9 NICE SQL Interview Questions (Updated 2024)
At NICE, SQL is used quite frequently for extracting and analyzing heaps of customer experience data. Because of this, NICE LOVES to ask SQL coding questions in interviews for Data Science, Analytics, and & Data Engineering jobs.
To help you practice for the NICE SQL interview, we've curated 9 NICE SQL interview questions – can you solve them?
9 NICE SQL Interview Questions
SQL Question 1: Calculate Monthly Average Ratings per Product
In the table, there is data for product reviews from users, including the date of the review submission and the number of stars given. Write a SQL query to calculate the average stars per product for each month. Here, you have to use SQL window function to perform this analysis.
Example Input Data:
| review_id | user_id | submit_date | product_id | star_rating |
|---|---|---|---|---|
| 1 | 23 | 2020-01-06 | 1000 | 4 |
| 2 | 34 | 2020-01-30 | 2000 | 5 |
| 3 | 23 | 2020-02-01 | 1000 | 3 |
| 4 | 56 | 2020-02-05 | 1000 | 2 |
| 5 | 12 | 2020-03-01 | 2000 | 4 |
Example Output:
| month | year | product_id | avg_rating |
|---|---|---|---|
| 1 | 2020 | 1000 | 4.0 |
| 1 | 2020 | 2000 | 5.0 |
| 2 | 2020 | 1000 | 2.5 |
| 3 | 2020 | 2000 | 4.0 |
Answer:
Here is the SQL query to solve it:
This query first extracts the month and year from the field. The function is used with the clause (window function) to calculate the average star rating for each product for each month and year. The clause is used to create a window of rows with the same product ID, month, and year.
Pro Tip: Window functions are a popular SQL interview topic, so practice every window function problem on DataLemur
SQL Question 2: Analyzing User Activity
NICE is a company that offers a diverse array of digital products to its customers. In order to understand customer behavior and user engagement, they record the user login activities.
They want to understand the peak login time for their users on their platform. For this purpose, they want to perform an analysis on hourly basis to identify the hour of the day when most logins occur.
Given the following example of a table, write an SQL query that will return the hour of the day with the highest total user logins.
Example Input:
| activity_id | user_id | login_time |
|---|---|---|
| 101 | 564 | 08/07/2022 07:45:00 |
| 102 | 340 | 08/07/2022 12:30:00 |
| 103 | 123 | 08/07/2022 13:00:00 |
| 104 | 789 | 08/07/2022 12:45:00 |
| 105 | 456 | 08/07/2022 19:00:00 |
| 106 | 890 | 08/07/2022 19:30:00 |
Answer:
This query breaks down the login times to the hour, and counts the number of logins for each hour. It then sorts the resulting counts in descending order and returns the hour with the most logins. Because of the , only the hour with the most logins will be returned.
SQL Question 3: Can you explain what a cross-join is and the purpose of using them?
A cross-join, also known as a cartesian join, is a JOIN that produces the cross-product of two tables. In a cross-join, each row from the first table is matched with every row from the second table, resulting in a new table with a row for each possible combination of rows from the two input tables.
Let's say you were building a Machine Learning model that attempts to score the probability of a customer purchasing a NICE product. Before working in Pandas and Tensorflow, you might want to do some Exploratory Data Analysis (EDA) in SQL, and generate all pairs of customers and NICE products.
Here's a cross-join query you could run:
Cross-joins are useful for generating all possible combinations, but they can also create huge tables if you're not careful. For instance, if you had 10,000 potential customers and NICE had 500 different product SKUs, the resulting cross-join would have 5 million rows!
SQL Question 4: Filter Customer Records Based on Boolean Conditions
NICE is an organization that aids businesses in delivering better customer experiences. In their customer records database, they store a significant amount of information about each customer, including , , , , , and .
Assume your job as a data analyst is to filter out customers with specific conditions that are pertinent to NICE's business operations. Write a SQL query to filter customers who:
- Signed up after 01/01/2021,
- Are on the "Basic" plan, and
- Their first name starts with “A”.
Example Input:
| customer_id | first_name | last_name | signup_date | plan_type | |
|---|---|---|---|---|---|
| 6171 | Alex | Smith | alexsmith@mail.com | 02/02/2021 | Basic |
| 7802 | Benjamin | Parker | benjaminparker@mail.com | 01/03/2021 | Premium |
| 5293 | Alice | Williams | alicewilliams@mail.com | 03/04/2021 | Basic |
| 6352 | Aaron | Johnson | aaronjohnson@mail.com | 04/04/2020 | Basic |
| 4517 | Emma | Brown | emmabrown@mail.com | 05/05/2021 | Premium |
Expected Output:
| customer_id | first_name | last_name | signup_date | plan_type | |
|---|---|---|---|---|---|
| 6171 | Alex | Smith | alexsmith@mail.com | 02/02/2021 | Basic |
| 5293 | Alice | Williams | alicewilliams@mail.com | 03/04/2021 | Basic |
Answer:
The above SQL query filters the 'customer' table based on the conditions specified. The clause filters rows where the is after '2021-01-01'. Then, the operator is used to add more conditions to the clause: should be 'Basic' and the should start with 'A'. The operator with a pattern 'A%' is used to check this. If all these conditions are satisfied, the row is included in the output.
SQL Question 5: How do you identify records in one table that are not present in a second table?
To find records in one table that aren't in another, you can use a and check for values in the right-side table.
Here is an example using two tables, NICE employees and NICE managers:
This will return all rows from NICE employees where there is no matching row in managers based on the column.
You can also use the operator in PostgreSQL and Microsoft SQL Server to return the records that are in the first table but not in the second. Here is an example:
This will retrieve all rows from employees that do not appear in managers. The operator works by retreivingthe rows that are returned by the first query, but not by the second.
Please note that is not supported by all DBMS systems, such as MySQL and Oracle (however, you can use the operator to achieve a similar outcome).
SQL Question 6: Average Handling Time Per Call Center Agent
At NICE, a company known for its software solutions for contact centers, one relevant quantity that can be calculated using the AVG function is the average handling time of calls per call center agent. Essentially, you are asked to find the average duration of calls each call center agent handles.
This metric is crucial as it gives insights into the efficiency and effectiveness of each call center agent and can be used for performance appraisal and resource allocation. Using the AVG function in SQL, we can easily compute this metric given we have data in consideration.
Let's assume we have the following tables:
Example Input:
| call_id | agent_id | start_time | end_time |
|---|---|---|---|
| 3286 | 426 | 08/15/2022 11:03:20 | 08/15/2022 11:13:00 |
| 3892 | 671 | 08/15/2022 11:10:45 | 08/15/2022 11:25:00 |
| 5286 | 426 | 08/15/2022 11:15:10 | 08/15/2022 11:27:00 |
| 7394 | 671 | 08/15/2022 11:30:30 | 08/15/2022 11:40:00 |
| 6744 | 426 | 08/15/2022 11:33:02 | 08/15/2022 11:40:00 |
Answer:
In the above SQL, we are extracting the time difference (in seconds) between the end_time and start_time of each call using . We divide the result by 60 to get the duration in minutes. The function is then used to find the average call duration for each agent.
To practice a very similar question try this interactive Microsoft Teams Power Users Question which is similar for performing calculations on user activities or this Amazon Average Review Ratings Question which is similar for calculating averages pertaining to user actions.
SQL Question 7: Can you explain the distinction between a left and right join?
A join in SQL allows you to retrieve data from multiple tables and combine it into a single set of results.
To demonstrate the difference between left vs. right join, 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 sales table, which has data on how many products were sold and which Google Ads keyword drove that sale.
: A LEFT JOIN retrieves all rows from the left table (in this case, the table) and any matching rows from the right table (the Sales table). If there is no match in the right table, values will be returned for the right table's columns.
: A RIGHT JOIN retrieves all rows from the right table (in this case, the Sales table) and any matching rows from the left table (the table). If there is no match in the left table, values will be returned for the left table's columns.
SQL Question 8: Average Call Quality Scores by Service Representatives
Suppose NICE is a company that offers call center services. You have a table called where each row contains information about the customer service representative's (rep_id) score for a call.
The score is based on various parameters like the representative's efficiency, politeness, problem-solving ability, etc., and ranges from 1 (worst) to 5 (best). Each call is timestamped at the time it ends.
Your task is to write a SQL query to find the average score for each rep_id for the month of June 2022.
Example Input:
| call_id | rep_id | end_time | score |
|---|---|---|---|
| 1001 | 1 | 06/01/2022 14:25:00 | 4.5 |
| 1002 | 2 | 06/02/2022 18:30:00 | 3.5 |
| 1003 | 1 | 06/05/2022 10:12:00 | 4.0 |
| 1004 | 2 | 06/10/2022 13:00:00 | 4.5 |
| 1005 | 1 | 07/01/2022 12:25:00 | 5.0 |
Example Output:
| rep_id | avg_score |
|---|---|
| 1 | 4.25 |
| 2 | 4.00 |
Answer:
In this query, we first filter out the rows corresponding to the month of June of the year 2022. Then, for each service representative (rep_id), we calculate the average of their call scores.
SQL Question 9: Calculate Product Sale Statistics
Given a table that keeps track of the number of each product sold in a certain month, along with its selling price. Write a SQL query to calculate the total sales, average price, and total number of units sold for each product. Use mathematical functions and arithmetic operations where appropriate.
Example Input:
| sale_id | product_id | month | units_sold | price_per_unit |
|---|---|---|---|---|
| 1 | 100 | 01/2022 | 100 | 20 |
| 2 | 100 | 02/2022 | 150 | 18 |
| 3 | 101 | 01/2022 | 80 | 30 |
| 4 | 102 | 01/2022 | 50 | 60 |
| 5 | 101 | 03/2022 | 60 | 25 |
Example Output:
| product_id | total_sales | average_price | total_units_sold |
|---|---|---|---|
| 100 | 4700 | 19 | 250 |
| 101 | 3400 | 27.5 | 140 |
| 102 | 3000 | 60 | 50 |
Answer:
This query calculates the total sales by multiplying each unit sold with their respective price per unit and summing them up. The average price is calculated by averaging the price per unit and rounding it to two decimal places. Finally, the sum of all units sold is calculated. The results are grouped by the product_id.
To practice a very similar question try this interactive Amazon Highest-Grossing Items Question which is similar for calculating statistics on product sales or this Wayfair Y-on-Y Growth Rate Question which is similar for using mathematical functions on sales data.
Preparing For The NICE SQL Interview
Assuming that you've already got basic SQL skills, the next best tip we have to prepare for the NICE SQL interview is to solve as many practice SQL interview questions as you can!
Beyond just solving the above NICE SQL interview questions, you should also solve the 200+ SQL questions from real Data Science & Analytics interviews which come from companies like Netflix, Airbnb, and Amazon.
Each interview question has multiple hints, detailed solutions and crucially, there's an interactive coding environment so you can right in the browser run your query and have it checked.
To prep for the NICE SQL interview it is also useful to solve SQL problems from other tech companies like:
But if your SQL skills are weak, forget about going right into solving questions – improve your SQL foundations with this free SQL tutorial.
This tutorial covers SQL topics like advantages of CTEs vs. subqueries and aggregate functions – both of these come up frequently during NICE SQL interviews.
NICE Data Science Interview Tips
What Do NICE Data Science Interviews Cover?
In addition to SQL interview questions, the other question categories tested in the NICE Data Science Interview are:
- Statistics & Probability Questions
- Python or R Coding Questions
- Product Data Science Interview Questions
- Machine Learning Questions
- Resume-Based Behavioral Questions
How To Prepare for NICE Data Science Interviews?
To prepare for NICE Data Science interviews read the book Ace the Data Science Interview because it's got:
- 201 interview questions taken from FAANG (FB, Apple, Amazon, Netflix, Google)
- a crash course covering Stats, ML, & Data Case Studies
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