9 Five9 SQL Interview Questions (Updated 2024)
At Five9, SQL is used frequently for analyzing vast customer interaction data for insights, and optimizing the performance of their cloud-based contact center software. That's why Five9 LOVES to ask SQL questions in interviews for Data Science and Data Engineering positions.
Thus, to help you prepare for the Five9 SQL interview, we'll cover 9 Five9 SQL interview questions – able to answer them all?
9 Five9 SQL Interview Questions
SQL Question 1: Calculate the average processing time for each agent
Five9 is a cloud-based call center software. For this question, let's suppose you're working with data about call processing times from Five9. The data consists of columns indicating the agent who took the call, the time the call started and when it ended. You need to write a SQL query that calculates the average call processing time for each agent.
Suppose we have the following sample data in the table:
Example Input
| call_id | agent_id | start_time | end_time |
|---|---|---|---|
| 1 | 101 | 08/09/2022 08:15:00 | 08/09/2022 08:25:30 |
| 2 | 102 | 08/09/2022 08:20:10 | 08/09/2022 08:45:00 |
| 3 | 101 | 08/09/2022 08:30:00 | 08/09/2022 08:39:00 |
| 4 | 103 | 08/09/2022 08:32:00 | 08/09/2022 08:50:00 |
| 5 | 102 | 08/09/2022 08:45:10 | 08/09/2022 09:02:00 |
Answer:
In this query, we first create a Common Table Expression (CTE) named that calculates the duration of each call in seconds. We then query this CTE grouping on the and calculate the average call duration for each agent.
For more window function practice, solve this Uber SQL problem on DataLemur's online SQL code editor:
SQL Question 2: Call Center Database Design
Five9 is a cloud contact center solution. Let's say as a part of new initiative, they want to understand their call center operation in a better way. Currently, they are interested in understanding the number of calls handled by each employee within a specific date range.
Assume the two tables in their database are: and . The table logs each individual call while the table keeps a record of all employees.
Example Input:
| call_id | employee_id | timestamp | call_duration_seconds | customer_id |
|---|---|---|---|---|
| 1 | 101 | 2022-05-01 14:30:00 | 300 | 2001 |
| 2 | 102 | 2022-05-03 09:10:00 | 450 | 2002 |
| 3 | 101 | 2022-06-02 11:15:00 | 600 | 2003 |
| 4 | 103 | 2022-06-05 16:45:00 | 350 | 2004 |
| 5 | 102 | 2022-06-21 13:30:00 | 800 | 2005 |
Example Input:
| employee_id | first_name | last_name | hire_date |
|---|---|---|---|
| 101 | Mike | Smith | 2019-07-15 |
| 102 | Jane | Doe | 2020-01-20 |
| 103 | John | Johnson | 2021-04-30 |
Construct a SQL query that gives out the number of calls managed by each employee from 1st May 2022 to 30th June 2022.
Answer:
This SQL query joins the and tables on the field. It then limits the records to only those where the call timestamp is between 1st May 2022 and 30th June 2022. The function is used to count the number of calls for each employee. The result is sorted in descending order based on the number of calls.
SQL Question 3: What is a cross-join, and when would you use one?
A cross-join, also known as a cartesian join, is a type of join that returns the cross-product of two tables. In a cross-join, each row from the first table is paired with every row from the second table, resulting in a new table that has a row for each possible combination of rows from the two input tables.
For example, say you worked on the Marketing Analytics team at Five9, and needed to understand what advertising copy (text) goes best with what advertising creative (the photo that gets posted with the advertisement copy).
Here is an example of a cross-join between a table of ad_copy and a table of ad_creative:
You could this query to generate all possible combinations of ad copy and ad creative to help you create more effective ads for Five9. Just be careful: if you had 1,000 rows of ad copy and 1,000 different image creatives, the resulting cross-join would have 1 million rows!
