9 C3.ai SQL Interview Questions (Updated 2024)
At C3.ai, SQL is used frequently for analyzing vast training datasets that get fed into the different Machine Learning & AI models c3 builds. So, it shouldn't surprise you that C3.ai almost always evaluates jobseekers on SQL problems during interviews for Data Analytics, Data Science, and Data Engineering jobs.
To help you ace the C3.ai SQL interview, we'll cover 9 C3 AI SQL interview questions – can you answer each one?
9 C3.AI SQL Interview Questions
SQL Question 1: Analyzing Energy Usage Over Time
The C3.ai platform contains a table named , which contains meter-reading data for a series of energy customers over time. Write a SQL query to find the difference in energy consumption for every customer between consecutive readings.
Example Input:
| reading_id | customer_id | reading_date | kwh |
|---|---|---|---|
| 1 | 001 | 2021-08-01 00:00:00 | 500 |
| 2 | 001 | 2021-09-01 00:00:00 | 1000 |
| 3 | 001 | 2021-10-01 00:00:00 | 1500 |
| 4 | 002 | 2021-08-01 00:00:00 | 600 |
| 5 | 002 | 2021-09-01 00:00:00 | 1200 |
| 6 | 002 | 2021-10-01 00:00:00 | 1800 |
Example Output:
| customer_id | reading_date | consumption |
|---|---|---|
| 001 | 2021-09-01 00:00:00 | 500 |
| 001 | 2021-10-01 00:00:00 | 500 |
| 002 | 2021-09-01 00:00:00 | 600 |
| 002 | 2021-10-01 00:00:00 | 600 |
Answer:
This SQL query uses the LAG window function to get the energy reading () from the previous row within each of customer_id. The difference between the current and the previous one is then calculated to find the energy consumption over the given period. This is done for each in a chronologically ordered manner ().
Pro Tip: Window functions are a popular SQL interview topic, so practice all the window function problems on DataLemur
SQL Question 2: Customer Usage Pattern Analysis
C3.ai provides AI software for various industries. One of its products is an energy management system for commercial buildings. To find usage patterns and optimize energy consumption, it stores energy usage data per hour. The tables include , , and .
Example Input:
| building_id | name | location | constructed_year |
|---|---|---|---|
| 1001 | "Herald Tower" | "NY" | 2000 |
| 1002 | "Spectrum Center" | "LA" | 2005 |
| 1003 | "Capstone Building" | "SF" | 2001 |
Example Input:
| usage_id | building_id | usage_date_time | energy_consumed |
|---|---|---|---|
| 17323 | 1001 | 2022-06-08 10:00:00 | 50 |
| 17324 | 1002 | 2022-06-08 10:00:00 | 70 |
| 17325 | 1002 | 2022-06-08 11:00:00 | 75 |
| 17326 | 1001 | 2022-06-08 11:00:00 | 55 |
Example Input:
| weather_id | location | weather_date_time | temperature |
|---|---|---|---|
| 9872 | "NY" | 2022-06-08 10:00:00 | 22 |
| 9873 | "LA" | 2022-06-08 10:00:00 | 28 |
| 9874 | "LA" | 2022-06-08 11:00:00 | 27 |
| 9875 | "NY" | 2022-06-08 11:00:00 | 21 |
Write an SQL query that returns the average energy usage per building during peak hours (9 AM - 5 PM) for a day when temperature was above 20 degrees Celsius.
Answer:
The query links the three tables on location and datetime, filters for times and temperatures according to the conditions, then groups by building name to return the average energy used for those buildings during the specified conditions.
SQL Question 3: What's the purpose of a primary key?
The primary key of a table is a column or set of columns that serves as a unique identifier for each row. It ensures that all rows are distinct and does not allow null values.
For example, say you had stored some Facebook ad campaign data that C3.ai ran:
The column uniquely identifies each row in the table, and the PRIMARY KEY constraint ensures that no two rows have the same . This helps to maintain the integrity of the data in the table by preventing duplicate rows.
The primary key is also an important part of the table because it allows you to easily identify and reference specific campaigns in your Facebook Ad data. You can use it to join to other tables in the database, such as a table containing data on the results of the campaigns.
SQL Question 4: Filter Customer Data Based on Specific Conditions
You are a data analyst at C3.ai. For sales and marketing purposes, your team is interested in the subset of customers who signed up in the last 6 months, are from USA, and have purchased a premium product.
