At MicroStrategy, SQL crucial for extracting and analyzing data from vast databases for business intelligence solutions, and managing data integration from multiple sources for reporting purposes. Because of this, MicroStrategy almost always evaluates jobseekers on SQL coding questions during interviews for Data Science, Analytics, and & Data Engineering jobs.
So, to help you practice for the MicroStrategy SQL interview, we've collected 10 MicroStrategy SQL interview questions – can you solve them?
Within MicroStrategy, you are required to analyze the reviews information in order to understand product performance. Write a SQL query to calculate the monthly average review rating for each product.
Here is a sample input provided in the table:
review_id | user_id | submit_date | product_id | stars |
---|---|---|---|---|
6171 | 123 | 2021-06-08 00:00:00 | 50001 | 4 |
7802 | 265 | 2021-06-10 00:00:00 | 69852 | 4 |
5293 | 362 | 2021-06-18 00:00:00 | 50001 | 3 |
6352 | 192 | 2021-07-26 00:00:00 | 69852 | 3 |
4517 | 981 | 2021-07-05 00:00:00 | 69852 | 2 |
We expect the output to show the , and (average of review stars) of each month:
mth | product | avg_stars |
---|---|---|
6 | 50001 | 3.50 |
6 | 69852 | 4.00 |
7 | 69852 | 2.50 |
In this instance, we don't actually need to use window functions to solve the problem.
Here is a PostgreSQL query to solve the problem:
This SQL query groups the data by month (extracted using the EXTRACT function) and product_id. It then calculates the average review stars for each group using the AVG function, and rounds the results to 2 decimal places.
To solve a similar window function interview problem which uses RANK() on DataLemur's free interactive coding environment, try this Amazon BI Engineer interview question:
MicroStrategy is a company that specializes in business intelligence, mobile software, and cloud-based services. You are provided with two tables, and . The table contains information about the various software purchases made by customers, and the table contains information about the usage statistics of these purchased software.
Your task is to write a query that will help MicroStrategy understand the most frequently used software in terms of total time spent among all the users for every purchased software to improve their customer engagement efforts. This query should return the software name, total times purchased and total time spent on the software across all purchases.
Here are the schemas for the and tables:
purchase_id | customer_id | purchase_date | software_name |
---|---|---|---|
1 | 100 | 2022-04-01 | MicroStrategy for Mobile |
2 | 200 | 2022-05-01 | MicroStrategy HyperIntelligence |
3 | 300 | 2022-06-01 | MicroStrategy Cloud |
4 | 100 | 2022-07-01 | MicroStrategy for Mobile |
5 | 400 | 2022-07-02 | MicroStrategy Cloud |
usage_id | customer_id | software_name | usage_time(hours) |
---|---|---|---|
1 | 100 | MicroStrategy for Mobile | 5 |
2 | 200 | MicroStrategy HyperIntelligence | 8 |
3 | 300 | MicroStrategy Cloud | 6 |
4 | 100 | MicroStrategy for Mobile | 7 |
5 | 400 | MicroStrategy Cloud | 9 |
This query works by first joining the and tables on the and columns. This ensures that we are looking at the usage time for each purchased software per customer. It then groups the results by and computes the total number of times each software is purchased () and the total usage time () across all purchases of each software. The results are sorted in descending order of to get the most frequently used software at the top.
The COALESCE() function can be used to replace NULL values with a specified value. For instance, if you are a Data Analyst at a company and are working on a customer analytics project to identify the most engaged customers, you may have access to the following data in the table:
customer_id | email_engagement | sms_engagement |
---|---|---|
101 | moderately_active | not_opted_in |
201 | un-subscribed | NULL |
301 | NULL | not_opted_in |
401 | not_active | very_active |
501 | very_active | very_active |
601 | NULL | NULL |
Before you could procede with your analysis, you would need to remove the NULLs, and replace them with the default value for email engagement (not_active), and the default sms_engagement level (not_opted_in).
To do this, you'd run the following query:
This would get you the following output:
customer_id | email_engagement | sms_engagement |
---|---|---|
101 | moderately_active | not_opted_in |
102 | un-subscribed | not_opted_in |
103 | not_active | not_opted_in |
104 | not_active | very_active |
105 | very_active | very_active |
106 | not_active | not_opted_in |
Imagine you work at MicroStrategy and you're tasked with better understanding users' behaviour. To specifically understand how different services are used, you want to compute the average usage time for each service in the company's portfolio. A user logs in to use a service and logs out when finished, capturing a timestamp of both events. Can you write an SQL query that calculates the average time spent on each service by all users?
usage_id | user_id | service_id | login_time | logout_time |
---|---|---|---|---|
100 | 1 | 2000 | 2022-08-02 08:00:00 | 2022-08-02 08:30:00 |
101 | 2 | 2000 | 2022-08-02 09:00:00 | 2022-08-02 09:30:00 |
102 | 3 | 2001 | 2022-08-02 10:00:00 | 2022-08-02 11:00:00 |
103 | 4 | 2001 | 2022-08-02 11:30:00 | 2022-08-02 12:00:00 |
104 | 5 | 2002 | 2022-08-02 12:30:00 | 2022-08-02 13:00:00 |
service_id | avg_usage_time_min |
---|---|
2000 | 30.0 |
2001 | 45.0 |
2002 | 30.0 |
This query calculates the time difference between and in minutes for each record in the table. It then groups all these time differences by and computes the average. The result is the average usage time in minutes for each service.
To practice a very similar question try this interactive Snapchat Sending vs. Opening Snaps Question which is similar for calculating the time spent on activities or this Amazon Server Utilization Time Question which is similar for computing the total time of usage.
