9 Howmet Aerospace SQL Interview Questions (Updated 2024)
SQL is used all the damn time at Howmet Aerospace for analyzing manufacturing data for quality control and managing aerospace-specific relational databases. That's why Howmet Aerospace almost always evaluates jobseekers on SQL query questions in interviews for Data Science, Data Engineering and Data Analytics jobs.
So, if you're studying for a SQL Assessment, we've curated 9 Howmet Aerospace SQL interview questions to practice, which are similar to recently asked questions at Howmet Aerospace – how many can you solve?
9 Howmet Aerospace SQL Interview Questions
SQL Question 1: Identify Top Customers for Howmet Aerospace
As an analyst at Howmet Aerospace, a global leader in engineered metal products, your main task is to constantly monitor and analyze the buying patterns of your customers.
One of the things the company is interested in are the 'big spenders' on aeroplane parts. Given the database , identify the 'Whale' customers, defined as those who spend over $1,000,000 in a given year. The tables contain information about each order placed in the given year with the customer_id, order_id, order_date, and order_value.
Your task is to write a query that identifies 'Whale' customers and calculates the total amount they have spent in 2022.
Example Input:
| order_id | customer_id | order_date | order_value |
|---|---|---|---|
| 1 | A | 2022-01-05 | 250000 |
| 2 | B | 2022-02-20 | 350000 |
| 3 | A | 2022-03-12 | 450000 |
| 4 | C | 2022-04-07 | 150000 |
| 5 | B | 2022-05-15 | 800000 |
| 6 | A | 2022-06-20 | 350000 |
| 7 | C | 2022-07-21 | 750000 |
| 8 | A | 2022-08-14 | 450000 |
| 9 | B | 2022-09-06 | 350000 |
| 10 | C | 2022-10-01 | 800000 |
Answer:
This query will display a list of 'Whale' customers and the total amount they have spent in 2022. It does this by grouping records by customer_id, then summing the order_value for each customer (group). The clause ensures that only customers who have spent more than $1,000,000 are included in the final result set. The final result set is ordered in descending order to have customers with the highest spending at the top.
To practice another SQL customer analytics question where you can solve it right in the browser and have your SQL code automatically checked, try this Walmart Labs SQL Interview Question:
SQL Question 2: Calculate Monthly Average Material Cost
At Howmet Aerospace, each assembly project uses different materials which are priced differently. Your task is to write a SQL query to calculate the average material cost per assembly project for each month. For each project, include the project_id, the month, and the average cost of materials.
Provide the result in descending order of the month and then arranged by the project_id.
Example Input:
| project_id | material_id | use_date | price |
|---|---|---|---|
| P001 | M567 | 2022-01-15 00:00 | 120.50 |
| P001 | M456 | 2022-01-20 00:00 | 80.25 |
| P002 | M789 | 2022-01-20 00:00 | 135.60 |
| P002 | M123 | 2022-02-10 00:00 | 90.00 |
| P001 | M567 | 2022-02-05 00:00 | 120.50 |
Example Output:
| month | project_id | avg_material_cost |
|---|---|---|
| 2022-02 | P001 | 120.50 |
| 2022-02 | P002 | 90.00 |
| 2022-01 | P001 | 100.38 |
| 2022-01 | P002 | 135.60 |
Answer:
The query first truncates the to the month using function to get the month under consideration. Then it applies the window function with the clause that breaks down the dataset into multiple 'windows' grouped by the and the month. For each 'window', it calculates the average cost. The final result is sorted in descending order by the month, and then by the .
Pro Tip: Window functions are a frequent SQL interview topic, so practice every window function problem on DataLemur
SQL QUESTION 3: Can you describe the meaning of a constraint in SQL in layman's terms?
Constraints are just rules for your DBMS to follow when updating/inserting/deleting data.
Say you had a table of Howmet Aerospace employees, and their salaries, job titles, and performance review data. Here's some examples of SQL constraints you could implement:
NOT NULL: This constraint could be used to ensure that certain columns in the employee table, such as the employee's first and last name, cannot contain NULL values.
UNIQUE: This constraint could be used to ensure that the employee ID is unique. This would prevent duplicate entries in the employee table.
PRIMARY KEY: This constraint could be used to combine the NOT NULL and UNIQUE constraints to create a primary key for the employee table. The employee ID could serve as the primary key.
FOREIGN KEY: This constraint could be used to establish relationships between the employee table and other tables in the database. For example, you could use a foreign key to link the employee ID to the department ID in a department table to track which department each employee belongs to.
CHECK: This constraint could be used to ensure that certain data meets specific conditions. For example, you could use a CHECK constraint to ensure that salary values are always positive numbers.
DEFAULT: This constraint could be used to specify default values for certain columns. For example, you could use a DEFAULT constraint to set the employee hire date to the current date if no value is provided when a new employee is added to the database.
Pro Tip: Window functions are a frequent SQL interview topic, so practice every window function problem on DataLemur
SQL Question 4: Optimizing Inventory Management
Howmet Aerospace has multiple production plants across the globe each producing certain components. One of the challenges they face is effectively managing their inventory of raw materials. A poorly managed inventory could result in production stoppages and lost business.
You are given the 'raw_materials' table detailing the raw materials used by each plant, and a 'daily_usage' table recording the daily usage of each raw material by each plant.
Your task is to answer two questions:
1. Which plant uses the highest amount of a particular raw material on average?
2. What is the average daily usage of raw materials in each plant?
Example Input:
| material_id | material_name | plant_id |
|---|---|---|
| 101 | Aluminium | 1 |
| 102 | Titanium | 1 |
| 103 | Nickel | 2 |
| 104 | Steel | 3 |
| 105 | Aluminium | 2 |
| 106 | Titanium | 3 |
| 107 | Nickel | 1 |
Example Input:
| date | plant_id | material_id | usage |
|---|---|---|---|
| 2022-06-01 | 1 | 101 | 100 |
| 2022-06-01 | 1 | 102 | 200 |
| 2022-06-02 | 1 | 101 | 150 |
| 2022-06-02 | 2 | 103 | 100 |
| 2022-06-03 | 1 | 107 | 50 |
| 2022-06-03 | 2 | 105 | 300 |
| 2022-06-03 | 3 | 104 | 200 |
| 2022-06-04 | 3 | 106 | 150 |
Answer:
1. To find the plant that uses the highest amount of each raw material on average:
2. To find the average daily usage of raw materials in each plant:
The first query will return the plant that uses the highest average amount of a particular raw material. The second query will provide the average daily usage of raw materials at each plant. It's important to track these metrics as they can offer insights into the efficiency of the production process and possible areas for improvement.
SQL QUESTION 5: Can you explain the distinction between a unique and a non-unique index?
Unique indexes help ensure that there are no duplicate key values in a table, maintaining data integrity. They enforce uniqueness whenever keys are added or changed within the index.
To define a unique index in PostgreSQL, you can use the following syntax:
To define a non-unique index in PostgreSQL, you can use the following syntax:
Non-unique indexes on the other hand, are used to improve query performance by maintaining a sorted order of frequently used data values, but they do not enforce constraints on the associated table.
SQL Question 6: Filter Customers Based on Order History.
Consider the two tables and in our database. The table contains information about all customers who did any transaction with Howmet Aerospace, including customer_id, name, and country. The table records every transaction with customer_id, order_id, product_id, quantity and order_date.
You are required to write a query that filters customers who ordered specific product(s) more than a certain quantity within a specified month period. Demonstrate this by filtering customers from who ordered the product more than quantities between and .
Table:
| customer_id | name | country |
|---|---|---|
| 1 | John Smith | USA |
| 2 | Jane Doe | USA |
| 3 | Mary Johnson | UK |
| 4 | James Brown | Canada |
Table:
| order_id | customer_id | product_id | quantity | order_date |
|---|---|---|---|---|
| 1001 | 1 | 105 | 60 | 01/15/2022 00:00:00 |
| 1002 | 1 | 105 | 50 | 02/10/2022 00:00:00 |
| 1003 | 2 | 105 | 120 | 03/22/2022 00:00:00 |
| 1004 | 3 | 107 | 30 | 02/18/2022 00:00:00 |
| 1005 | 4 | 105 | 80 | 01/28/2022 00:00:00 |
Answer:
Your SQL query would look like this:
This query filters the table for orders of product made within the specified date range, groups by and filters for total ordered quantity greater than . This result is joined with the table to add customer details, particularly the , and the final step is to filter for customers who live in the . Thus, the query returns the names of customers from the who ordered more than quantities of product in the period from to .
SQL QUESTION 7: What are the different normal forms (NF)?
The three levels of database normalization (also known as normal forms) are:
First Normal Form (1NF):
- Each column should contain the same type of data (no mixing strings vs. integers)
- Each column in a table contains a single value (no lists or containers of data)
- Each row in the table is unique
Second Normal Form (2NF)
- It's in 1st Normal Form
- All non-key attributes are dependent on the primary key
Said another way, to achieve 2NF, besides following the 1NF criteria, the columns should also be dependent only on that table's primary key.
Third normal form (3NF) if it meets the following criteria:
- It's in 2nd Normal Form
- There are no transitive dependencies in the table.
A transitive dependency means values in one column are derived from data in another column. For example, it wouldn't make sense to keep a column called ""vehicle's age"" and ""vehicle's manufacture date" (because the vehicle's age can be derived from the date it was manufactured).
Although there is a 4NF and 5NF, you probably don't need to know it for the Howmet Aerospace interview.
SQL Question 8: Highest Producing Aerospace Engine Manufacturer
Given a database of engine production logs, write a query to identify the engine manufacturer that produced the most engines for Howmet Aerospace in a specific year. Assume we are interested in the year 2020. The results should be grouped by manufacturer and include a count of engines produced.
Example Input:
| log_id | manufacture_date | engine_id | manufacturer |
|---|---|---|---|
| 1012 | 01/10/2020 | E110 | ManufacturerA |
| 2045 | 02/18/2020 | E729 | ManufacturerB |
| 3040 | 03/15/2020 | E382 | ManufacturerC |
| 4072 | 04/22/2020 | E992 | ManufacturerA |
| 5079 | 05/28/2020 | E721 | ManufacturerB |
| 6101 | 06/12/2020 | E820 | ManufacturerA |
| 7156 | 07/03/2020 | E213 | ManufacturerC |
| 8139 | 08/19/2020 | E779 | ManufacturerB |
Example Output:
| manufacturer | num_of_engines |
|---|---|
| ManufacturerA | 3 |
| ManufacturerB | 3 |
| ManufacturerC | 2 |
Answer:
This SQL query filters the table for records where is in the year 2020. Then, it groups records by and counts the number of records in each group. The clause orders the output by the count of engines, in descending order.
SQL Question 9: Analyzing Customer Purchases and Product Inventory
As a Data Analyst at Howmet Aerospace, you are tasked with analyzing customer behavior to understand which types of products are being purchased most often. In this task, you are required to join a table that logs each purchase a customer makes, with a table that contains details about each product.
Your task is to write a SQL query to find out which category of products is the most popular in terms of the number of units sold.
Example Input:
| purchase_id | customer_id | product_id | units_sold | purchase_date |
|---|---|---|---|---|
| 3719 | 156 | 2022 | 3 | 05/01/2022 00:00:00 |
| 5407 | 389 | 5045 | 1 | 05/14/2022 00:00:00 |
| 2938 | 675 | 3066 | 5 | 06/10/2022 00:00:00 |
| 6428 | 452 | 2022 | 2 | 07/18/2022 00:00:00 |
| 7198 | 921 | 3066 | 1 | 08/05/2022 00:00:00 |
Example Input:
| product_id | product_name | category | price |
|---|---|---|---|
| 2022 | Aero Engine S1 | Engines | $15000 |
| 5045 | Titanium Wing L1 | Wings | $20000 |
| 3066 | Aluminium Fuselage F1 | Fuselages | $35000 |
Answer:
This query joins the table with the table on the common field. It then groups by the field in the table and calculates the total number of units sold for each category using the SUM() function. The results are ordered in descending order of , and only the top row (i.e., the category with the most units sold) is returned using the LIMIT clause.
Since joins come up so often during SQL interviews, try this interactive Snapchat JOIN SQL interview question:
Preparing For The Howmet Aerospace SQL Interview
The best way to prepare for a SQL interview, besides making sure you have strong SQL fundamentals, is to practice a ton of real SQL questions that were asked in recent job interviews. Besides solving the above Howmet Aerospace SQL interview questions, you should also solve the 200+ SQL exercises on DataLemur which come from companies like FAANG tech companies and tech startups.
Each exercise has hints to guide you, step-by-step solutions and most importantly, there's an online SQL coding environment so you can instantly run your query and have it checked.
To prep for the Howmet Aerospace SQL interview it is also helpful to practice SQL questions from other defense & aerospace contractors like:
But if your SQL query skills are weak, don't worry about jumping right into solving questions – strengthen your SQL foundations with this DataLemur SQL Tutorial.
This tutorial covers topics including LEAD/LAG window functions and AND/OR/NOT – both of which show up frequently during SQL interviews at Howmet Aerospace.
Howmet Aerospace Data Science Interview Tips
What Do Howmet Aerospace Data Science Interviews Cover?
For the Howmet Aerospace Data Science Interview, in addition to SQL query questions, the other types of questions which are covered:
- Probability & Statistics Questions
- Coding Questions in Python or R
- Product Analytics Questions
- Machine Learning and Predictive Modeling Questions
- Behavioral Interview Questions
How To Prepare for Howmet Aerospace Data Science Interviews?
To prepare for Howmet Aerospace Data Science interviews read the book Ace the Data Science Interview because it's got:
- 201 interview questions sourced from companies like Microsoft, Google & Amazon
- a crash course covering Python, SQL & ML
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