11 Allegro MicroSystems SQL Interview Questions (Updated 2024)
At Allegro MicroSystems, SQL is used all the damn time for analyzing sensor data. So, it shouldn't surprise you that Allegro MicroSystems often tests SQL coding questions during interviews for Data Science and Data Engineering positions.
Thus, to help you practice for the Allegro MicroSystems SQL interview, we'll cover 11 Allegro MicroSystems SQL interview questions – able to answer them all?
11 Allegro MicroSystems SQL Interview Questions
SQL Question 1: Identify the Power Users
Allegro Microsystems wants to identify the power users amongst their customer base. A power user is defined as one who has made the most number of purchases and has the highest average product rating. To assess this, the data analyst needs to analyze the and the tables in the company's database. Could you write a SQL query to identify the top 5 power users?
Example Input:
| purchase_id | user_id | purchased_date | product_id |
|---|---|---|---|
| 5678 | 890 | 07/04/2022 00:00:00 | 1043 |
| 2345 | 312 | 05/14/2022 00:00:00 | 2234 |
| 9823 | 456 | 06/20/2022 00:00:00 | 5566 |
| 5678 | 123 | 06/15/2022 00:00:00 | 1043 |
| 5984 | 890 | 07/30/2022 00:00:00 | 5566 |
Example Input:
| rating_id | user_id | rated_date | product_id | stars |
|---|---|---|---|---|
| 1011 | 890 | 07/05/2022 00:00:00 | 1043 | 5 |
| 2022 | 312 | 05/15/2022 00:00:00 | 2234 | 4 |
| 3033 | 456 | 06/21/2022 00:00:00 | 5566 | 3 |
| 4044 | 123 | 06/16/2022 00:00:00 | 1043 | 2 |
| 5055 | 890 | 07/31/2022 00:00:00 | 5566 | 5 |
Answer:
This PostgreSQL query joins the and tables on and to create a unified view of each user's purchase history and product ratings. Then, it groups the data by to consolidate each user's actions into a single row. Next, it calculates the total number of purchases made () and the average product rating () per user. Finally, it orders the results in descending order firstly based on the number of purchases made and secondly on the average product rating from high to low, limiting the output to the top 5 rows to identify the power users.
To practice a related customer analytics question on DataLemur's free online SQL code editor, try this Microsoft Azure Cloud SQL Interview Question:
SQL Question 2: Analyze Product Sales Performance
As a data analyst at Allegro MicroSystems, you are tasked to analyze the sales performance of different products over the months. Write a SQL query to find the average number of units sold per month for each product. The result set should be sorted by product and month.
A unit is considered sold if it appears in the table. The table contains all products ever sold by Allegro MicroSystems.
The table has the following structure:
Example Input
| sale_id | sale_date | product_id | units_sold |
|---|---|---|---|
| 1 | 2022-01-14 | 1 | 15 |
| 2 | 2022-01-15 | 2 | 30 |
| 3 | 2022-01-20 | 2 | 20 |
| 4 | 2022-02-01 | 1 | 20 |
| 5 | 2022-02-05 | 2 | 25 |
| 6 | 2022-02-10 | 1 | 30 |
The table has the following structure:
Example Input
| product_id | product_name |
|---|---|
| 1 | Product 1 |
| 2 | Product 2 |
Hint: You might have to use window functions for this task.
Answer:
This query first joins the and tables on the column. Then, it uses the function to separate out the month from the column. An window function calculates the average of for each product on a monthly basis, partitioned by and month. The result is then ordered by and month.
p.s. Window functions show up super often during SQL interviews, so practice the 27+ window function questions on DataLemur
SQL Question 3: Can you give an example of a one-to-one relationship between two entities, vs. a one-to-many relationship?
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.
SQL Question 4: Product Sales Analysis
Allegro MicroSystems is a global leader in developing high-performance power and sensing solutions. They manufacture and sell a variety of semiconductor devices worldwide, which are used in automotive and industrial applications. Your task is to design a database system to manage and track their sales, and then write a query to identify the product having the maximum sales each month.
Two tables are particularly relevant to this task:
- contains information about the products sold by Allegro Microsystems.
- contains the sales data for these products.
Example Input:
| product_id | product_name | product_category |
|---|---|---|
| 1001 | Power IC | Power Solutions |
| 1002 | Hall-effect Sensor IC | Sensing Technologies |
| 1003 | Fan Controller IC | Motor Controllers |
Example Input:
| sale_id | product_id | sale_date | price | units_sold |
|---|---|---|---|---|
| 5001 | 1001 | 06/10/2022 | $1.2 | 5000 |
| 5002 | 1001 | 06/18/2022 | $1.2 | 6000 |
| 5003 | 1002 | 07/05/2022 | $0.8 | 7000 |
| 5004 | 1003 | 07/26/2022 | $1.5 | 3000 |
| 5005 | 1003 | 06/08/2022 | $1.5 | 4000 |
| 5006 | 1003 | 06/25/2022 | $1.5 | 6000 |
Answer:
The following PostgreSQL query can be used to identify the product having the maximum sales each month:
This query first aggregates the sales data at a monthly level for each product and calculates the total sales value (price x units sold) per product per month. Then, it ranks the products based on the total sales value in descending order. Each month's product with the maximum sales is the one whose record appears first. The DATE_TRUNC function is used to bucket the sales data into months.
