At Advantech, SQL is used all the damn time for analyzing industrial IoT data for predictive maintenance and managing their vast database of IoT products for efficient inventory management. That's why Advantech almost always asks SQL query questions during interviews for Data Science and Data Engineering positions.
So, to help you ace the Advantech SQL interview, we've curated 10 Advantech SQL interview questions – can you solve them?
Advantech is a technology company offering a range of products and services. To evaluate the importance of customers and identify "whale" customers, let's say the most valuable customers are those who have bought the most number of products in the past year. Your task is to write a SQL query to identify these customers.
Consider the following two tables: 'Customers' and 'Purchases'
Example Input:
customer_id | first_name | last_name |
---|---|---|
100 | John | Doe |
101 | Jane | Smith |
102 | Sam | Brown |
Example Input:
purchase_id | customer_id | product_id | purchase_date |
---|---|---|---|
1 | 100 | 2001 | 2021-03-20 |
2 | 100 | 2002 | 2021-04-15 |
3 | 101 | 2001 | 2021-05-22 |
4 | 102 | 2002 | 2021-06-10 |
5 | 101 | 2003 | 2021-07-30 |
6 | 101 | 2002 | 2021-08-12 |
7 | 102 | 2003 | 2021-09-27 |
8 | 100 | 2003 | 2021-11-05 |
9 | 101 | 2001 | 2022-01-16 |
10 | 100 | 2002 | 2022-02-23 |
Note: 'purchase_date' has entries from the past year.
This query uses a JOIN operation between the Customers and Purchases tables based on the 'customer_id' attribute. It filters the Purchases for the past year using a WHERE clause and then groups the data by the 'customer_id'. It calculates the number of products purchased per customer using the COUNT() function. The result is sorted in descending order to highlight the customers with the highest number of purchases. This way, we identify the power users or "whale" customers for Advantech.
To solve a related customer analytics question on DataLemur's free interactive coding environment, try this Microsoft SQL Interview problem:
Consider the following scenario at Advantech:
Advantech has an online system where customers leave reviews and ratings on its products. An important task for the data team at Advantech, a leading provider of industrial computerization products, is to analyze these reviews to enlighten their product development team.
A very common request from the product team is to understand the product satisfaction level on a month by month basis. Write a SQL query that calculates the monthly average rating given for each product.
review_id | user_id | submit_date | product_id | stars |
---|---|---|---|---|
6171 | 123 | 2021-06-08 | 50001 | 4 |
7802 | 265 | 2021-06-10 | 69852 | 4 |
5293 | 362 | 2021-06-18 | 50001 | 3 |
6352 | 192 | 2021-07-26 | 69852 | 3 |
4517 | 981 | 2021-07-05 | 69852 | 2 |
month | product_id | avg_stars |
---|---|---|
Jun | 50001 | 3.50 |
Jun | 69852 | 4.00 |
Jul | 69852 | 2.50 |
You can calculate the monthly average rating for each product by using the SQL window function, AVG(), combined with the DATE_TRUNC() method to get the month part from the submit_date.
This SQL query first truncates the submit_date to the month and then calculates the average star rating for each product_id, month pair. The result is ordered by the submit_date and product_id to make it easy to see the average ratings for each month.
Pro Tip: Window functions are a frequent SQL interview topic, so practice every window function problem on DataLemur
The clause in SQL allows you to select records that are unique, eliminating duplicates.
For example, if you had a table of Advantech employees, and wanted to see what are all the unique job titles that currently worked at the company, you could write the following query:
Advantech is interested in filtering their customer database in order to analyze their customers. Specifically, they want to find customers from the 'Technology' sector who have made purchases over 1000. They are interested in the customer name, company, sector, and total purchases.
customer_id | name | company | sector |
---|---|---|---|
00351 | John Doe | Advantech | Technology |
00478 | Mary Smith | Pfizer | Healthcare |
00687 | Lucas Green | Apple | Technology |
00852 | Emma Johnson | Johnson & Johnson | Healthcare |
00991 | Liam Brown | Microsoft | Technology |
purchase_id | customer_id | total |
---|---|---|
10501 | 00351 | 5500 |
10782 | 00478 | 900 |
15293 | 00687 | 8200 |
16352 | 00852 | 3000 |
20417 | 00991 | 6000 |
name | company | sector | total_purchases |
---|---|---|---|
John Doe | Advantech | Technology | 5500 |
Mary Smith | Pfizer | Healthcare | 900 |
Lucas Green | Apple | Technology | 8200 |
Liam Brown | Microsoft | Technology | 6000 |
This query works by joining the 'customers' and 'purchases' tables based on matching customer_id values. It then uses a WHERE clause to filter rows based on whether the customer's sector is 'Technology' and they've made purchases over 1000.
When designing a database schema, a one-to-one relationship between two entities is characterized by each entity being related to a single instance of the other. An example of this is the relationship between a car and a license plate - each car has one license plate, and each license plate belongs to one car.
On the other hand, a one-to-many relationship is when one entity can be associated with multiple instances of the other entity. For example, a person can have multiple email addresses, but each email address only relates back to one person.
Advantech, a tech-based company, recently launched a digital marketing campaign. As part of the campaign, it shows digital ads to users and tracks each user's interactions with the ads. These interactions include viewing the ad (impression), clicking on the ad (click), and purchasing a product after clicking on the ad (conversion). Using this tracking data, your task is to calculate the click-through rates (CTR) and conversion rates (CR) for the digital ads.
ad_id | user_id | event | event_date |
---|---|---|---|
105 | 34234 | impression | 2022-08-01 13:00:00 |
106 | 38745 | impression | 2022-08-01 13:05:00 |
105 | 34234 | click | 2022-08-01 13:06:00 |
106 | 38745 | click | 2022-08-01 13:09:00 |
105 | 34234 | conversion | 2022-08-01 14:00:00 |
For each ad, the CTR is calculated as the number of clicks divided by the number of impressions, and the CR is calculated as the number of conversions divided by the number of clicks.
The SQL query above will give us the CTR and CR for each ad. It uses statements inside the function to count the number of each event type for each ad. The CTR and CR are then calculated by dividing these counts.
To solve a similar SQL problem on DataLemur's free online SQL coding environment, try this Facebook SQL Interview question:
In SQL, the operator combines the result of two or more SELECT statements into a single result set. Note that each statement within the UNION must have the same number of columns and the columns must have similar data types. The operator removes duplicates from the final result set. If you want to include duplicates in the final result set, you can use the operator instead.
Here's an example of using the UNION operator to combine the results of two SELECT statements that retrieve data from tables of Advantech's Facebook ads and their Google ads:
This statement would return a combined result set of both Facebook and Google ads that have a spend greater than $500.
Imagine you're working with the Advantech's sales data. Advantech wants to understand the performance of their products on a monthly basis.
They want you to write a query that identifies the average quantity sold per product for each month.
Please use the following tables to solve this problem:
sale_id | sale_date | product_id | quantity |
---|---|---|---|
101 | 01/05/2023 | 001 | 10 |
102 | 01/12/2023 | 002 | 5 |
103 | 02/03/2023 | 001 | 15 |
104 | 02/18/2023 | 002 | 20 |
105 | 02/28/2023 | 001 | 20 |
month | product | avg_quantity |
---|---|---|
1 | 001 | 10.00 |
1 | 002 | 5.00 |
2 | 001 | 17.50 |
2 | 002 | 20.00 |
In terms of what this query does, it's selecting the average quantity per month for each distinct product. It does this by extracting the month from the 'sale_date' column, grouping by that month and the 'product_id', then applying the AVG aggregate function to the 'quantity' sold.
Advantech operates in various locales, and one frequent task is that the company needs to find customer records from a particular city. For instance, you could be asked to find all customer records from the city starting with the string 'san' (like San Francisco, Santiago, etc.).
To solve this SQL problem, you need to write a command that fetches all rows where city starts with 'san' using the SQL keyword.
This SQL query fetches all columns () from where the column starts with the string 'san' (). In SQL, the '%' character is a wildcard that matches any sequence of characters, so 'san%' matches any string that starts with 'san'. The records are then filtered as per this condition and the results are inline with our expected output.
Cross join and natural join are like two sides of a coin in the world of SQL.
Cross joins is like the wild and reckless cousin who creates a giant new table by combining every row from table A with every row from table B, no questions asked, no common key needed.
Natural joins are like the more refined and selective cousin who only combines rows from multiple tables if they have something in common (i.e., common columns/keys).
While cross join doesn't discriminate and will create a massive table if given the chance, natural join is more selective and only returns a table with the number of rows equal to the number of matching rows in the input tables. So, choose your JOIN wisely!
The key to acing a Advantech SQL interview is to practice, practice, and then practice some more! Besides solving the earlier Advantech SQL interview questions, you should also solve the 200+ tricky sql questions on DataLemur which come from companies like Netflix, Google, and Amazon.
Each problem on DataLemur has multiple hints, full answers and most importantly, there is an interactive coding environment so you can right in the browser run your query and have it checked.
To prep for the Advantech SQL interview you can also be a great idea to solve SQL problems from other tech companies like:
However, if your SQL skills are weak, forget about diving straight into solving questions – go learn SQL with this DataLemur SQL Tutorial.
This tutorial covers SQL concepts such as SUM/AVG window functions and Self-Joins – both of these show up frequently during SQL job interviews at Advantech.
In addition to SQL query questions, the other types of problems tested in the Advantech Data Science Interview are:
To prepare for Advantech Data Science interviews read the book Ace the Data Science Interview because it's got: