At Bytes Technology, SQL does the heavy lifting for extracting and analyzing customer behavior data to enhance product features. Because of this, Bytes Technology asks SQL questions during interviews for Data Science, Data Engineering and Data Analytics jobs.
To help you ace the Bytes Technology SQL interview, here’s 9 Bytes Technology SQL interview questions – able to answer them all?
Given two tables and , where logs users' purchasing activities and contains personal information about users. Power users are those who have made at least 10 purchases in the past month. Write an SQL query to find the user_ids and respective names of power users.
purchase_id | user_id | product_id | purchase_date |
---|---|---|---|
100 | 59 | 4001 | 2022-09-15 |
101 | 37 | 8745 | 2022-09-16 |
102 | 59 | 6805 | 2022-09-19 |
103 | 62 | 7234 | 2022-09-10 |
104 | 59 | 8745 | 2022-09-23 |
105 | 37 | 7234 | 2022-09-26 |
106 | 59 | 8503 | 2022-09-10 |
107 | 63 | 8745 | 2022-09-26 |
108 | 37 | 5003 | 2022-09-06 |
109 | 59 | 7234 | 2022-09-22 |
110 | 59 | 4001 | 2022-09-19 |
111 | 37 | 5003 | 2022-09-06 |
112 | 37 | 8745 | 2022-09-16 |
113 | 59 | 7234 | 2022-09-18 |
114 | 59 | 8745 | 2022-09-23 |
user_id | first_name | last_name |
---|---|---|
37 | Karen | Doe |
59 | John | Smith |
62 | Jane | Parker |
63 | Mike | Johnson |
Here's a PostgreSQL query to find the power users from the provided tables:
This query first selects user_ids from the table who have made 10 or more purchases in the past month. This subquery returns a list of such user IDs. Then, the main query fetches the details of these users from the table. Remember, the dates will always be relative to the current date when executing this query.
To solve a related super-user data analysis question on DataLemur's free online SQL code editor, try this recently asked Microsoft SQL interview question:
Bytes Technology has a sales team whose performance is tracked monthly. The firm wants to understand the monthly revenue progress of each salesperson. Your task is to write a SQL query that calculates the cumulative sales revenue of each salesperson for every month.
sale_id | salesperson_id | sale_date | product_id | sale_price |
---|---|---|---|---|
101 | 1 | 02/01/2022 | 1001 | 500 |
102 | 2 | 02/15/2022 | 1002 | 300 |
103 | 2 | 02/20/2022 | 1001 | 500 |
104 | 1 | 03/01/2022 | 1002 | 300 |
105 | 1 | 03/15/2022 | 1001 | 500 |
106 | 2 | 03/20/2022 | 1003 | 700 |
107 | 1 | 04/05/2022 | 1001 | 500 |
108 | 2 | 04/15/2022 | 1002 | 300 |
month | salesperson_id | cumulative_revenue |
---|---|---|
2 | 1 | 500 |
2 | 2 | 800 |
3 | 1 | 1300 |
3 | 2 | 1500 |
4 | 1 | 1800 |
4 | 2 | 1800 |
The above query partitions the data by salesperson_id then orders it by sale_date to find the cumulative sales for each month. For each month, we sum the sales for that month and all prior months in that year for each salesperson. This way we get the cumulative sales revenue for each salesperson for each month.
To practice a related window function SQL problem on DataLemur's free interactive coding environment, try this Amazon SQL Interview Question:
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The CHECK constraint is used to set a rule for the data in a column. If a row is inserted or updated and the data in the column does not follow the rule specified by the CHECK constraint, the operation will be unsuccessful.The CHECK constraint is often used in conjunction with other constraints, such as NOT NULL or UNIQUE.
You might consider implementing the CHECK constraint in your database if you want to ensure that certain data meets specific conditions. This can be helpful for maintaining the quality and reliability of your data.
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.
Bytes Technology is interested in analyzing the use of their software products among business customers. In order to do this, they need to filter their customer database to find all business customers who have purchased their software in the last two years and who have more than 50 employees. Assume the 'customers' table has columns for 'customer_id', 'purchase_date', 'industry_type' and 'number_of_employees'.
customer_id | purchase_date | industry_type | number_of_employees |
---|---|---|---|
121 | 01/10/2019 | 'Business' | 45 |
482 | 04/22/2020 | 'Business' | 65 |
893 | 11/08/2021 | 'Education' | 30 |
365 | 10/30/2019 | 'Business' | 60 |
546 | 03/25/2021 | 'Business' | 55 |
customer_id | purchase_date | industry_type | number_of_employees |
---|---|---|---|
482 | 04/22/2020 | 'Business' | 65 |
365 | 10/30/2019 | 'Business' | 60 |
546 | 03/25/2021 | 'Business' | 55 |
This SQL query filters out the relevant rows from the table where is later than or equal to January 1, 2020 (i.e., within the last two years), is 'Business', and is more than 50. The query uses the command to apply these multiple boolean conditions.
An inner join returns only the rows that match the join condition between the two tables, whereas a full outer join returns all rows from both tables, including any unmatched rows.
Example:
Example:
Bytes Technology, a software product company, runs several digital ad campaigns to boost their software sales. They are interested in analyzing their campaign performance over different periods of time. Particularly, they are interested in the click-through rates (the number of times an ad was clicked after being viewed) and the conversion rates (number of times the navigation from ad-click resulted in adding the product to the cart).
For these calculations, assume that if a user clicks on an ad multiple times, each click is counted separately. Similarly, if a user adds the product to the cart multiple times, each instance would also count as separate conversion.
Given the tables , , and below, write a query that calculates the click-through and conversion rates on a monthly basis.
view_id | user_id | view_date | ad_id |
---|---|---|---|
1001 | 745 | 05/06/2022 18:34:00 | 6001 |
1002 | 340 | 05/06/2022 18:35:00 | 6002 |
1003 | 101 | 05/07/2022 12:00:00 | 6001 |
1004 | 340 | 05/08/2022 14:30:00 | 6002 |
click_id | user_id | click_date | ad_id |
---|---|---|---|
5001 | 745 | 05/06/2022 18:40:00 | 6001 |
5002 | 340 | 05/06/2022 18:36:00 | 6002 |
5003 | 101 | 05/07/2022 12:05:00 | 6001 |
add_id | user_id | add_date | ad_id |
---|---|---|---|
2001 | 745 | 05/06/2022 18:45:00 | 6001 |
2002 | 101 | 05/07/2022 12:10:00 | 6001 |
This SQL block starts by joining the three tables: , , and . It then groups by month and . Finally, it calculates the click-through and conversion rates and displays these values with the total number of views, clicks, and adds. In this way, we calculate the click-through and conversion rates for each advertisement on a monthly basis. Please note that join conditions also ensure that clicks and adds happen after views and clicks respectively.
To solve a related problem on DataLemur's free online SQL code editor, try this Facebook SQL Interview question:
The clause works similarly to the clause, but it is used to filter the groups of rows created by the clause rather than the rows of the table themselves.
For example, say you were analyzing Bytes Technology sales data:
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 $500k.
Write a SQL query to find the top 3 customers who spent the most on purchases in Bytes Technology in the year 2022. Assume we have two tables: 'customers' and 'purchases.' The 'customers' Table contains customer details such as customer_id, name, etc. The 'purchases' table consists of details about the transactions made by customers like purchase_id, customer_id, purchase_date, and total_amount.
customer_id | name |
---|---|
1001 | John Doe |
1002 | Mary Johnson |
1003 | James Smith |
1004 | Patricia Brown |
1005 | Robert Davis |
purchase_id | customer_id | purchase_date | total_amount |
---|---|---|---|
501 | 1001 | 01/05/2022 | 500.00 |
502 | 1002 | 01/10/2022 | 200.00 |
503 | 1003 | 01/15/2022 | 300.00 |
504 | 1004 | 02/11/2022 | 400.00 |
505 | 1005 | 02/20/2022 | 600.00 |
506 | 1001 | 03/05/2022 | 700.00 |
507 | 1002 | 03/10/2022 | 100.00 |
508 | 1003 | 04/15/2022 | 500.00 |
509 | 1005 | 05/20/2022 | 600.00 |
This query joins the 'customers' and 'purchases' tables on the customer_id. It then sums up the total amount spent by each customer in the year 2022. The result is then ordered in descending order based on the total amount spent, and the top 3 customers with the highest spend are returned.
Because join questions come up frequently during SQL interviews, practice this SQL join question from Spotify:
You are working with Bytes Technology's HR and Data Science teams and are tasked with finding employee performance metrics. Given the following table, write a SQL query to calculate the rounded average performance score, the absolute difference between the maximum and minimum performance scores, and the square root of the sum of all performance scores for each department.
employee_id | department | performance_score |
---|---|---|
101 | 'Sales' | 88 |
102 | 'Sales' | 95 |
103 | 'Marketing' | 82 |
104 | 'Marketing' | 91 |
105 | 'Tech' | 100 |
106 | 'Tech' | 89 |
107 | 'Tech' | 92 |
108 | 'Sales' | 79 |
109 | 'Marketing' | 88 |
This query first groups the dataset by using the clause. Then it computes the following for each group:
To practice a very similar question try this interactive Amazon Average Review Ratings Question which is similar for calculation of average scores or this Alibaba Compressed Mean Question which is similar for calculation and rounding.
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 earlier Bytes Technology SQL interview questions, you should also solve the 200+ DataLemur SQL Interview Questions which come from companies like Google, Uber, and Microsoft.
Each exercise has multiple hints, detailed solutions and crucially, there is an interactive SQL code editor so you can right online code up your query and have it checked.
To prep for the Bytes Technology SQL interview it is also wise to solve interview questions from other tech companies like:
However, if your SQL coding skills are weak, don't worry about going right into solving questions – strengthen your SQL foundations with this free SQL tutorial.
This tutorial covers topics including SUM/AVG window functions and Union vs. UNION ALL – both of these show up routinely in Bytes Technology SQL interviews.
Beyond writing SQL queries, the other topics to prepare for the Bytes Technology Data Science Interview are:
The best way to prepare for Bytes Technology Data Science interviews is by reading Ace the Data Science Interview. The book's got: