At Clarivate, SQL does the heavy lifting for analyzing large scientific and patent datasets for insights and managing their massive bibliographic and citation databases. That's why Clarivate almost always asks SQL query questions during interviews for Data Science and Data Engineering positions.
So, to help you prepare for the Clarivate Analytics SQL interview, we'll cover 9 Clarivate SQL interview questions – can you answer each one?
Clarivate is a global leader in providing trusted insights and data analytics. In the perspective of a data analyst at Clarivate, you have been given a task to analyze the data. You are required to calculate the average product rating for each month. Please write a SQL query that gives the monthly average ratings for each product. The query must make use of SQL window functions.
Imagine, you have given a table named . The structure and sample rows of the table is as given below:
review_id | user_id | submit_date | product_id | stars |
---|---|---|---|---|
6171 | 123 | 2022-06-08 00:00:00 | 50001 | 4 |
7802 | 265 | 2022-06-10 00:00:00 | 69852 | 4 |
5293 | 362 | 2022-06-18 00:00:00 | 50001 | 3 |
6352 | 192 | 2022-07-26 00:00:00 | 69852 | 3 |
4517 | 981 | 2022-07-05 00:00:00 | 69852 | 2 |
The output of your query should be presented in a table format as:
mth | product | avg_stars |
---|---|---|
6 | 50001 | 3.50 |
6 | 69852 | 4.00 |
7 | 69852 | 2.50 |
Please ignore any null or missing values
This SQL query uses the aggregate function as a window function to calculate the average ratings () for each product () by each month (). The clause splits the data into different windows (in this case, there is a window for each combination of month and product_id). The clause then orders the data within each window by the rating date. Finally, the function calculates the average rating for each product for each month across all the given data in the table.
For more window function practice, solve this Uber SQL Interview Question on DataLemur's interactive coding environment:
Clarivate is a global leader in providing solutions to accelerate the lifecycle of innovation. You're working with a dataset from their Analytics division related to patent applications. Your task is to design a database schema that captures this patent information, taking into consideration the applicants, the patent classifications and the filing date. Once the tables are designed, write a query to find the top 10 applicants who have filed the most patent applications for a specific year.
These are the details you have about each patent application:
Take into consideration that an application can have multiple applicants and multiple patent classifications.
application_id | filing_date |
---|---|
1 | 2020-01-01 |
2 | 2020-02-02 |
3 | 2021-03-03 |
application_id | applicant_id |
---|---|
1 | 1 |
1 | 2 |
2 | 2 |
3 | 1 |
applicant_id | applicant_name |
---|---|
1 | Applicant 1 |
2 | Applicant 2 |
This query first creates a CTE () to count the number of applications each applicant has for the year 2020. We then use that CTE to join with the table to get the applicant name. Finally, we order by the application count in descending order and limit the result to the top 10 applicants. Note that the query can be adjusted for other years by changing the condition in the WHERE clause.
Joins in SQL allow you to combine data from different tables based on a shared key or set of keys.
Four JOIN types are available in SQL. For an example of each one, say you had sales data exported from Clarivate's Salesforce CRM stored in a PostgreSQL database, and had access to two tables: and .
As a data analyst at Clarivate, you are asked to analyze the table to find out the customers who have subscription and have recently updated their profiles within the last 7 days. You will also have to filter out customers who are flagged as 'Inactive'.
The table has the following schema:
customer_id | subscription_type | last_updated | status | |
---|---|---|---|---|
101 | jane.doe@example.com | Premium | 2022-10-04 | Active |
102 | john.doe@example.com | Basic | 2022-09-28 | Active |
103 | mary.jane@example.com | Premium | 2022-09-27 | Inactive |
104 | ron.john@example.com | Premium | 2022-10-02 | Active |
105 | jill.hill@example.com | Basic | 2022-09-30 | Active |
You can execute the following SQL command to get the required data:
This query would return the and of all customers who have a 'Premium' subscription and whose profiles were updated within the last 7 days. It also removes any customers who are flagged as 'Inactive'. This is done using the SQL , , and operators to combine the required conditions.
An inner join only includes rows from both tables that satisfy the join condition, whereas a full outer join includes all rows from both tables, regardless of whether they match the join condition or not.
For a concrete example, imagine you had two database tables, an advertising campaigns table which had information on Google Ads keywords and how much was bid for each keyword, and a Clarivate sales table, which has data on how many products were sold and which Google Ads keyword drove that sale.
: retrieves rows from both tables where there is a match in the shared key or keys. For example, an INNER JOIN between the table and the table could be performed using the keyword column as the shared key. This would retrieve only the rows where the keyword in the table matches the keyword in the table.
: retrieves all rows from both tables, regardless of whether there is a match in the shared key or keys. If there is no match, values will be returned for the columns of the non-matching table.
As a data analyst at Clarivate(a global leader in providing trusted insights and analytics), you are asked to determine the average daily usage of different research softwares by employees over a week. The company wants this information to understand which softwares are the most utilized and accordingly plan software licenses and trainings.
The table logs daily software usage details. It includes the , , and (number of hours an employee used a software on a particular day).
log_id | employee_id | usage_date | software_id | usage_hours |
---|---|---|---|---|
101 | 458 | 2022-11-14 | 1001 | 2 |
102 | 149 | 2022-11-15 | 2002 | 4 |
103 | 458 | 2022-11-15 | 1001 | 3 |
104 | 786 | 2022-11-16 | 3003 | 1 |
105 | 149 | 2022-11-16 | 2002 | 5 |
106 | 458 | 2022-11-16 | 1001 | 3 |
107 | 786 | 2022-11-17 | 3003 | 1.5 |
108 | 149 | 2022-11-17 | 2002 | 4 |
109 | 458 | 2022-11-18 | 1001 | 2.5 |
The output should have and (average daily usage of a software by all employees over a week).
software_id | avg_daily_usage |
---|---|
1001 | 2.75 |
2002 | 4.33 |
3003 | 1.25 |
To solve this problem, you can use the AVG function on the column and GROUP BY .
This query first filters the records that are within a week (from 15th to 21st Nov 2022), then groups the records by . The AVG function is used to compute the average usage hours for each software. The output is a table consisting of and its average daily usage. To make it more meaningful, you could convert the average daily usage to hours and minutes format or round it to 2 decimal places.
Imagine you are organizing a party and have two database tables: one table of people you want to invite and another list of food items you want to serve.
A cross join would be like inviting every person on your list to the party and serving them every food item on the menu, regardless of whether they like the food or not. So, if you had 10 people on your invite list and 5 food items on the menu, you would generate all 50 different combinations of people and food (10 x 5 = 50).
On the other hand, a natural join would be like inviting only the people who like the food items on the menu (based on doing a inner/left/right/outer JOIN on a common key like ).
Clarivate is a global leader in providing trusted insights and analytics to accelerate the pace of innovation. They have products that cover scientific and academic research, patent intelligence, pharmaceutical and biotech intelligence, etc. Let's consider that they want to assess the usage level of their clients. They would like to know, on average, how many articles each user accesses monthly for their research purpose.
Consider a simplified database table with data about every instance of a user accessing an article. For each access, the table records the , , , and .
access_id | user_id | access_date | article_id |
---|---|---|---|
1001 | 145 | 01/03/2022 | 3001 |
1002 | 98 | 01/04/2022 | 3015 |
1003 | 145 | 01/05/2022 | 3001 |
1004 | 98 | 02/05/2022 | 3015 |
1005 | 112 | 02/08/2022 | 3125 |
1006 | 112 | 02/10/2022 | 3121 |
1007 | 112 | 02/10/2022 | 3125 |
Write a PostgreSQL query that groups by and month of and finds the average number of articles accessed per user for each month.
The above SQL query uses the statement to group the data by and month of . Then it uses to find the number of distinct articles accessed by each user for each month. It will give the insights on the usage level of each user on a monthly basis.
Please note that the needs to be of date or timestamp type to use the function and keyword ensures we're not counting the same article multiple times if it was accessed by the same user in the given month.
You are working as a data analyst for Clarivate. You have been asked by the marketing department to filter customers based on the field. The request is to identify customers having a Yahoo email id.
Your customer records are stored in the table given below:
cust_id | first_name | last_name | |
---|---|---|---|
1001 | John | Doe | john_doe@gmail.com |
1002 | Jane | Smith | jane_smith@yahoo.com |
1003 | Mary | Johnson | mary_johnson@hotmail.com |
1004 | James | Brown | james_brown@yahoo.com |
1005 | Patricia | Williams | patricia_williams@outlook.com |
Please write SQL code to solve this problem.
The SQL query selects all fields from the table where the is like '%@yahoo.com'. This will fetch all records where the ends with '@yahoo.com', thus filtering all customers using a Yahoo email id.
cust_id | first_name | last_name | |
---|---|---|---|
1002 | Jane | Smith | jane_smith@yahoo.com |
1004 | James | Brown | james_brown@yahoo.com |
The key to acing a Clarivate SQL interview is to practice, practice, and then practice some more! In addition to solving the above Clarivate SQL interview questions, you should also solve the 200+ SQL coding questions which come from companies like Netflix, Airbnb, and Amazon.
Each interview question has multiple hints, fully explained answers along with a discussion board to see how others solved it and most importantly, there is an online SQL coding environment so you can easily right in the browser your SQL query and have it graded.
To prep for the Clarivate SQL interview you can also be useful to solve SQL questions from other tech companies like:
But if your SQL skills are weak, don't worry about jumping right into solving questions – strengthen your SQL foundations with this interactive SQL tutorial.
This tutorial covers topics including handling strings and aggreage functions like MIN()/MAX() – both of which show up frequently during Clarivate SQL assessments.
Besides SQL interview questions, the other types of problems to practice for the Clarivate Data Science Interview are:
The best way to prepare for Clarivate Data Science interviews is by reading Ace the Data Science Interview. The book's got: