At Veeco, SQL is used day-to-day for analyzing manufacturing data for optimizing production processes, and querying customer databases for targeted marketing strategies. Unsurprisingly this is why Veeco Instruments often tests SQL coding questions in interviews for Data Analyst, Data Science, and BI jobs.
As such, to help you practice for the Veeco Instruments SQL interview, we'll cover 10 Veeco SQL interview questions – how many can you solve?
Veeco Instruments presents great importance to power users who regularly purchase their high-tech microdevices and accessories. These VIP customers contribute significantly to the company's sales and are crucial for the business.
We want to identify top 5 customers who have made the highest purchases in the last 12 months. Write a SQL query to find these power users. Consider the 'orders' table with the following structure and example data:
order_id | customer_id | order_date | product_id | product_price |
---|---|---|---|---|
3787 | 456 | 2020-08-15 | 30050 | 500 |
9092 | 778 | 2020-09-12 | 40452 | 900 |
2342 | 981 | 2021-06-09 | 40001 | 300 |
5223 | 456 | 2021-04-15 | 35452 | 250 |
8462 | 987 | 2021-07-29 | 40001 | 300 |
Assuming that each order consists of one unit of the product, the total purchase by a customer is calculated by summing up the product price of their orders.
Here is the PostgreSQL query that would solve the problem:
This query filters the orders placed in the last year, then groups them by customer id. For each customer, it calculates the total purchase amount. The 'ORDER BY' clause sorts the customers in the descending order of total purchases, and the 'LIMIT' clause restricts the output to the top 5 customers.
To solve a related super-user data analysis question on DataLemur's free online SQL code editor, try this Microsoft Teams Power User SQL Interview Question:
Veeco Instruments Inc. is a leading global semiconductor manufacturing company which relies on vast amount processing and testing equipment.
In the dataset provided, we have a table called 'equipment_logs' which keeps logs of different pieces of equipment used for production. The table showcases an equipment's id, type, performance rating (a score out of 5), and the date on which this rating is logged.
The performance rating indicates how well an equipment is working. A lower score means the equipment needs maintenance or replacement.
Your task is to write a SQL query that calculates the monthly average performance rating of each type of equipment using window functions.
log_id | equipment_id | equipment_type | performance_rating | log_date |
---|---|---|---|---|
1 | 22 | 'vacuum_pump' | 4 | 2022-04-15 |
2 | 22 | 'vacuum_pump' | 5 | 2022-05-10 |
3 | 46 | 'annealing_oven' | 3 | 2022-04-21 |
4 | 46 | 'annealing_oven' | 1 | 2022-05-18 |
5 | 46 | 'annealing_oven' | 2 | 2022-05-28 |
6 | 54 | 'photo_stabilizer' | 5 | 2022-05-15 |
Using PostgreSQL, we can solve this task as follows:
This query first extracts the month from the "log_date" column and assigns it to a new column named "month". Then it calculates the average performance rating of each type of equipment for each month. The PARTITION BY clause is used to divide the data into partitions, based on equipment type and month, so that the average can be computed for each partition separately. The ORDER BY clause is used to sort the output by month and equipment type.
NOTE: The actual average performance scores might need to be formatted or rounded off as needed.
Pro Tip: Window functions are a frequent SQL interview topic, so practice every window function problem on DataLemur
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:
Veeco Instruments wants to identify customers who have purchased specific types of instruments more than three times in the last year and are located in New York. The instruments of interest are categorized as 'Type A' and 'Type B'. Given the customer and purchase data, write a query to retrieve the relevant customer details.
customer_id | first_name | last_name | state |
---|---|---|---|
001 | John | Doe | New York |
002 | Jane | Doe | California |
003 | Jim | Beam | New York |
004 | Jack | Daniels | Texas |
purchase_id | customer_id | product_type | purchase_date |
---|---|---|---|
0001 | 001 | Type A | 01/07/2022 |
0002 | 001 | Type B | 02/10/2022 |
0003 | 001 | Type A | 03/12/2022 |
0004 | 001 | Type A | 12/01/2022 |
0005 | 002 | Type B | 06/15/2022 |
0006 | 003 | Type A | 05/20/2022 |
0007 | 004 | Type B | 04/30/2022 |
0008 | 001 | Type B | 08/22/2022 |
0009 | 001 | Type A | 09/19/2022 |
This query first filters the customers who live in New York. Then, it filters out the customers who have purchased 'Type A' or 'Type B' products more than three times in the last year. The clause groups the purchases by , and the clause filters out the customers based on the specified conditions.
Imagine you've got giant AF jigsaw puzzle with thousands of pieces, and each piece represents a piece of data in your database. You've spent hours organizing the puzzle into neat little piles, with each pile representing a table in your database. This process is called normalization, and it's a great way to make your database efficient, flexible, and easy to maintain.
But what if you want to solve the puzzle faster (aka make your queries run faster?)?
That's where denormalization comes in – Denormalization is like the puzzle-solving equivalent of taking a shortcut!
Instead of putting all the pieces in separate piles, you might decide to clone some of the pieces, and then have that one puzzle piece be put into multiple piles. Clearly, we are breaking the rules of physics, but that's just like de-normalization because it breaks the normal rules of normalization (1st, 2nd, 3rd normal forms).
By adding redundant puzzle pieces, it can be easier to find the pieces you need, but it also means that you have to be extra careful when you're moving pieces around or adding new ones (aka INSERT/UPDATE commands become more complex).
