320 |
| 8 | 103 | 2022-04-22 | 310 |
Example Output:
| year | month | product | total_sales | avg_sale_price |
|---|---|---|---|---|
| 2022 | 3 | 101 | 420 | 210 |
| 2022 | 4 | 101 | 210 | 210 |
| 2022 | 3 | 102 | 270 | 135 |
| 2022 | 3 | 103 | 300 | 300 |
| 2022 | 4 | 103 | 630 | 315 |
Answer:
In the SQL query above, we first partition the sales data by year, month, and product_id. This results in unique combinations of year, month, and product. We then apply the and functions to calculate the total sales and average sales price, respectively, for each combination. The clause is used to sort the output by year, month, and product.
Pro Tip: Window functions are a popular SQL interview topic, so practice every window function problem on DataLemur
SQL Question 3: What's the difference between a clustered and non-clustered index?
A clustered index is an index where the order of the rows in the database corresponds to the order of the rows in the index. Because of this, a table can only have one clustered index, but it can have multiple non-clustered indexes.
The main difference between the two is that the database tries to keep the data in the same order as the corresponding keys in the clustered index. This can improve the performance of most query operations, as it provides a linear-access path to the data stored in the database.
SQL Question 4: Average duration of projects managed by each project manager at Sage Group
Given a database of projects that are being managed or have been managed within the Sage Group, and the associated project managers, calculate the average duration in days of the projects for each project manager in the database.
Example Input:
| project_id | project_manager_id | start_date | end_date |
|---|---|---|---|
| 101 | 400 | 01/01/2022 | 01/31/2022 |
| 102 | 500 | 02/01/2022 | 03/01/2022 |
| 103 | 400 | 01/15/2022 | 02/15/2022 |
| 104 | 600 | 03/01/2022 | 04/01/2022 |
| 105 | 500 | 02/15/2022 | 03/15/2022 |
Example Output:
| project_manager_id | average_project_duration |
|---|---|
| 400 | 30.50 |
| 500 | 30.00 |
| 600 | 31.00 |
Answer:
This query calculates the duration of each project first, and then calculates the average duration for each project manager.
In the given example, we have 3 project managers. Project manager 400 leads 2 projects with durations of 31 and 30 days, thus the average is 30.5 days. Similarly, manager 500 leads 2 projects with durations of 29 and 31 days, so the average is 30 days. Finally, manager 600 leads a single project with a duration of 31 days, so the average is 31 days.
To practice a very similar question try this interactive Facebook Average Post Hiatus (Part 1) Question which is similar for calculating date differences or this Alibaba Compressed Mean Question which is similar for requiring SQL aggregations.
SQL Question 5: In database normalization, what's the distinction between 1NF, 2NF, and 3NF?
The three levels of database normalization (also known as normal forms) are:
First Normal Form (1NF):
- Each column should contain the same type of data (no mixing strings vs. integers)
- Each column in a table contains a single value (no lists or containers of data)
- Each row in the table is unique
Second Normal Form (2NF)
- It's in 1st Normal Form
- All non-key attributes are dependent on the primary key
Said another way, to achieve 2NF, besides following the 1NF criteria, the columns should also be dependent only on that table's primary key.
Third normal form (3NF) if it meets the following criteria:
- It's in 2nd Normal Form
- There are no transitive dependencies in the table.
A transitive dependency means values in one column are derived from data in another column. For example, it wouldn't make sense to keep a column called ""vehicle's age"" and ""vehicle's manufacture date" (because the vehicle's age can be derived from the date it was manufactured).
Although there is a 4NF and 5NF, you probably don't need to know it for the Sage Group interview.
SQL Question 6: Average Sales per Product Category
Considering that Sage Group is a software company selling different categories of software products, here is a SQL interview question related to that:
For each product category, what was the total quantity sold and the average sales per item in the last quarter of 2021?
Given the following tables and :
Example Input:
| product_id | product_name | category |
|---|---|---|
| 1 | Sage 50cloud | Accounting |
| 2 | Sage X3 | ERP |
| 3 | Sage Intacct | Financial Management |
| 4 | Sage People | HR and People System |
| 5 | Sage CRM | Customer Relationship Management |
Example Input:
| sale_id | product_id | quantity_sold | sale_date | price_per_item |
|---|---|---|---|---|
| 1001 | 1 | 50 | 10/10/2021 00:00:00 | 100 |
| 1002 | 2 | 25 | 11/11/2021 00:00:00 | 200 |
| 1003 | 2 | 75 | 12/10/2021 00:00:00 | 150 |
| 1004 | 3 | 30 | 09/15/2021 00:00:00 | 400 |
| 1005 | 1 | 50 | 12/25/2021 00:00:00 | 90 |
| 1006 | 4 | 20 | 10/05/2021 00:00:00 | 350 |
| 1007 | 5 | 10 | 11/20/2021 00:00:00 | 500 |
Answer:
This query joins the and table on the field. It narrows down the records to the last quarter of the year 2021. It then groups the data by product category (as per ) and calculates the total quantity sold and averaged sales for each category. The SUM() and AVG() functions are used to compute the total and average values, respectively.
SQL Question 7: Can you explain the purpose of the constraint and give an example of when you might use it?
The CHECK constraint is used to enforce rules on the data in a specific column. If a row is inserted or updated with data that does not follow the CHECK constraint's rule, the operation will fail. The CHECK constraint is often used with other constraints, such as NOT NULL or UNIQUE, to ensure that data meets certain conditions. You may want to use a CHECK constraint in your database if you want to maintain the quality and reliability of your data by ensuring that it meets specific criteria.
For example, you might use a CHECK constraint to ensure that a column contains only positive numbers, or that a date is within a certain range.
SQL Question 8: Find Customers with 'Sage' in Their Name
As a part of the Sage group, we are interested in identifying our customers who have 'Sage' in their names. Please write a SQL query that selects all customers from the 'customers' table where the 'name' field contains the word 'Sage'. Note that the match should be case-insensitive.
Example Input:
| customer_id | name | country | signup_date |
|---|---|---|---|
| 101 | John Sage | USA | 2021-01-10 |
| 102 | Sage Williams | UK | 2021-03-12 |
| 103 | Sarah K. | USA | 2021-05-18 |
| 104 | SageGroupie | IND | 2021-07-30 |
| 105 | Joseph Sageley | AUS | 2021-09-11 |
Answer:
This will result in the following output:
Example Output:
| customer_id | name | country | signup_date |
|---|---|---|---|
| 101 | John Sage | USA | 2021-01-10 |
| 102 | Sage Williams | UK | 2021-03-12 |
| 104 | SageGroupie | IND | 2021-07-30 |
| 105 | Joseph Sageley | AUS | 2021-09-11 |
The query uses the SQL LIKE operator in combination with the '%' wildcard to filter the customers whose name contains the string 'sage'. The function LOWER is used to make this operation case-insensitive, ensuring that it matches 'SAGE', 'sage', 'Sage' and so on.
SQL Question 9: Analysis of Customer Purchase History
Suppose you are managing a database at Sage Group. The two relevant tables are and , which contain information about the customers and their purchase history respectively.
You need to write a query that joins these two tables and returns the list of customers who have made at least one purchase, along with the total amount spent by each customer. Include only those customers who have spent more than $1000.
Sample Input:
| customer_id | first_name | last_name | |
|---|---|---|---|
| 101 | John | Doe | johndoe@example.com |
| 102 | Jane | Smith | janesmith@example.com |
| 103 | Jim | Brown | jimbrown@example.com |
Sample Input:
| purchase_id | customer_id | item_number | purchase_amount |
|---|---|---|---|
| 201 | 101 | 12345 | $500 |
| 202 | 101 | 23456 | $750 |
| 203 | 102 | 12345 | $700 |
| 204 | 103 | 67890 | $500 |
Example Output:
| customer_id | first_name | last_name | total_purchase_amount | |
|---|---|---|---|---|
| 101 | John | Doe | johndoe@example.com | $1250 |
Answer:
In this query, we first join the and tables on the column, which is common between them. We then group the results by , , , and , followed by calculating the total purchase amount for each customer. The clause helps us filter out customers who have spent more than $1000.
Since joins come up frequently during SQL interviews, try this interactive Snapchat SQL Interview question using JOINS:
Preparing For The Sage Group 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.
In addition to solving the above Sage Group SQL interview questions, you should also solve the 200+ SQL Interview Questions on DataLemur which come from companies like Facebook, Google, and VC-backed startups.
Each exercise has hints to guide you, step-by-step solutions and most importantly, there is an online SQL code editor so you can easily right in the browser your query and have it executed.
To prep for the Sage Group SQL interview it is also helpful to solve interview questions from other tech companies like:
But if your SQL foundations are weak, forget about going right into solving questions – refresh your SQL knowledge with this interactive SQL tutorial.
This tutorial covers things like Union vs. UNION ALL and GROUP BY – both of which pop up often during SQL job interviews at Sage Group.
Sage Group Data Science Interview Tips
What Do Sage Group Data Science Interviews Cover?
Beyond writing SQL queries, the other types of questions to prepare for the Sage Group Data Science Interview are:
- Probability & Statistics Questions
- Python Pandas or R Coding Questions
- Analytics and Product-Metrics Questions
- Machine Learning Questions
- Resume-Based Behavioral Questions
How To Prepare for Sage Group Data Science Interviews?
To prepare for Sage Group Data Science interviews read the book Ace the Data Science Interview because it's got:
- 201 interview questions taken from FAANG (FB, Apple, Amazon, Netflix, Google)
- a refresher covering Python, SQL & ML
- over 900+ reviews on Amazon & 4.5-star rating