# 8 Foxconn SQL Interview Questions (Updated 2024)

Updated on

February 14, 2024

At Foxconn, SQL is used day-to-day for analyzing production line efficiency and predicting equipment maintenance needs, crucial aspects in the electronics manufacturing industry. That's why Foxconn LOVES to ask SQL problems in interviews for Data Science, Analytics, and & Data Engineering jobs.

So, to help you study for the Foxconn SQL interview, here’s 8 Foxconn SQL interview questions – how many can you solve?

## 8 Foxconn SQL Interview Questions

### SQL Question 1: Identifying Valuable Customers for Foxconn

Imagine working as a Data Analyst at Foxconn, a company known for producing a significant amount of electronics. The primary key metrics for identifying valuable customers, or whale users, are the total amount spent on purchases and the frequency of these purchases.

Identify the customers that have made more than five purchases and the total amount spent exceeds \$10,000 over the past year.

##### Table:
transaction_idcustomer_idpurchase_dateproduct_idamount_spent
1010012022-01-1210012500
1020022022-02-2610023000
1040032022-03-101003300
1050012022-03-1110012500
1060012022-03-1510012500
1070022022-03-2310024000
1080042022-04-011003200
1090012022-04-0510012500

This SQL query groups the data by and filters the results so that we only see rows where there are more than five transactions and the total amount spent is above \$10,000. Therefore, the output of this query will be the valuable customers for Foxconn according to the defined metrics - frequency of purchases and total amount spent.

To practice another SQL customer analytics question where you can solve it right in the browser and have your SQL query automatically checked, try this Walmart Labs SQL Interview Question:

### SQL Question 2: Calculate Running Total Orders Per Product

At Foxconn, there is a need to calculate the running total orders per product. The calculation should be ordered by date. The sample dataset represents orders made in the Foxconn system with the product ordered and the date of the order.

Here is some sample data:

##### Example Input:
order_idproduct_idorder_date
11002021-01-01
21012021-01-03
31002021-01-04
41022021-01-07
51012021-02-01
61022021-02-03
##### Example Output:
order_dateproduct_idrunning_total_orders
2021-01-011001
2021-01-031011
2021-01-041002
2021-01-071021
2021-02-011012
2021-02-031022

In this SQL question, we are required to return a running total of orders per product, with the running total ordered by date. We use a PostgresSQL window function, where we partition by and order by . This ensures that our running total is calculated separately for each product and in order of date. The clause ensures that all preceding rows for the current product are included in the running total. The final output gives us the date of the order, the product id for the order, and a running total of orders for the product up to and including that date.

To practice a similar window function interview problem which uses RANK() on DataLemur's free online SQL code editor, solve this Amazon SQL question asked in a BI Engineer interview:

### SQL Question 3: What's a database view, and what's their purpose?

A database view is a virtual table that is created based on the results of a SELECT statement, and provides you a customized, read-only version of your data that you can query just like a regular table.

You'd want to use a view for a few reasons:

• views allow you to create a simpler versions of your data based on whose running the query (such as hiding un-important columns/rows from business analysts if they're just random noisy artifricats of your Data Infrastructure pipelines)
• views can help you comply with information security requirements by hiding sensitive data from certain users (important for regulated industries like govermnet and healthcare!)
• views often improve performance for complicated queries by pre-computing the results and saving them in a view (which is often faster than re-executing the original query)... just be careful since static views don't update if the underlying data changes!

### SQL Question 4: Filter Foxconn's Product and Employee Data

As a data analyst at Foxconn, you have been tasked with finding out which electronic products have been assembled by employees working on the night shift (22:00 - 06:00) for the last month, and how many units of each product they have assembled. Consider the following tables:

##### Example Input:

Here's an example of a PostgreSQL query that could be used to pull this data:

And the possible output:

product_nametotal_units_assembled
iPhone120
MacBook60

The given query joint product, product assembly log, and employee shifts table. It filters to include only the data where employees worked on the night shift within the given date range, and then sums the number of units assembled by each product.

### SQL Question 5: What's the difference between a left and right join?

Both left and right joins in SQL allow you to combine data from different tables based on a shared key or set of keys. For a concrete example of the difference between these two join types, say you had sales data exported from Foxconn's Salesforce CRM stored in a PostgreSQL database, and had access to two tables: and .

: retrieves all rows from the left table (in this case, the sales table) and any matching rows from the right table (the table). If there is no match in the right table, NULL values will be returned for the right table's columns.

• : retrieves all rows from the right table (in this case, the customers table) and any matching rows from the left table (the sales table). If there is no match in the left table, NULL values will be returned for the left table's columns.

### SQL Question 6: Analyze Purchase History

Your role as a Data Analyst at Foxconn involves analyzing the purchase history of customers and understanding the relationship with product information. Write a SQL query to find out the top 3 products in terms of units sold for each customer.

Consider two tables for this task: and .

##### Example Input:
purchase_idcustomer_idproduct_idunits_soldpurchase_date
101123410001208/01/2022
102123410002109/01/2022
103123410003510/01/2022
104567810001408/01/2022
105567810003310/01/2022
##### Example Input:
product_idproduct_nameprice
10001iPhone 13799
10002iWatch Series 7399
10003MacBook Pro1299

In the above query, we first create a Common Table Expression (CTE) named to group sales by customer and product, and calculate the total units sold. In the main query, we join this CTE with the table to get the respective product names and order by in a descending order. We limit the output to 3 records for each customer.

Since joins come up so often during SQL interviews, try an interactive Spotify JOIN SQL question:

### SQL Question 7: Why is database normalization a good idea?

Normalization can reduce redundancy and improve performance and flexibility in a database.

By dividing larger tables into smaller, more specific ones linked by foreign keys, it is often possible to improve query speed. Note: it is not always the case that query speed will be improved due to costly nature of joins. Still, we think normalization is a good idea because it can also increase flexibility by making it easier to alter individual tables without affecting others, which can be useful for adapting Foxconn's database to ever-changing business needs.

### SQL Question 8: Calculate the Average Salary and Rounded Up Salary per Department

Foxconn is a multinational electronics contract manufacturer company. They would like to know the average salary amount per department and the rounded up average salary amount to the nearest whole number per department. Write a SQL query to solve this.

In this case, we will use the ROUND() and AVG() functions of SQL.

Let's say we have the following table of .

#### Example Input:

employee_iddepartment_idsalary
7299014658.32
8309023927.44
8359014892.47
8969033705.13
9149033401.78
9359024021.07

#### Example Output:

department_idavg_salaryrounded_avg_salary
9014775.3954775
9023974.2553974
9033553.4553554

The above query finds the average salary for each department. Using the ROUND() function, it also rounds up the average salary to the nearest whole number. The results are type cast to decimal and integer for avg_salary and rounded_avg_salary respectively to maintain proper decimal places and whole numbers. The GROUP BY clause is used to group these results by department id.

To practice a very similar question try this interactive Amazon Average Review Ratings Question which is similar for calculating averages and rounding values or this Alibaba Compressed Mean Question which is similar for finding rounded mean values.

### Foxconn SQL Interview Tips

The best way to prepare for a Foxconn SQL interview is to practice, practice, practice. Besides solving the earlier Foxconn SQL interview questions, you should also solve the 200+ tricky sql questions on DataLemur which come from companies like Facebook, Google and unicorn tech startups.

Each SQL question has multiple hints, fully explained answers along with a discussion board to see how others solved it and crucially, there is an interactive coding environment so you can right in the browser run your SQL query answer and have it checked.

To prep for the Foxconn SQL interview it is also useful to solve SQL questions from other tech companies like:

In case your SQL skills are weak, don't worry about jumping right into solving questions – strengthen your SQL foundations with this free SQL for Data Analytics course.

This tutorial covers SQL concepts such as creating summary stats with GROUP BY and handling timestamps – both of these pop up routinely in Foxconn SQL assessments.

### Foxconn Data Science Interview Tips

#### What Do Foxconn Data Science Interviews Cover?

In addition to SQL query questions, the other question categories to prepare for the Foxconn Data Science Interview are:

#### How To Prepare for Foxconn Data Science Interviews?

The best way to prepare for Foxconn Data Science interviews is by reading Ace the Data Science Interview. The book's got:

• 201 Interview Questions from FAANG & startups
• A Refresher on SQL, Product-Sense & ML
• Amazing Reviews (900+ 5-star reviews on Amazon)