Avid Technology employees use SQL daily for analyzing customer usage data for product improvement suggestions, and managing vast databases of media and entertainment industry data. So, it shouldn't surprise you that Avid Technology typically asks SQL questions during interviews for Data Science, Data Engineering and Data Analytics jobs.
To help you ace the Avid Technology SQL interview, here's 11 Avid Technology SQL interview questions in this blog.
In the context of Avid Technology, a software company that specializes in video and audio production technology, a "power user" or "VIP user" can be a customer who purchases their products often and in large volumes. In this query, you are asked to identify the top 3 users who have made the highest number of purchases over the last year. Consider that the purchases' data is stored in the table and the user information is stored in the table.
Here are the sample tables:
user_id | username |
---|---|
1 | user1 |
2 | user2 |
3 | user3 |
4 | user4 |
5 | user5 |
purchase_id | user_id | product_id | purchase_date | quantity |
---|---|---|---|---|
101 | 1 | 501 | 2022-06-01 | 3 |
102 | 2 | 502 | 2022-06-10 | 2 |
103 | 1 | 503 | 2022-07-05 | 1 |
104 | 3 | 501 | 2022-08-12 | 5 |
105 | 2 | 501 | 2022-08-20 | 1 |
106 | 1 | 502 | 2022-09-01 | 2 |
107 | 4 | 504 | 2022-09-10 | 3 |
108 | 5 | 503 | 2022-09-15 | 1 |
109 | 2 | 503 | 2022-10-05 | 1 |
This query first joins the and tables on the . It filters the records from the past year in the table. Then, for each user, it counts the number of purchases they have. It returns the usernames along with their corresponding purchase counts, sorted in descending order, and limits the output to the top 3 users.
To solve a related super-user data analysis question on DataLemur's free online SQL coding environment, try this recently asked Microsoft SQL interview question:
Given a table of Avid Technology employee salary information, write a SQL query to find all employees who earn more than their own boss.
employee_id | name | salary | department_id | manager_id |
---|---|---|---|---|
1 | Emma Thompson | 3800 | 1 | |
2 | Daniel Rodriguez | 2230 | 1 | 10 |
3 | Olivia Smith | 8000 | 1 | 8 |
4 | Noah Johnson | 6800 | 2 | 8 |
5 | Sophia Martinez | 1750 | 1 | 10 |
8 | William Davis | 7000 | 2 | NULL |
10 | James Anderson | 4000 | 1 | NULL |
employee_id | employee_name |
---|---|
3 | Olivia Smith |
This is the output because Olivia Smith earns 8,000, surpassing her manager, William Davis who earns $7,800.
Write a SQL query for this question and run your code right in DataLemur's online SQL environment:
First, we perform a SELF-JOIN where we treat the first table () as the managers' table and the second table () as the employees' table. Then we use a clause to filter the results, ensuring we only get employees whose salaries are higher than their manager's salary.
If the code above is tough, you can find a detailed solution here: Employees Earning More Than Their Boss.
A cross-join, also known as a cartesian 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. This results in a table with a row for every possible combination of rows from the two input tables.
An example of when this might be useful is if you wanted to first make a dataset containing all possible pairs of customers and products data, in order to later build a Machine Learning model to predict the probability of a customer purchasing a particular product.
However, it is important to note that cross-joins can create very large tables, especially if the input tables have a large number of rows. For example, if you had 10,000 customers and 5,000 different product SKUs, the resulting cross-join would have 50 million rows.
As a data analyst at Avid Technology, your task is to analyze the company's product review dataset. Assume you have a table named which stores reviews made by customers for different products every month. The table has columns (unique identifier for the review), (unique identifier of the user who made the review), (date when the review was submitted), (unique identifier for the product), and (star rating given to the product by the user).
The task you need to accomplish is to write a SQL query that calculates the average star rating of each product per month. Treat the as a timestamp that includes both date and time. Extract the month from the and use that in your grouping.
review_id | user_id | submit_date | product_id | stars |
---|---|---|---|---|
1 | 101 | 06/02/2022 10:00:00 | 1001 | 4 |
2 | 202 | 06/05/2022 09:00:00 | 2002 | 5 |
3 | 303 | 06/10/2022 15:00:00 | 1001 | 3 |
4 | 404 | 07/15/2022 20:30:00 | 2002 | 4 |
5 | 505 | 07/18/2022 08:00:00 | 3003 | 5 |
6 | 606 | 07/20/2022 11:45:00 | 1001 | 4 |
month | product_id | avg_stars |
---|---|---|
6 | 1001 | 3.5 |
6 | 2002 | 5.0 |
7 | 1001 | 4.0 |
7 | 2002 | 4.0 |
7 | 3003 | 5.0 |
The query uses the PostgreSQL function to get the month part from the . It then groups the reviews by month and and calculates the average rating for each group using the aggregate function. The clause sorts the result by month and .
For more window function practice, try this Uber SQL problem on DataLemur's online SQL code editor:
The UNIQUE constraint makes sure that all values in a column are distinct. It is often paired with other constraints, like NOT NULL, to ensure that the data follows certain rules.
For example, say you were an analyst on the marketing team at Avid Technology, and had access to a database on marketing campaigns:
In this example, the UNIQUE constraint is applied to the "campaign_name" field to ensure that each campaign has a unique name. This helps to ensure the integrity of the data in the table and prevents errors that could occur if two campaigns had the same name.
Avid Technology is a company that specializes in creating software for multimedia content production. Imagine you work as a data analyst at Avid Technology, and you are asked to understand how users are interacting with their suite of software.
You need to design a database that can hold all these information, and then write a SQL query to identify the most popular software (i.e., software with the most total usage) for any given day.
user_id | user_name |
---|---|
1 | Austin |
2 | Bella |
3 | Charles |
software_id | software_name |
---|---|
1 | Avid Media Composer |
2 | Pro Tools |
3 | Sibelius |
log_id | user_id | software_id | usage_date_time |
---|---|---|---|
1 | 1 | 1 | 2022-08-01 10:01:24 |
2 | 2 | 2 | 2022-08-01 10:25:30 |
3 | 1 | 2 | 2022-08-01 12:15:47 |
4 | 1 | 1 | 2022-08-01 15:00:00 |
5 | 3 | 1 | 2022-08-01 16:45:13 |
6 | 2 | 3 | 2022-08-01 18:30:59 |
7 | 1 | 2 | 2022-08-01 19:05:20 |
8 | 3 | 1 | 2022-08-01 21:10:45 |
Assuming the date provided is '2022-08-01', the PostgreSQL query would be:
This query will first filter our software usage log for entries where the usage date is '2022-08-01'. Then, it will group these entries by software name, and count the number of entries in each group to get the total number of usages for each software. Finally, it orders the results in descending order of total usages and returns the software with the most usages.
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.
For example, say you had a database that stores ad campaign data from Avid Technology's Google Analytics account.
Here's what some constraints could look like:
In this example, the constraint is used to ensure that the "budget" and "cost_per_click" fields have positive values. This helps to ensure that the data in the database is valid and makes sense in the context of ad campaigns.
You can also use the constraint to ensure that data meets other specific conditions. For example, you could use a constraint to ensure that the "start_date" is before the "end_date" for each ad campaign.
You are working as a database analyst for Avid Technology. You are tasked to find all active customers from the United States who aren't on any subscription plan. Active customers are those who have made a purchase in the past 6 months. The company defines 'subscription plans' as any product with a product_id between 50000 to 60000.
Use the and tables for this task.
customer_id | first_name | last_name | country | is_active |
---|---|---|---|---|
10123 | John | Doe | USA | True |
18193 | Jane | Smith | USA | False |
23109 | Alice | Johnson | CAN | True |
order_id | product_id | customer_id | order_date |
---|---|---|---|
21765 | 49999 | 10123 | 2022-05-17 |
21766 | 67000 | 10123 | 2022-06-22 |
28101 | 55000 | 18193 | 2022-01-18 |
28301 | 59000 | 23109 | 2021-12-27 |
In this query, we are joining the and tables on the . Then, we are applying several conditions with the clause. We only want active customers who live in the USA, so we use . To exclude customers on any subscription plan, we use . And to only consider purchases in the last 6 months, we use .
In Avid Technology, each project has a start date and an end date. Can you write a SQL query to find the average duration of projects in days for each project manager?
project_id | start_date | end_date | project_manager_id |
---|---|---|---|
101 | 2019-01-01 | 2019-06-30 | 1 |
102 | 2019-03-01 | 2019-09-30 | 2 |
103 | 2019-02-01 | 2019-07-30 | 1 |
104 | 2019-04-01 | 2020-03-01 | 3 |
105 | 2019-01-01 | 2019-12-31 | 2 |
project_manager_id | avg_project_duration |
---|---|
1 | 210 |
2 | 272 |
3 | 336 |
In this query, we first compute the duration of each project in days using the EXTRACT function. The difference between the end_date and start_date is calculated for each project, and then the day part of this interval is extracted. The AVG function is then used to compute the average duration of projects for each project manager. The output is grouped by project_manager_id.
To practice a very similar question try this interactive Facebook Average Post Hiatus (Part 1) Question which is similar for calculating duration between two dates or this Amazon Server Utilization Time Question which is similar for dealing with start and end times.
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A database index is a way to optimize the performance of a database by reducing the amount of data that needs to be searched to retrieve a record.
There are several types of indexes that can be used in a database:
For a concrete example, say you had a table of Avid Technology customer payments with the following columns: payment_id, customer_id, payment_amount, and payment_date.
Here's what a clustered index on the column would look like:
A clustered index on the column would determine the physical order of the records in the table based on the . This means that the records with the earliest values would be stored together physically in the table, followed by records with later payment_date values.
Having a clustered index on the column can speed up queries that filter or sort the data based on the payment_date, as the records are already physically sorted in the table. For example, if you want to retrieve all the payments made in the month of January, the database can use the clustered index to quickly locate and retrieve the desired records without having to search through the entire table.
Avid Technology is using digital ads to drive sales of its software products. For each ad, they record the number of impressions (i.e., how many times an ad is displayed) and clicks (i.e., when a user clicks on the ad to know more about the product). Given the data of ad impressions and clicks, can you write a SQL query to derive the click-through-rates (CTR) for each ad? Click-through-rate (CTR) is the ratio of users who click on a specific link to the number of total users who viewed the ad, often defined as the number of clicks divided by the number of impressions, and then multiplied by 100 to get a percentage.
ad_id | impressions | clicks |
---|---|---|
101 | 25000 | 3750 |
102 | 18000 | 3600 |
103 | 23000 | 4000 |
104 | 16000 | 2400 |
105 | 20000 | 3000 |
ad_id | CTR |
---|---|
101 | 15.00% |
102 | 20.00% |
103 | 17.39% |
104 | 15.00% |
105 | 15.00% |
You can calculate the click-through-rates with the following query:
This query first converts the counts of clicks and impressions to decimal for accurate division result. Then it calculates the ratio of "clicks" and "impressions", multiplies it by 100 to get the percentage, representing click-through-rates. To get it to two decimal places, the function is used.
To solve a related problem on DataLemur's free online SQL coding environment, try this SQL interview question asked by Facebook:
The best way to prepare for a Avid Technology SQL interview is to practice, practice, practice. In addition to solving the above Avid Technology SQL interview questions, you should also solve the 200+ SQL exercises on DataLemur which come from companies like Google, Microsoft and Silicon Valley startups.
Each problem on DataLemur has hints to guide you, detailed solutions and crucially, there is an interactive SQL code editor so you can easily right in the browser your SQL query and have it graded.
To prep for the Avid Technology SQL interview you can also be wise to practice SQL problems from other tech companies like:
In case your SQL skills are weak, don't worry about going right into solving questions – improve your SQL foundations with this interactive SQL tutorial.
This tutorial covers topics including GROUP BY and window functions – both of these come up often in Avid Technology SQL interviews.
In addition to SQL interview questions, the other types of problems covered in the Avid Technology Data Science Interview include:
I'm sorta biased, but I think the optimal way to study for Avid Technology Data Science interviews is to read the book Ace the Data Science Interview.
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