{#Question-5}
A UNIQUE constraint ensures that all values in a column are different. This provides uniqueness for the column(s) and helps identify each row uniquely. Unlike primary key, there can be multiple unique constraints defined per table.
In MicroStrategy, we have a product review system where customers can rate our products. We would like to calculate the average rating for each product for each month. Provide a list of products with their corresponding average ratings and the months when they were reviewed.
review_id | user_id | submit_date | product_id | stars |
---|---|---|---|---|
101 | 9900 | 2022-09-02 | 111 | 3 |
102 | 8800 | 2022-09-04 | 111 | 5 |
103 | 7700 | 2022-09-05 | 222 | 4 |
104 | 6600 | 2022-09-10 | 222 | 2 |
105 | 5500 | 2022-10-13 | 111 | 2 |
106 | 4400 | 2022-10-15 | 222 | 4 |
month | product_id | avg_stars |
---|---|---|
9 | 111 | 4.00 |
9 | 222 | 3.00 |
10 | 111 | 2.00 |
10 | 222 | 4.00 |
This SQL query extracts the month from the submission date and groups the data by month and product_id. It then calculates the average rating for each product for each month using the AVG() function.
While both types of databases are used to store data (obviously), there's some key differences in how they store and organize data.
Relational databases try to represent the world into neat little tables, with rows and columns. Non-relational (NoSQL) databases use a variety of data models to represent data, including document, key-value, columnar, and graph storage formats.
While the exact types of NoSQL databases is beyond the scope of a Data Analyst and Data Scientist SQL interview at MicroStrategy, it's good to know that companies generally choose to use NoSQL databases:
Your task is to analyze MicroStrategy's weblogs, which records each user's interactions with the different versions of MicroStrategy's application. Your goal is to write a SQL query that retrieves the total number of user interactions for each version of the MicroStrategy application. Join this with the table to provide the context for each of these application versions.
log_id | user_id | log_time | app_version_id |
---|---|---|---|
101 | 6231 | 06/28/2021 12:01:00 | V1.0 |
102 | 8546 | 07/01/2021 14:23:00 | V1.0 |
103 | 6231 | 06/28/2021 12:03:00 | V1.0 |
104 | 3901 | 07/03/2021 09:10:00 | V2.0 |
105 | 7235 | 07/05/2021 13:15:00 | V2.0 |
app_version_id | release_date | retired_date |
---|---|---|
V1.0 | 01/01/2021 00:00:00 | 12/31/2021 23:59:00 |
V2.0 | 01/01/2022 00:00:00 | NULL |
This query joins the table and the table on the field . The statement groups the data according to application version. The function counts the number of logs (user interactions) per application version, which represents the total traffic. Finally, the results are ordered in descending order, so the versions with the highest traffic are listed first.
Since joins come up so often during SQL interviews, try this interactive Snapchat Join SQL question:
In this question, you are an analyst at MicroStrategy. You are dealing with the 'sales' table which stores the data of all the sales transactions. The table includes information about the product, the category it belongs to, the selling price, and cost price of each product.
The task is to write a SQL query to find the total sales and average profit margin for each product category for the year 2022. The profit margin for each item is calculated as and it should be rounded to two decimal places. Report the total sales and average profit margin for each category in the descending order of total sales.
transaction_id | product_id | category | selling_price | cost_price | transaction_date |
---|---|---|---|---|---|
1 | 1001 | Electronics | 599 | 400 | 01/15/2022 |
2 | 2002 | Apparel | 80 | 60 | 01/20/2022 |
3 | 1002 | Electronics | 1200 | 1000 | 04/02/2022 |
4 | 2001 | Apparel | 300 | 200 | 05/11/2022 |
5 | 1003 | Electronics | 400 | 300 | 05/20/2022 |
6 | 3001 | Books | 20 | 15 | 06/13/2022 |
7 | 3002 | Books | 15 | 10 | 07/01/2022 |
category | total_sales | avg_profit_margin |
---|---|---|
Electronics | 2199 | 0.25 |
Apparel | 380 | 0.32 |
Books | 35 | 0.27 |
The following is a PostgreSQL query that will solve the problem:
This query first filters the data for the year 2022. It uses the SUM() function to calculate total sales and AVG() function to calculate average profit margin for each category. The ROUND() function is used to round off the profit margin to 2 decimal places.
To practice a very similar question try this interactive Amazon Highest-Grossing Items Question which is similar for needing to aggregate sales data by product category or this Wayfair Y-on-Y Growth Rate Question which is similar for focusing on yearly financial analysis.
Some similarities between unique and non-unique indexes include:
Some differences between unique and non-unique indexes include:
The key to acing a MicroStrategy SQL interview is to practice, practice, and then practice some more! Beyond just solving the above MicroStrategy SQL interview questions, you should also solve the 200+ SQL Interview Questions on DataLemur which come from companies like Microsoft, Google, Amazon, and tech startups.
Each interview question has multiple hints, step-by-step solutions and crucially, there's an online SQL code editor so you can right in the browser run your SQL query and have it executed.
To prep for the MicroStrategy SQL interview you can also be wise to solve SQL questions from other tech companies like:
However, if your SQL foundations are weak, don't worry about going right into solving questions – strengthen your SQL foundations with this free SQL for Data Analytics course.
This tutorial covers things like sorting results with ORDER BY and CASE/WHEN statements – both of these come up frequently during SQL job interviews at MicroStrategy.
In addition to SQL interview questions, the other types of questions to prepare for the MicroStrategy Data Science Interview are:
To prepare for MicroStrategy Data Science interviews read the book Ace the Data Science Interview because it's got: