8 Applied Materials SQL Interview Questions (Updated 2024)
At Applied Materials, SQL does the heavy lifting for analyzing semiconductor manufacturing data, and for optimizing material requirement forecasts in the supply chain management process. Because of this, Applied Materials often tests SQL questions in interviews for Data Science, Analytics, and & Data Engineering jobs.
In case you're studying for a SQL Interview, here’s 8 Applied Materials SQL interview questions to practice, which are similar to commonly asked questions at Applied Materials – how many can you solve?
8 Applied Materials SQL Interview Questions
SQL Question 1: Average Monthly Revenue for Each Product
Applied Materials is a company that provides manufacturing solutions for the semiconductor, flat panel display and solar photovoltaic industries. From their sales table, they want to know the average monthly revenue for each product.
Assume they have a sales table that holds the following sample data:
Example Input:
| sales_id | product_id | sale_date | quantity | price_per_unit |
|---|---|---|---|---|
| 1 | 1001 | 2022-01-03 | 20 | 50.00 |
| 2 | 1002 | 2022-02-04 | 15 | 30.00 |
| 3 | 1001 | 2022-02-15 | 25 | 50.00 |
| 4 | 1003 | 2022-03-07 | 10 | 90.00 |
| 5 | 1002 | 2022-03-28 | 20 | 30.00 |
| 6 | 1001 | 2022-04-12 | 30 | 50.00 |
| 7 | 1003 | 2022-04-25 | 15 | 90.00 |
Answer:
This PostgreSQL query first truncates the sale_date to the month level. It then calculates the total revenue for each sale (quantity * price_per_unit). The function is used in conjunction with a window function to calculate the average monthly revenue, partitioned by product and ordered by month. The final result is ordered by product_id and month.
p.s. Window functions show up super frequently during SQL interviews, so practice the 27+ window function questions on DataLemur
SQL Question 2: Filter Customer Orders based on Specific Conditions
Given a database of customers and their orders from Applied Materials, write a SQL query to filter out the customer records who ordered more than 5 products during the month of June, 2022 and their total order price was above 1500 USD.
Assume the following table structure of two tables, and :
Example Input:
| customer_id | customer_name | customer_email |
|---|---|---|
| 001 | John Doe | john@doe.com |
| 002 | Jane Smith | jane@smith.com |
| 003 | Sarah Connor | sarah@connor.com |
| 004 | Kyle Reese | kyle@reese.com |
Example Input:
| order_id | customer_id | order_date | product_id | quantity | price |
|---|---|---|---|---|---|
| 1001 | 001 | 06/02/2022 | 50001 | 3 | 500 |
| 1002 | 002 | 06/10/2022 | 69852 | 6 | 300 |
| 1003 | 001 | 06/18/2022 | 50001 | 9 | 100 |
| 1004 | 003 | 06/20/2022 | 50001 | 6 | 300 |
| 1005 | 001 | 06/25/2022 | 69852 | 5 | 200 |
| 1006 | 004 | 06/26/2022 | 50001 | 7 | 350 |
Answer:
Use SQL WHERE and AND to filter on multiple conditions:
The above Postgresql statement will join and tables on the field, filter on the condition that the order month is June, 2022, and only show the rows where the customers have ordered more than 5 products and the total order price is greater than 1500 USD. The clause is needed to group the aggregations ( and ) by each customer. The clause is used to filter out the groups that don't meet the conditions after the clause. undefined
SQL Question 3: What does it mean to perform a self-join?
A self-join is a type of JOIN where a table is joined to itself. To execute a self-join, you must include the table name twice in the FROM clause and assign a different alias to each instance. You can then join the two copies of the table using a JOIN clause, and use a WHERE clause to specify the relationship between the rows.
For example, say you had website visitor data for Applied Materials, exported from the company's Google Analytics account. In support of the web-dev team, you had to analyze pairs of pages for UX or navigational issues. As part of that analysis, you wanted to wanted to generate all pairs of URLs, but needed to avoid pairs where both the URLs were the same since that's not a valid pair.
The self-join query woulld like the following:
This query returns the url of each page () along with the url of the page that referred to it (). The self-join is performed using the field, which specifies the id of the page that referred the visitor to the current page, and avoids any pages that referred themself (aka data anomalies).
SQL Question 4: Calculate Click-through-Rate for Applied Materials Online Ads
Applied Materials, a provider of material engineering solutions, relies heavily on digital marketing to advertise its products to potential customers. The company measures the success of its ad campaigns by analyzing the click-through rates (CTR). The click-through rate is defined as the number of clicks an ad gets divided by the number of times the ad is shown (impressions), expressed as a percentage.
The data is stored in two tables and .
Example Input:
Example Input:
The task is to compute the click-through rate for each campaign.
Answer:
This query joins the and table on the field. counts the number of unique ad clicks, and counts the number of times the ad was displayed. The click-through rate (expressed as a percentage) is then calculated by diving the total clicks by total impressions. This operation is performed for each campaign.
To practice a related SQL problem on DataLemur's free interactive coding environment, try this SQL interview question asked by Facebook:
SQL Question 5: What's the purpose of the the command?
Similar to the and / operators, the PostgreSQL INTERSECT operator combines result sets of two or more statements into a single result set. However, only returns the rows that are in BOTH select statements.