On the plus side, denormalization can improve the performance of your database and make it easier to use. On the downside, it can make your database more prone to errors and inconsistencies, and it can be harder to update and maintain. In short, denormalization can be a helpful tool, but it's important to use it wisely!
As an analyst at Veeco Instruments - a leading company manufacturing and providing process equipment solutions that enable the manufacture of LEDs, power electronics, hard disk drives, MEMS, semiconductors, and optoelectronics - you are tasked with a critical job. To optimize production, you have to analyze the average time taken to produce different types of units in the company's various production lines.
Please write a SQL query that finds the average production time per unit type across all Veeco's production lines using the tables provided below:
production_id | line_id | start_time | end_time | unit_type |
---|---|---|---|---|
115 | 1 | 08/01/2022 08:00:00 | 08/01/2022 16:00:00 | Type 1 |
349 | 2 | 08/01/2022 08:30:00 | 08/01/2022 17:30:00 | Type 2 |
264 | 3 | 08/01/2022 08:00:00 | 08/01/2022 17:00:00 | Type 3 |
420 | 1 | 08/02/2022 08:00:00 | 08/02/2022 16:00:00 | Type 1 |
513 | 2 | 08/02/2022 08:30:00 | 08/02/2022 17:30:00 | Type 2 |
This query is using the function to calculate the average duration of production for each . The function is used to convert the time interval between and into seconds, and then it's divided by 3600 to get the average production time in hours. The result is grouped by .
unit_type | avg_prod_time_hours |
---|---|
Type 1 | 8.00 |
Type 2 | 9.00 |
Type 3 | 9.00 |
Here, you can see the average production times for each type of unit across all production lines. |
Both the and window functions are used to access a row at a specific offset from the current row.
However, the function retrieves a value from a row that follows the current row, whereas the function retrieves a value from a row that precedes the current row.
Often, the offset for both functions is 1, which gives access to the immediately following/preceding row. Here's a SQL query example:
Veeco Instruments specializes in the manufacture of industrial hardware. They have many products categorized in different product types. Each product goes through multiple stages of production before it is ready for distribution.
Suppose you're given a database which contains two tables. The first one is and it contains information about each product such as , and . The second table is and it contains information about the production costs per product for various stages of production.
Your task for this interview question is to write a SQL query which calculates the average production cost for each category of products.
product_id | product_category | product_name |
---|---|---|
1 | Microscopes | Microscope A |
2 | Microscopes | Microscope B |
3 | Lithography Systems | System A |
4 | Lithography Systems | System B |
id | product_id | stage | cost |
---|---|---|---|
1 | 1 | Manufacturing | 1000 |
2 | 1 | Packaging | 200 |
3 | 1 | Quality Control | 300 |
4 | 2 | Manufacturing | 1200 |
5 | 2 | Packaging | 250 |
6 | 2 | Quality Control | 350 |
7 | 3 | Manufacturing | 4000 |
8 | 3 | Packaging | 500 |
9 | 3 | Quality Control | 700 |
product_category | avg_production_cost |
---|---|
Microscopes | 1075.00 |
Lithography Systems | 2200.00 |
This query first performs a JOIN operation to combine the data from both the and tables. It then groups the data by the product category and uses the AVG function to calculate the average cost of production for each category.
Veeco Instruments is interested in analyzing their customers who have registered with a particular email domain, for example, . From the customer records database, provide a detailed list of all the customers who have used this domain.
customer_id | first_name | last_name | date_of_joining | |
---|---|---|---|---|
101 | John | Doe | john_doe@gmail.com | 01/08/2019 |
102 | Sara | Miller | sara_miller@yahoo.com | 05/10/2020 |
103 | Tom | Cruise | tom_cruise@gmail.com | 18/03/2021 |
104 | Emma | Watson | emma_watson@hotmail.com | 26/07/2021 |
105 | Robert | Dawney | robert_dawney@gmail.com | 05/12/2021 |
You can find these customers by querying the database with a SQL command using the keyword. Here is an example of how you can approach this:
With this query, you'll be able to search the customers' table and find all the records where the column contains the text . This is done using the LIKE keyword followed by a string in the WHERE clause, where the '%' character acts as a wildcard, representing any sequence of characters.
The output will be all the rows that contain in the email column. So, the customers John Doe, Tom Cruise, and Robert Dawney will be listed.
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 Veeco Instruments'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.
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. In addition to solving the above Veeco Instruments SQL interview questions, you should also solve the 200+ SQL questions from real Data Science & Analytics interviews which come from companies like Facebook, Google and unicorn tech startups.
Each interview question has hints to guide you, detailed solutions and best of all, there's an interactive coding environment so you can right online code up your SQL query answer and have it checked.
To prep for the Veeco Instruments SQL interview it is also wise to practice SQL problems from other tech companies like:
But if your SQL foundations are weak, forget about going right into solving questions – improve your SQL foundations with this SQL tutorial for Data Analytics.
This tutorial covers SQL concepts such as Subqueries and filtering strings using LIKE – both of which pop up frequently in Veeco Instruments SQL assessments.
In addition to SQL interview questions, the other question categories to prepare for the Veeco Instruments Data Science Interview are:
The best way to prepare for Veeco Instruments Data Science interviews is by reading Ace the Data Science Interview. The book's got: