8 Open Text SQL Interview Questions (Updated 2024)

Updated on

March 1, 2024

At OpenText, SQL crucial for analyzing and manipulating large data sets related to enterprise information management, and they even have their own embedded database product called Gupta SQLBase. That's why Open Text frequently asks SQL problems during interviews for Data Analytics, Data Science, and Data Engineering jobs.

As such, to help you prepare for the Open Text SQL interview, we've collected 8 OpenText SQL interview questions – able to answer them all?

8 OpenText SQL Interview Questions

SQL Interview Question 1: Identifying Power Users at Open Text

Open Text Corporation provides a platform that allows individuals and organizations to store, locate, and manage information. For this question, let's say that Open Text identifies power users as those who have uploaded a significantly larger number of files compared to other users.

Question Description:

Given the following two tables, one is and the other is , write a SQL query to identify all power users who have uploaded more than 10 times the average number of files uploaded per user.

Example Input:
Example Input:


In this query, a subquery is used to calculate the average number of files uploaded by each user (avg_uploads). Then, we calculate the total number of files uploaded by each user with a group by user id and name. We then filter out the users who have uploaded more than 10 times the average number of files uploaded using the HAVING clause.

To practice a related super-user data analysis question on DataLemur's free online SQL code editor, try this recently asked Microsoft SQL interview question: Microsoft SQL Interview Question: Teams Super User

SQL Question 2: Calculate Running Total of Document Downloads

Assume you work at OpenText, a company that focuses on Enterprise Information Management (EIM). One of your responsibilities is to analyze the number of document downloads on monthly basis and calculate the cumulative/downloads running total for each document.

The company has a database table named "downloads" with documentation about each download event. Each row represents a document download by a user and contains columns for:

  1. download_id: A unique identifier for the download event.
  2. document_id: A unique identifier for the document.
  3. user_id: A unique identifier for the user.
  4. download_date: The date on which the document was downloaded.

Based on the given "downloads" table, write a SQL query to calculate the cumulative monthly downloads for each document. The output should be in descending order of the month and include:

  1. Month Year: The month and year of the downloads.
  2. document_id: The unique identifier for the document.
  3. cumulative_downloads: The cumulative number of times the document was downloaded up to end of the month.
Example Input:
Example Output:
Month Yeardocument_idcumulative_downloads


In this query, we use the window function named with the clause to compute the cumulative number of downloads for each document. The window is partitioned by the and ordered by the year and month of the , giving us the running total of downloads. We order the result by in descending order to see the most current downloads first.

To practice a similar window function interview problem which uses RANK() on DataLemur's free online SQL code editor, try this Amazon BI Engineer interview question: Amazon Window Function SQL Interview Problem

SQL Question 3: What's the difference between a unique and non-unique index?

Unique indexes help ensure that there are no duplicate key values in a table, maintaining data integrity. They enforce uniqueness whenever keys are added or changed within the index.

Non-unique indexes on the other hand, are used to improve query performance by maintaining a sorted order of frequently used data values, but they do not enforce constraints on the associated table.

Unique Indexes are blazing fast. Non unique indexes can improve query performance, but they are often slower because of their non-unique nature.

OpenText SQL Interview Questions

SQL Question 4: Average Rating of a Product

Given the table, write an SQL query to find the average rating for each product on a monthly basis. The result set should contain the month (0-12), product_id, and average ratings (rounded to 2 decimal places) for each month.

Example Input:
617112306/08/2022 00:00:00500014
780226506/10/2022 00:00:00698524
529336206/18/2022 00:00:00500013
635219207/26/2022 00:00:00698523
451798107/05/2022 00:00:00698522
Example Output:


This PostgreSQL query uses the function to get the month of the . The query then groups by month and and calculates the average of the for each group. The result is ordered by month and product. It's important to note that the months are represented as numbers (e.g., 6 for June, 7 for July) and the average of stars is rounded to 2 decimal points for better readability.

SQL Question 5: Name the different types of joins in SQL. What does each one do?

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 Open Text's Salesforce CRM stored in a PostgreSQL database, and had access to two tables: and .

  • : retrieves rows from both tables where there is a match in the shared key or keys. For example, an INNER JOIN between the Sales table and the Customers table would retrieve only the rows where the customer_id in the Sales table matches the customer_id in the table.
  • : retrieves all rows from the left table (in this case, the sales table) and any matching rows from the right table (the table). If there is no match in the right table, NULL values will be returned for the right table's columns.
  • : retrieves all rows from the right table (in this case, the customers table) and any matching rows from the left table (the sales table). If there is no match in the left table, NULL values will be returned for the left table's columns.
  • : retrieves all rows from both tables, regardless of whether there is a match in the shared key or keys. If there is no match, NULL values will be returned for the columns of the non-matching table.

SQL Question 6: Calculate Stats on User Webpage Visits

You work at Open Text and have a website for which you keep logs of user visits. Each visit has a timestamp, user ID, page ID, and the duration of the visit in seconds. You'd like to understand the average and variance visit duration for each webpage in the last 7 days, and round the results to 2 decimals. Variance is a statistical measurement of the spread between numbers in a data set.

Assume today's date is '2022-10-01'.

Here are your data tables:

Example Input:
100012022-09-25 12:30:0030001300
100122022-09-25 13:00:0030001250
100232022-09-26 15:00:0030001320
100342022-09-27 08:00:0030002100
100412022-09-28 09:30:0030002120
100522022-09-29 10:00:0030001270
100632022-09-30 11:30:0030002110
100742022-09-30 12:00:0030002130
100812022-10-01 12:00:0030001280
Example Output:


This is a PostgreSQL query to calculate average and variance visit duration for each webpage in the last 7 days, rounding results to 2 decimals. The function rounds the results to 2 decimals, the function calculates the average duration and the function calculates the sample variance. The WHERE clause filters out records that are older than 7 days. We group by to get separate results for each webpage.

To practice a very similar question try this interactive Snapchat Sending vs. Opening Snaps Question which is similar for requiring you to calculate aggregate metrics like time spend on actions and Alibaba Compressed Mean Question which is similar for needing to compute averages with rounding and precision.

SQL Question 7: In the context of a database transaction, what does ACID mean?

To ensure the reliability and integrity of data, a database management system (DBMS) strives to maintain the ACID properties: Atomicity, Consistency, Isolation, and Durability. To illustrate these concepts, consider the following examples of how ACID properties apply to banking transactions:

  1. Atomicity: A transaction is either completed in full or not at all. For example, if a customer is transferring money between accounts, the transaction should either transfer the entire amount or none at all.
  2. Consistency: A transaction is only allowed to complete if it follows all rules and constraints within the database. For example, if a customer is withdrawing money from an account, the transaction should only be allowed to proceed if there are sufficient funds available. Otherwise, the transaction is rejected.
  3. Isolation: Concurrent transactions are kept separate from each other, so that the changes made by one transaction cannot be seen by another transaction until the first one is complete. This helps prevent conflicts, such as two customers attempting to withdraw money from the same account at the same time.
  4. Durability: Once a transaction has been committed and completed, the changes made by the transaction are permanently stored in the database and will not be lost even if the database or system crashes. For example, if a customer makes a deposit, the transaction should be durable so that the deposit is not lost in the event of a system failure.

SQL Question 8: Calculate the Average Daily Document Generation

As a data analyst at Open Text, a global leader in Enterprise Information Management (EIM), you are required to review the company's document generation system. The company generates numerous documents each day for various business processes. You have access to a 'documents' table which lists every document created, along with its creation time.

Can you write an SQL query to calculate the average number of documents generated each day? Please consider only documents from the last 90 days for this calculation.

Example Input:
915110/01/2022 09:45:00
763210/01/2022 10:22:00
827310/02/2022 13:56:00
862510/02/2022 14:03:00
982110/03/2022 12:35:00
941110/03/2022 15:10:00
Expected Output:


This SQL query first groups all documents by the day on which they are created. Then it calculates the count of documents per day. Then the outer query calculates the average of these daily counts. The clause in the inner query ensures that only documents from the last 90 days are considered.

How To Prepare for the Open Text 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 Open Text SQL interview questions, you should also solve the 200+ SQL exercises on DataLemur which come from companies like Microsoft, Google, Amazon, and tech startups. DataLemur SQL and Data Science Interview Questions

Each exercise has multiple hints, detailed solutions and crucially, there is an online SQL coding environment so you can instantly run your SQL query answer and have it graded.

To prep for the Open Text SQL interview it is also a great idea to solve interview questions from other tech companies like:

However, if your SQL query skills are weak, don't worry about diving straight into solving questions – go learn SQL with this DataLemur SQL tutorial.

SQL tutorial for Data Analytics

This tutorial covers things like handling missing data (NULLs) and manipulating date/time data – both of these pop up routinely during Open Text SQL assessments.

OpenText Data Science Interview Tips

What Do Open Text Data Science Interviews Cover?

Beyond writing SQL queries, the other types of problems to practice for the Open Text Data Science Interview are:

Open Text Data Scientist

How To Prepare for Open Text Data Science Interviews?

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