SQL Question 4: Average Call Duration
Five9 is a provider of cloud software for contact centers. As part of their routine analysis, they often like to monitor the average duration of calls handled by each agent on a daily basis. Assume you have a database table that logs every call made by the agents, where each row contains the agent_id, start time and end time of the call. Write a SQL query to find the average call duration by each agent for the date '2022-06-08'.
Example Input:
| call_id | agent_id | start_time | end_time |
|---|---|---|---|
| 1 | 101 | 2022-06-08 09:00:00 | 2022-06-08 09:15:00 |
| 2 | 101 | 2022-06-08 09:30:00 | 2022-06-08 09:45:00 |
| 3 | 102 | 2022-06-08 10:00:00 | 2022-06-08 10:15:00 |
| 4 | 102 | 2022-06-08 10:30:00 | 2022-06-08 11:00:00 |
| 5 | 101 | 2022-06-08 11:30:00 | 2022-06-08 11:45:00 |
Example Output:
| agent_id | avg_duration_minutes |
|---|---|
| 101 | 15.0 |
| 102 | 22.5 |
Answer:
This query uses the PostgreSQL function to calculate the duration of each call in seconds, then divides by 60 to convert it to minutes. It calculates the average duration of all calls made by each agent on the specific date, '2022-06-08'.
To practice a very similar question try this interactive Verizon International Call Percentage Question which is similar for calculating durations of calls or this Stripe Repeated Payments Question which is similar for handling timestamped data.
SQL Question 5: What's the difference between the and operators?
is used to combine the output of multiple statements into one big result!
Suppose you were a Data Analyst at Five9 working on a Sales Analytics project, and had data on sales leads from both the company's Salesforce CRM and it's legacy Hubspot CRM. To write a query to analyze leads created before 2023 started, across both CRMs, you could use in the following way:
filters out duplicates, so each email/job title/company only shows up once (even if it's in both Salesforce and HubSpot CRMs).
On the other hand, the operator does NOT filter out duplicates, so if a lead shows up in both CRMs, it'll be listed twice in the output of .
SQL Question 6: Calculating Click-Through-Rate in Five9
Five9 is a cloud contact center software for the digital enterprise, bringing the power of cloud innovation to customers and facilitating more than three billion customer interactions annually. Taking this into account, one possible interview question could involve understanding the click-through rates (CTR) for the different campaigns being run by the company.
Imagine that Five9 runs multiple digital marketing campaigns. They present banners, sidebar ads, and video ads. They want to find out the click-through-rate (CTR) of each type of campaign for each day. The CTR is calculated as click / impressions * 100%.
Given two tables - 'impressions' that tracks each time an ad was shown and 'clicks' that tracks each time an ad was clicked, can you write a SQL query to find out the CTR for each campaign type for each day?
Example Input:
| impression_id | campaign_type | date |
|---|---|---|
| 1 | banner | 2021-01-01 |
| 2 | video | 2021-01-01 |
| 3 | sidebar | 2021-01-01 |
| 4 | banner | 2021-01-02 |
| 5 | video | 2021-01-02 |
Example Input:
| click_id | campaign_type | date |
|---|---|---|
| 1 | banner | 2021-01-01 |
| 2 | banner | 2021-01-01 |
| 3 | sidebar | 2021-01-01 |
Answer:
This query joins the impressions and clicks tables based on the campaign type and the date. It then groups the data by the date and campaign type, and calculates the CTR as the ratio of the count of click IDs to the count of impression IDs times 100. The result is the CTR for each campaign type for each day.
To practice another question about calculating rates, try this SQL interview question from TikTok on DataLemur's interactive SQL code editor:
SQL Question 7: What's the difference between and clause?
The clause works similarly to the clause, but it is used to filter the groups of rows created by the clause rather than the rows of the table themselves.
For example, say you were analyzing Five9 sales data:
This query retrieves the total sales for all products in each region, and uses the clause to only sales made after January 1, 2023. The rows are then grouped by region and the clause filters the groups to include only those with total sales greater than $500k.
SQL Question 8: Locate Customers from a Specific Region in Five9
Five9 is a cloud software company specialized in contact centers. Inside their customer database, they have fields such as , , , , and . For this exercise, the field corresponds to the geographical region of the customer. Your task is to filter customers who are from the North America ('NA') region.
Given the following table:
Example Input:
| customer_id | first_name | last_name | region | |
|---|---|---|---|---|
| 101 | John | Doe | jdoe@example.com | NA |
| 102 | Jane | Smith | jsmith@example.com | EU |
| 103 | Carlos | Mendes | cmendes@example.com | LA |
| 104 | Alice | Chen | achen@example.com | AP |
| 105 | Robert | Johnson | rjohnson@example.com | NA |
The expected output would be:
Example Output:
| customer_id | first_name | last_name | region | |
|---|---|---|---|---|
| 101 | John | Doe | jdoe@example.com | NA |
| 105 | Robert | Johnson | rjohnson@example.com | NA |
Answer:
The above SQL query will retrieve all records from the table where the matches 'NA' which represents North America. This would help Five9 filter all their customers from the North America region.
SQL Question 9: Fetch Customer Information Along With Their Total Spending
As a data analyst at Five9, your task is to examine the company's database to understand the customers' buying behavior. You need to write a SQL query that joins the table, table, and the table to fetch each customer's information alongside their total spending.
The table is structured as follows:
Example Input:
| customer_id | first_name | last_name | |
|---|---|---|---|
| 101 | John | Doe | john.doe@example.com |
| 102 | Jane | Smith | jane.smith@example.com |
| 103 | David | Johnson | david.johnson@example.com |
The table is structured as follows:
Example Input:
| order_id | customer_id | order_date |
|---|---|---|
| 5001 | 101 | 2021-07-10 00:00:00 |
| 5002 | 102 | 2022-03-15 00:00:00 |
| 5003 | 103 | 2022-01-20 00:00:00 |
The table is structured as follows:
Example Input:
| item_id | order_id | product_name | price |
|---|---|---|---|
| 9001 | 5001 | Phone | 299 |
| 9002 | 5002 | Laptop | 799 |
| 9003 | 5003 | TV | 1299 |
Answer:
With the following PostgreSQL query:
This query first joins the , , and tables on their related columns ( and ). It then groups the joined table by and calculates the sum of the in each group, which represents each customer's total spending. Each row in the result corresponds to one customer with their total spending.
Since joins come up frequently during SQL interviews, practice this Snapchat Join SQL question:
How To Prepare for the Five9 SQL Interview
The best way to prepare for a Five9 SQL interview is to practice, practice, practice.
Besides solving the above Five9 SQL interview questions, you should also solve the 200+ SQL exercises on DataLemur which come from companies like Google, Facebook, Microsoft and Amazon.
Each SQL question has hints to guide you, full answers and crucially, there is an interactive coding environment so you can instantly run your SQL query answer and have it checked.
To prep for the Five9 SQL interview you can also be a great idea to solve interview questions from other tech companies like:
In case your SQL skills are weak, forget about going right into solving questions – strengthen your SQL foundations with this interactive SQL tutorial.
This tutorial covers things like SUM/AVG window functions and sorting data with ORDER BY – both of which pop up frequently in Five9 interviews.
Five9 Data Science Interview Tips
What Do Five9 Data Science Interviews Cover?
In addition to SQL interview questions, the other types of questions to prepare for the Five9 Data Science Interview are:
- Stats Interview Questions
- Python or R Programming Questions
- Business Sense and Product-Sense Questions
- Machine Learning and Predictive Modeling Questions
- Behavioral & Resume-Based Questions
How To Prepare for Five9 Data Science Interviews?
The best way to prepare for Five9 Data Science interviews is by reading Ace the Data Science Interview. The book's got:
- 201 Interview Questions from FAANG tech companies
- A Refresher on Stats, ML, & Data Case Studies
- Great Reviews (900+ 5-star reviews on Amazon)