Write a valid SQL query to filter the company's customer database and obtain the desired records.
You are given two tables - and .
example input:
| Customer_id | Signup_date | Country |
|---|---|---|
| 1 | 2022-03-01 | USA |
| 2 | 2022-02-01 | USA |
| 3 | 2021-06-20 | CAN |
| 4 | 2022-04-25 | USA |
| 5 | 2021-12-19 | USA |
example input:
| Purchase_id | Customer_id | Product_type | Purchase_date |
|---|---|---|---|
| 10 | 2 | Premium | 2022-05-10 |
| 20 | 1 | Standard | 2022-07-12 |
| 30 | 4 | Premium | 2022-07-25 |
| 40 | 3 | Premium | 2022-06-14 |
| 50 | 5 | Standard | 2022-06-17 |
We want to return a list of customer_ids.
Answer:
Using PostgreSQL, the query would be written as follows:
This query will provide the customer_id of all recent customers (those who signed up in the last six months) from the USA who have purchased a premium product. The JOIN is used to combine the Customers and Purchases tables where the customer ids match, and the WHERE clause applies the specific requirements for the customers we're interested in.
SQL Question 5: In database design, what do foreign keys do?
A foreign key is a column or group of columns in a table that refers to the primary key in another table. The foreign key constraint helps maintain referential integrity between the two tables. The table with the foreign key is called the child table, while the table with the candidate key is called the parent or referenced table.
For example, consider a database with two tables: and customer_idcustomer_idcustomer_id` column in C3.ai customers table.
SQL Question 6: Calculate the average power generated per turbine
C3.ai is a leading enterprise AI software provider for accelerating digital transformation. Looking at that scope, we can construct a problem where we have a database of turbines which each record the power they generate hourly.
In this scenario, we'd like you to find the average power generated by each turbine in the database.
For this problem, imagine we have a table containing the following columns:
- : The ID of the turbine that generated the power.
- : The timestamp at which the power was generated.
- : The amount of power generated, in kilowatts (kW).
Here's some example data for this:
Example Input:
| turbine_id | timestamp | power_generated |
|---|---|---|
| 1 | 1/1/2020 00:00 | 500 |
| 1 | 1/1/2020 01:00 | 520 |
| 1 | 1/1/2020 02:00 | 510 |
| 2 | 1/1/2020 00:00 | 400 |
| 2 | 1/1/2020 01:00 | 410 |
| 2 | 1/1/2020 02:00 | 420 |
Imagine we want the output in the following format:
Example Output:
| turbine_id | average_power_generated |
|---|---|
| 1 | 510 |
| 2 | 410 |
Answer:
Here's an SQL query which would solve this problem:
This query groups all the data by the , and then for each turbine, it computes the average of the column.
SQL Question 7: Can you explain what / SQL commands do?
For a tangible example of in PostgreSQL, suppose you were doing an HR Analytics project for C3.ai, and had access to C3.ai's contractors and employees data. Assume that some employees were previously contractors, and vice versa, and thus would show up in both tables.
You could use operator to find all contractors who never were a employee using this query:
Note that is available in PostgreSQL and SQL Server, while is the equivalent operator which is available in MySQL and Oracle (but don't worry about knowing which RDBMS supports which exact commands since C3.ai interviewers aren't trying to trip you up on memorizing SQL syntax).
SQL Question 8: Analyzing Click-Through Rates for C3.ai's Digital Ad Campaigns
As a data analyst at C3.ai, you have been provided with the following datasets to analyze the effectiveness of digital ad campaigns via their Click-Through Rates (CTR). C3.ai has run several digital ad campaigns aimed at attracting potential customers to their website. These ads have appeared on various online platforms and are each identified by a unique campaign_id. When a potential customer clicks on one of these ads, they are directed to C3.ai's website, and an entry is recorded on the table.
Meanwhile, the table tracks when a user, who has arrived at the website via an ad, adds a product to their cart.
The structure of the tables is as follows:
Table:
| campaign_id | user_id | click_time |
|---|---|---|
| 1001 | 487 | 07/08/2022 00:00:00 |
| 1002 | 265 | 07/10/2022 00:00:00 |
| 1005 | 741 | 07/18/2022 00:00:00 |
| 1003 | 192 | 07/26/2022 00:00:00 |
| 1004 | 981 | 07/05/2022 00:00:00 |
Table:
| user_id | conversion_time | product |
|---|---|---|
| 487 | 07/08/2022 00:01:00 | Product1 |
| 265 | 07/10/2022 00:10:00 | Product3 |
| 741 | 07/18/2022 01:00:00 | Product2 |
| 192 | 07/26/2022 01:30:00 | Product1 |
Your task is to write a PostgreSQL query that calculates the click-through conversion rate for each ad campaign. The click-through conversion rate is defined as the number of users who add a product to their cart after clicking an ad, divided by the total number of users who click on the ad, for each ad campaign.
Answer:
Explanation:
In this query, we're executing a left join on and tables based on and . This ensures that we're only considering conversions that happened after a user clicked an ad.
Next, we're grouping our results by and calculating the conversion rate by dividing the which represents the unique users who converted, by the , which represents the unique users who clicked the ad.
Conversion rate is a fundamental KPI in digital advertising to measure the success of online campaigns.
To practice another question about calculating rates, solve this SQL interview question from TikTok on DataLemur's online SQL code editor:
SQL Question 9: Analyze customer purchase history with product details
Given two tables, one being table storing customer details as well as their purchase history and other being table containing product details. Your task is to write a SQL query that will join both the tables and provide a list of customers along with the details of the products they have purchased.
Table:
| customer_id | customer_name | purchase_id |
|---|---|---|
| 1 | James Smith | 1001 |
| 2 | Maria Garcia | 1002 |
| 3 | David Johnson | 1003 |
| 4 | Sally Davis | 1004 |
| 5 | Michael Miller | 1005 |
Table:
| product_id | purchase_id | product_name | price |
|---|---|---|---|
| 501 | 1001 | Product_501 | $500 |
| 502 | 1002 | Product_502 | $400 |
| 503 | 1003 | Product_503 | $600 |
| 504 | 1004 | Product_504 | $200 |
| 505 | 1005 | Product_505 | $700 |
Expected Output:
| customer_id | customer_name | purchase_id | product_name | price |
|---|---|---|---|---|
| 1 | James Smith | 1001 | Product_501 | $500 |
| 2 | Maria Garcia | 1002 | Product_502 | $400 |
| 3 | David Johnson | 1003 | Product_503 | $600 |
| 4 | Sally Davis | 1004 | Product_504 | $200 |
| 5 | Michael Miller | 1005 | Product_505 | $700 |
Answer:
Here is a PostgreSQL query to solve the problem:
With this query, we join Customers table and Products table on the column . It creates a result set that contains customer details as well as the details of the products they have purchased.
Since join questions come up frequently during SQL interviews, practice this Spotify JOIN SQL question:
How To Prepare for the C3.ai SQL Interview
The key to acing a C3.ai SQL interview is to practice, practice, and then practice some more!
Beyond just solving the earlier C3.ai SQL interview questions, you should also solve the 200+ SQL questions on DataLemur which come from companies like Google, Facebook, Microsoft and Amazon.
Each problem on DataLemur has multiple hints, detailed solutions and best of all, there's an interactive SQL code editor so you can instantly run your SQL query and have it graded.
To prep for the C3.ai SQL interview you can also be a great idea to solve SQL problems from other tech companies like:
In case your SQL query skills are weak, don't worry about going right into solving questions – improve your SQL foundations with this SQL tutorial for Data Analytics.
This tutorial covers SQL concepts such as CASE/WHEN statements and joining multiple tables – both of which show up routinely during C3.ai interviews.
C3.AI Data Science Interview Tips
What Do C3.ai Data Science Interviews Cover?
In addition to SQL interview questions, the other types of questions tested in the C3.ai Data Science Interview are:
- Statistics and Probability Questions
- Python or R Programming Questions
- Business Sense and Product-Sense Questions
- Machine Learning Questions
- Behavioral & Resume-Based Questions
How To Prepare for C3.ai Data Science Interviews?
The best way to prepare for C3.ai Data Science interviews is by reading Ace the Data Science Interview. The book's got:
- 201 Interview Questions from FAANG & startups
- A Crash Course covering SQL, Product-Sense & ML
- Amazing Reviews (900+ reviews, 4.5-star rating)