9 Sprinklr SQL Interview Questions (Updated 2024)
At Sprinklr, SQL is used all the damn time for analyzing social media management and customer insights data. That's why Sprinklr often tests SQL coding questions in interviews for Data Analyst, Data Science, and BI jobs.
Thus, to help you prepare for the Sprinklr SQL interview, we've collected 9 Sprinklr SQL interview questions – can you solve them?
9 Sprinklr SQL Interview Questions
SQL Question 1: Identifying Power Users From Campaign Data
Sprinklr helps businesses manage their customer interactions across many different channels like social media, email, chat, and SMS. An important activity for Sprinklr's customers could be posting high number of campaign messages across multiple channels. These power users, or VIP users, are users who are routinely posting a high number of messages onto one or more channels.
Given a table with columns (int), (int), (int), (varchar), and (date) where is the count of messages posted in a single campaign, write a SQL query that identifies users who have posted a total of 300 or more messages onto a single channel or more in the past month.
Example Input:
| campaign_id | user_id | message_count | channel | post_date |
|---|---|---|---|---|
| 1001 | 1212 | 100 | "social" | 2023-04-22 |
| 1002 | 1234 | 250 | "email" | 2023-04-15 |
| 1003 | 1212 | 200 | "social" | 2023-04-01 |
| 1004 | 1234 | 50 | "chat" | 2023-04-10 |
| 1005 | 1212 | 100 | "chat" | 2023-04-20 |
Answer:
This query selects the and from the table where the is within the past month. It groups the result by both and , then filters for users who have posted a total of 300 or more messages in the past month. The clause is used after to filter the groups that don't meet the criteria.
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:
SQL Question 2: Average User Ratings for each product in a given month
Assume you are working as a data analyst at Sprinklr, a social media management software company. The team would like to understand the rating trends of product usage for each month. Sprinklr has a 'product_reviews' table that stores feedback from users about products with a rating included. Write a SQL query to find the average rating of each product for each month.
Assume we have the following sample data:
Example Input:
| review_id | user_id | submit_date | product_id | stars |
|---|---|---|---|---|
| 6171 | 123 | 2022-06-08 | 50001 | 4 |
| 7802 | 265 | 2022-06-10 | 69852 | 4 |
| 5293 | 362 | 2022-06-18 | 50001 | 3 |
| 6352 | 192 | 2022-07-26 | 69852 | 3 |
| 4517 | 981 | 2022-07-05 | 69852 | 2 |
We would like to find the average rating for each product each month. The table should return the month, product_id, and the average stars.
Answer:
This query works by first extracting the month from the using the function. It then groups by this extracted month and the and calculates the average for each group. It also sorts the results by month and then by the average stars in descending order.
Expected output from this query on the sample data will be:
Example Output:
| month | product_id | avg_stars |
|---|---|---|
| 6 | 50001 | 3.50 |
| 6 | 69852 | 4.00 |
| 7 | 69852 | 2.50 |
Pro Tip: Window functions are a frequent SQL interview topic, so practice every window function problem on DataLemur
SQL Question 3: What are the similarities and difference between relational and NoSQL databases?
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 Sprinklr, it's good to know that companies generally choose to use NoSQL databases:
- when dealing with unstructured or semi-structured data
- when the database needs to be scaled horizontally easily
- when the data is non-relational (like storing social network data which makes more sense in a graph format)
SQL Question 4: Designing a Database for Tracking Customer Interactions
Suppose you are asked to design a database for Sprinklr, a customer experience management platform. The system is intended to track customer interactions on different social media platforms. Key elements of the business problem are:
- Users initiate interactions, which can be tracked on different social media platforms (e.g., Facebook, Instagram, Twitter).
- Interactions include posts, likes, and comments.
- An interaction contains several information including user ID, social media platform, type of interaction (post, like, comment), data of the interaction and its content (in case of a post or a comment).
In this context, design the necessary tables and their relationships. What columns belong to which tables and what database performance considerations would you propose for this specific design?
Example Input:
| user_id | user_name |
|---|---|
| 1 | Alice |
| 2 | Bob |
| 3 | Charlie |
Example Input:
| interaction_id | user_id | platform | interaction_type | interaction_date | content |
|---|---|---|---|---|---|
| 101 | 1 | Post | 01/01/2023 | Happy New Year! | |
| 102 | 2 | Like | 01/01/2023 | NULL | |
| 103 | 1 | Comment | 01/01/2023 | Looking forward to the new year! |
Answer:
A PostgreSQL query that would provide the number of interactions per user across all platforms could look like this:
This query would output an aggregation of the total count of interactions (posts, likes, comments) per user across all social media platforms. For optimization, one might consider adding indexes on the columns in both and tables since this SQL statement performs a JOIN on these columns. Indexing can accelerate the speed of this operation. Additionally, the Interactions table could possibly grow significantly larger than the Users table given that a user can have multiple interactions, so consider partitioning the Interactions table by date to assist with manageability and query performance.
SQL Question 5: Are NULLs handled the same as zero's and blank spaces in SQL?
In SQL, zero's are numerical values which can be used in calculations and comparisons just like any other number. A blank space, also known as an empty string, is a character value and can be used in character manipulation functions and comparisons.
NULLs aren't the same as zero's or blank spaces. NULLs represent unkonwn, missing, or not applicable values. They are not included in calculations and comparisons involving NULL values always result in NULL.
SQL Question 6: Calculate Average Response Time for Each Client
For a company like Sprinklr, which provides customer experience management (CXM) services, it would be important to measure and monitor the average response time for each client. For this question, you need to find the average response time for each client over a given month. Response time is the duration between a client's request (timestamp when the request is made) and the first response from customer service (timestamp of the reply).
Example Input:
| request_id | client_id | request_timestamp | response_timestamp |
|---|---|---|---|
| 4001 | 042 | 2022-09-01 10:00:00 | 2022-09-01 10:10:00 |
| 4002 | 013 | 2022-09-01 11:00:00 | 2022-09-01 11:20:00 |
| 4003 | 042 | 2022-09-02 09:00:00 | 2022-09-02 09:15:00 |
| 4004 | 013 | 2022-09-03 16:00:00 | 2022-09-03 16:10:00 |
| 4005 | 042 | 2022-09-04 08:00:00 | 2022-09-04 08:30:00 |
Example Output:
| month | client_id | avg_response_time_(minutes) |
|---|---|---|
| 9 | 042 | 18.33 |
| 9 | 013 | 15.00 |
Answer:
The SQL query averages the time difference between the request_timestamp and response_timestamp of each client's request. The result is grouped by the month of the request (extracted from request_timestamp) and the client's ID. AVG function calculates the average response time in minutes. The response times are measured in minutes, assuming that the response_timestamp and request_timestamp are of the timestamp type in the PostgreSQL database.
To practice a very similar question try this interactive Facebook Average Post Hiatus (Part 1) Question which is similar for measuring time between two events or this Amazon Average Review Ratings Question which is similar for calculating averages based on a specific category.
SQL Question 7: What would you do to optimize a SQL query that was running slow?
There's several steps you can take to troubleshoot a slow SQL query.
First things first, figure out why the query is slow! You can use and commands in PostgreSQL to identify any performance bottlenecks. After that, you can start changing your query, depending on what the source of the query performance issue is.
Generally, indexes can help speed up queries. Also de-normalizing your tables might help, to remove slow joins.
Lastly, you could always just upgrade your hardware! Time and money wasted improving query performance could just be better spent on mroe powerful database servers!
While this is a very surface-level approach to optimizing SQL query performance, it should do the trick for Data Analyst interviews and Data Science interviews at Sprinklr. Data Engineers should know a bit more about the and before the interview.
SQL Question 8: Filtering Customer Transactions
As a data analyst for Sprinklr, you've received a request to find all customers whose first names start with 'JOH' and have made a transaction worth more than $1000. Using the customers and transactions tables below, how would you go about identifying these customers and their transactions?
Example Input:
| customer_id | first_name | last_name | DOB | |
|---|---|---|---|---|
| 1001 | John | Doe | 12/01/1980 | john.doe@gmail.com |
| 1002 | Johnny | Depp | 09/06/1963 | johnny.depp@gmail.com |
| 1003 | Jonathan | Davids | 10/15/1970 | jonathan.davids@gmail.com |
| 1004 | Jennifer | Lopez | 07/24/1969 | jennifer.lopez@gmail.com |
'transactions' Example Input:
| transaction_id | customer_id | transaction_date | transaction_amount |
|---|---|---|---|
| 001 | 1001 | 01/01/2022 00:00:00 | 1500 |
| 002 | 1002 | 01/02/2022 00:00:00 | 900 |
| 003 | 1003 | 01/03/2022 00:00:00 | 5000 |
| 004 | 1004 | 01/04/2022 00:00:00 | 750 |
Answer:
The SQL query accomplishes the request by joining the tables on the customer_id field. Here, it also uses the LIKE operator to filter the first_names that start with 'JOH' and checks if the transaction_amount is more than $1000.
SQL Question 9: Calculating Product Discrepancies
Sprinklr is interested in analyzing its product sales performance. They would like to identify products where the current sales drastically differs from the average sales of previous months. They are particularly interested in the products where the absolute difference is greater than a certain threshold and also want the results rounded to two decimal places.
To explore this, we'll use two tables: and .
Example input:
| product_id | name | category |
|---|---|---|
| 1 | Product A | Electronics |
| 2 | Product B | Home Deco |
| 3 | Product C | Stationery |
Example input:
| sale_id | product_id | sale_date | units |
|---|---|---|---|
| 101 | 1 | 2022-06-15 | 50 |
| 102 | 2 | 2022-06-12 | 20 |
| 103 | 3 | 2022-06-10 | 30 |
| 104 | 1 | 2022-07-15 | 30 |
| 105 | 2 | 2022-07-17 | 100 |
| 106 | 3 | 2022-07-13 | 35 |
| 107 | 1 | 2022-08-15 | 25 |
| 108 | 2 | 2022-08-17 | 90 |
| 109 | 3 | 2022-08-01 | 40 |
The threshold for the absolute difference is set to 15 units.
Answer:
The query first calculates the average sales of the preceding months for each product, then it calculates the sales of the current month. Finally, it calculates the absolute difference between these two and rounds it to two decimal places. It includes only the products where the absolute difference is greater than 15 units.
To practice a very similar question try this interactive Wayfair Y-on-Y Growth Rate Question which is similar for analysing sales performance using window functions or this Amazon Highest-Grossing Items Question which is similar for identifying top performing products.
How To Prepare for the Sprinklr 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.
In addition to solving the earlier Sprinklr SQL interview questions, you should also solve the 200+ SQL coding questions which come from companies like FAANG and tech startups.
Each interview question has multiple hints, full answers and best of all, there is an online SQL coding environment so you can instantly run your query and have it checked.
To prep for the Sprinklr SQL interview you can also be useful to solve interview questions from other tech companies like:
But if your SQL skills are weak, don't worry about going right into solving questions – go learn SQL with this DataLemur SQL tutorial.
This tutorial covers SQL topics like aggregate functions like SUM()/COUNT()/AVG() and manipulating date/time data – both of these pop up frequently during SQL job interviews at Sprinklr.
Sprinklr Data Science Interview Tips
What Do Sprinklr Data Science Interviews Cover?
Besides SQL interview questions, the other types of problems to prepare for the Sprinklr Data Science Interview are:
- Statistics and AB Testing Questions
- Python Pandas or R Coding Questions
- Analytics and Product-Metrics Questions
- ML Interview Questions
- Behavioral Based Interview Questions
How To Prepare for Sprinklr Data Science Interviews?
The best way to prepare for Sprinklr Data Science interviews is by reading Ace the Data Science Interview. The book's got:
- 201 Interview Questions from tech companies like Google & Microsoft
- A Refresher covering SQL, Product-Sense & ML
- Amazing Reviews (900+ 5-star reviews on Amazon)