SQL Question 5: What does the SQL keyword do?
The keyword removes duplicates from a query.
Suppose you had a table of Allegro MicroSystems customers, and wanted to figure out which cities the customers lived in, but didn't want duplicate results.
table:
| name | city |
|---|---|
| Akash | SF |
| Brittany | NYC |
| Carlos | NYC |
| Diego | Seattle |
| Eva | SF |
| Faye | Seattle |
You could write a query like this to filter out the repeated cities:
Your result would be:
| city |
|---|
| SF |
| NYC |
| Seattle |
SQL Question 6: Calculate Average Product Sale by Month
Allegro MicroSystems is a company that is primarily involved in the design and manufacture of high-performance semiconductors. Let's say that as a Data Analyst, your task includes understanding the monthly average sales of the company's different products.
Given the following table representing the sales of different products over several months, write a SQL query to calculate the average product sale per month. The table has columns product_id, sale_date (the date when the sale was made) and sales_count (the number of products sold).
Example Input:
| sale_id | product_id | sale_date | sales_count |
|---|---|---|---|
| 61538 | 311 | 06/08/2022 | 200 |
| 76492 | 250 | 06/10/2022 | 180 |
| 53003 | 311 | 06/18/2022 | 220 |
| 64326 | 250 | 07/26/2022 | 150 |
| 45019 | 311 | 07/05/2022 | 205 |
Expected Output:
| month | product_id | avg_sales |
|---|---|---|
| 6 | 311 | 210 |
| 7 | 311 | 205 |
| 6 | 250 | 180 |
| 7 | 250 | 150 |
Answer:
The function is used to get the month value from the sale_date column. Then the query groups the sales by month and product_id, and calculates the average sales_count for these groupings. The AVG function is used to compute the average.
SQL Question 7: What's the difference between relational and non-relational databases?
While both types of databases are used to store data (no duh!), relational databases and non-relational (also known as NoSQL databases) differ in a few important ways, most importantly on the way data is stored. Relational databases use a data model consisting of tables and rows, while NoSQL databases use a variety of data models, including document, key-value, columnar, and graph storage formats.
This added flexibilty makes NoSQL databases great for non-tabular data (like hierarchal data or JSON data), or data where the type/format is constantly evolving. With this added flexibility, comes one big weakness – you won't get ACID-compliance. That means, unlike relational databases which are typically adhere to the ACID properties (atomic, consistent, isolated, and durable), you don't get as strong guarentees with most non-relational databases.
SQL Question 8: Finding Customers from Specific Countries
Allegro MicroSystems is a global company, let's say they would like to filter their customer records to find all customers from a specific country. The pattern string will be the country name and the relevant data is the customer_id, first_name, last_name, and country.
Consider the 'customers' table below:
Example Input:
| customer_id | first_name | last_name | country |
|---|---|---|---|
| 1001 | John | Doe | USA |
| 1002 | Jane | Doe | USA |
| 1003 | James | Bond | UK |
| 1004 | Sarah | Connor | Germany |
| 1005 | Max | Payne | USA |
Your task is to generate a query that will allow you to search for customers from a specific country, for instance, 'USA'.
Answer:
Here is a PostgreSQL query:
This query will select the customer_id, first_name, last_name, and country from the customers table where the country is 'USA'. The LIKE keyword is used in the WHERE clause to search for the specified pattern in a column. In this scenario, it filters for customers whose country is 'USA'.
Example Output:
| customer_id | first_name | last_name | country |
|---|---|---|---|
| 1001 | John | Doe | USA |
| 1002 | Jane | Doe | USA |
| 1005 | Max | Payne | USA |
SQL Question 9: Compute the Average Sales of Each Product Type
As a data analyst at Allegro MicroSystems, your manager wants you to analyze the historical sales data and to find the average sales of each product type for the year 2022. They are also interested in knowing the names of the customers who bought those products. For this purpose, you have two tables: "sales" and "customers".
The "sales" table contains the sales data with columns: "sale_id", "customer_id", "product_type", "sale_date" and "sale_amount".
And the "customers" table contains information about customers with columns: "customer_id", "first_name", "last_name".
Example Input:
| sale_id | customer_id | sale_date | product_type | sale_amount |
|---|---|---|---|---|
| 101 | 321 | 01/20/2022 | Microcontroller | 300.00 |
| 102 | 212 | 01/21/2022 | Sensor | 200.00 |
| 103 | 321 | 02/10/2022 | Microcontroller | 500.00 |
| 104 | 543 | 02/15/2022 | Transistor | 100.00 |
| 105 | 212 | 03/30/2022 | Sensor | 150.00 |
Example Input:
| customer_id | first_name | last_name |
|---|---|---|
| 321 | John | Doe |
| 212 | Jane | Smith |
| 543 | Emma | Williams |
Example Output:
| product_type | avg_sale_amount | customer_name |
|---|---|---|
| Microcontroller | 400.00 | John Doe |
| Sensor | 175.00 | Jane Smith |
| Transistor | 100.00 | Emma Williams |
Answer:
This query joins the "sales" and "customers" tables on "customer_id", filters the data for the year 2022, and calculates the average "sale_amount" for each "product_type". It also concatenates "first_name" and "last_name" from the "customers" table to get the full names of customers. Results are grouped by product_type and customer_name to show the average sale amount by product type for each customer.
Because joins come up frequently during SQL interviews, practice this Spotify JOIN SQL question:
SQL Question 10: How would you go about optimizing a slow SQL query?
There's several steps you can take to troubleshoot a slow SQL query.
First things first, figure out why the query is slow! You can use and commands in PostgreSQL to identify any performance bottlenecks. After that, you can start changing your query, depending on what the source of the query performance issue is.
Generally, indexes can help speed up queries. Also de-normalizing your tables might help, to remove slow joins.
Lastly, you could always just upgrade your hardware! Time and money wasted improving query performance could just be better spent on mroe powerful database servers!
While this is a very surface-level approach to optimizing SQL query performance, it should do the trick for Data Analyst interviews and Data Science interviews at Allegro MicroSystems. Data Engineers should know a bit more about the and before the interview.
SQL Question 11: Calculate Standard Deviation of Weekly Sensor Output
In manufacturing environments, we often need to calculate statistical values for quality control and consistency checking. Allegro MicroSystems produces various sensors whose weekly output at each manufacturing station is recorded. Your task is to write SQL query which calculates standard deviation, rounded to 2 decimal points, of sensors' weekly output for each manufacturing station.
Assume, you have a table "weekly_output" with following sample data:
Example Input:
| station_id | week_start_date | product_id | sensor_output |
|---|---|---|---|
| 101 | 05/30/2022 | 7001 | 25631 |
| 102 | 06/06/2022 | 7026 | 25039 |
| 101 | 06/13/2022 | 7001 | 25852 |
| 102 | 06/20/2022 | 7001 | 25463 |
| 101 | 06/27/2022 | 7026 | 25577 |
| 103 | 07/04/2022 | 7001 | 24999 |
| 104 | 07/11/2022 | 7026 | 25774 |
| 101 | 07/18/2022 | 7001 | 25803 |
| 104 | 07/25/2022 | 7026 | 25111 |
Your task is to write a query which calculates the standard deviation for each manufacturing station (station_id), rounded to 2 decimal points.
Example Output:
| station_id | stddev_output |
|---|---|
| 101 | 82.71 |
| 102 | 288.64 |
| 103 | 0 |
| 104 | 464.02 |
Answer:
Use the function to calculate the standard deviation and the function to round it off to 2 decimal points.
This query groups rows by and uses the function to calculate the standard deviation for each group. The function is used to round off the calculated standard deviation to 2 decimal points. The function in SQL gives us the sample standard deviation. If station has only one record, it would return null, but PostgreSQL treats null as zero in numeric calculations.
How To Prepare for the Allegro MicroSystems SQL Interview
The best way to prepare for a Allegro MicroSystems SQL interview is to practice, practice, practice.
In addition to solving the earlier Allegro MicroSystems SQL interview questions, you should also solve the 200+ FAANG SQL Questions on DataLemur which come from companies like Facebook, Google, and VC-backed startups.
Each problem on DataLemur has hints to guide you, full answers and crucially, there's an interactive SQL code editor so you can right online code up your SQL query answer and have it checked.
To prep for the Allegro MicroSystems SQL interview it is also a great idea to solve SQL questions from other tech companies like:
However, if your SQL coding skills are weak, forget about jumping right into solving questions – refresh your SQL knowledge with this DataLemur SQL Tutorial.
This tutorial covers SQL concepts such as handling timestamps and RANK() window functions – both of these come up routinely during SQL job interviews at Allegro MicroSystems.
Allegro MicroSystems Data Science Interview Tips
What Do Allegro MicroSystems Data Science Interviews Cover?
In addition to SQL interview questions, the other types of problems to practice for the Allegro MicroSystems Data Science Interview are:
- Statistics and Probability Questions
- Python or R Coding Questions
- Product Data Science Interview Questions
- ML Modelling Questions
- Resume-Based Behavioral Questions
How To Prepare for Allegro MicroSystems Data Science Interviews?
To prepare for Allegro MicroSystems Data Science interviews read the book Ace the Data Science Interview because it's got:
- 201 interview questions taken from FAANG & startups
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