For a concrete example, say you were on the Sales Analytics team at Applied Materials, and had data on sales leads exported from both HubSpot and Salesforce CRMs in two different tables. To write a query to analyze leads created after 2023 started, that show up in both CRMs, you would use the command:
SQL Question 6: Analyze Customer Orders and Join with Products Table
Suppose we have a customer database and product database in Applied Materials. The tables are provided as follows:
Example Input:
| customer_id | name | |
|---|---|---|
| 1 | John Doe | john_doe@email.com |
| 2 | Jane Smith | jane_smith@email.com |
| 3 | Mary Johnson | mary_johnson@email.com |
| 4 | James Brown | james_brown@email.com |
Example Input:
| order_id | customer_id | order_date | product_id | quantity |
|---|---|---|---|---|
| 1001 | 1 | 2022-06-11 | 2001 | 2 |
| 1002 | 2 | 2022-06-12 | 2002 | 1 |
| 1003 | 1 | 2022-06-13 | 2001 | 3 |
| 1004 | 3 | 2022-06-14 | 2003 | 1 |
| 1005 | 4 | 2022-06-15 | 2001 | 1 |
Example Input:
| product_id | product_name | product_category |
|---|---|---|
| 2001 | Product A | Semiconductors |
| 2002 | Product B | Solar Panels |
| 2003 | Product C | Semiconductors |
The task is to write a SQL query that returns each customer's name, total number of orders and the most ordered product category.
Answer:
This query joins the , , and tables on their respective id columns. It groups the result by customer name and product category to calculate the total number of orders for each category. Finally, the result is sorted by the total orders in descending order to get the most ordered product category for each customer.
Since joins come up frequently during SQL interviews, try an interactive Spotify JOIN SQL question:
SQL Question 7: Can you describe the concept of a database index and the various types of indexes?
In a database, an index is a data structure that improves the speed of data retrieval operations on a database table at the cost of additional writes and the use of more storage space to maintain the index data structure.
There are several types of indexes that can be used in a database:
- Primary index: a unique identifier for each row in a table and is used to access the row directly.
- Unique index: used to enforce the uniqueness of the indexed columns in a table. It does not allow duplicate values to be inserted into the indexed columns.
- Composite index: is created on multiple columns of a table. It can be used to speed up the search process on the combination of columns.
- Clustered index: determines the physical order of the data in a table. There can be only one clustered index per table.
- Non-clustered index: does NOT determine the physical order of the data in a table. A table can have multiple non-clustered indexes.
SQL Question 8: Production Quality Monthly Report
As a data analyst for Applied Materials, your task is to prepare a monthly report to gauge the quality of products being manufactured from three different machines. Each machine produces a unique product and its production cycle ends every hour, recording the total units produced and total defective units.
Here is the structure of a table reflecting this scenario:
Example Input:
| log_id | machine_id | product_id | cycle_end_time | total_units | defective_units |
|---|---|---|---|---|---|
| 1001 | 1 | A | 2022-09-09 07:00:00 | 500 | 10 |
| 1002 | 2 | B | 2022-09-09 08:00:00 | 450 | 20 |
| 1003 | 1 | A | 2022-09-09 09:00:00 | 510 | 15 |
| 1004 | 3 | C | 2022-09-09 10:00:00 | 400 | 5 |
| 1005 | 2 | B | 2022-09-10 07:00:00 | 460 | 20 |
Your question is to write a SQL query to get a monthly report that shows the average defect rate, defined as (sum of defective_units / sum of total_units), for each product. The report should present this information per each month of the year.
Example Output:
| month | product | average_defect_rate |
|---|---|---|
| 9 | A | 0.0252 |
| 9 | B | 0.0435 |
| 9 | C | 0.0125 |
Answer:
This query uses the function to get the month portion from the . It then calculates the defect rate by dividing the sum of by the sum of for each . The function is used to limit the result to 4 decimal places. The statement groups the result by and . Finally, the clause is used to sort the results in ascending order by and . undefined
How To Prepare for the Applied Materials SQL Interview
Assuming that you've already got basic SQL skills, the next best tip we have to prepare for the Applied Materials SQL interview is to solve as many practice SQL interview questions as you can!
Beyond just solving the above Applied Materials SQL interview questions, you should also solve the 200+ SQL questions from real Data Science & Analytics interviews which come from companies like Google, Facebook, Microsoft and Amazon.
Each problem on DataLemur has hints to guide you, step-by-step solutions and crucially, there is an interactive SQL code editor so you can easily right in the browser your SQL query answer and have it graded.
To prep for the Applied Materials SQL interview it is also wise to solve SQL questions from other tech companies like:
In case your SQL coding skills are weak, don't worry about jumping right into solving questions – go learn SQL with this DataLemur SQL tutorial.
This tutorial covers SQL topics like math functions like ROUND()/CEIL() and WHERE with AND/OR/NOT – both of which come up routinely during Applied Materials SQL interviews.
Applied Materials Data Science Interview Tips
What Do Applied Materials Data Science Interviews Cover?
For the Applied Materials Data Science Interview, besides SQL questions, the other types of questions to practice:
- Probability & Statistics Questions
- Coding Questions in Python or R
- Data Case Study Questions
- ML Modelling Questions
- Behavioral Interview Questions
How To Prepare for Applied Materials Data Science Interviews?
The best way to prepare for Applied Materials Data Science interviews is by reading Ace the Data Science Interview. The book's got:
- 201 Interview Questions from FAANG (FB, Apple, Amazon, Netflix, Google)
- A Crash Course covering Python, SQL & ML
- Amazing Reviews (900+ 5-star reviews on Amazon)