# 11 KLA SQL Interview Questions (Updated 2024)

Updated on

February 6, 2024

At KLA, SQL is used across the company for analyzing production efficiency within chip inspection systems, and for defect identification. As such, KLA LOVES to ask SQL query questions in interviews for Data Science, Data Engineering and Data Analytics jobs.

In case you're stressed about an upcoming KLA SQL Assessment, here’s 11 KLA SQL interview questions to practice – how many can you solve?

## 11 KLA SQL Interview Questions

### SQL Question 1: Top Semiconductor Products by Test Score

KLA conducts multiple tests on their semiconductor products to ensure their reliability and effectiveness. An aggregate "test score" is calculated for each product based on various criteria. Calculate the product with the highest average test score for each month using window functions.

Here's a hypothetical set of data:

#### Sample Input:

test_idproduct_idtest_datetest_score
1A12022-01-0389
2B22022-01-2095
3A12022-01-3090
4B22022-02-1591
5B22022-02-2893
6C32022-02-0196
7C32022-02-1097

#### Expected Output:

monthbest_productavg_score
1A189.5
2C396.5

This window function first partitions the data by month and product id and then calculates the average test score for each product in each month. The QUALIFY statement is then used to select the row with the highest average test score for each month.

The first window function finds the average test score for each product in each month. The second window function ranks these averages within each month, with the product having the highest average test score getting a rank of 1. The QUALIFY clause restricts the output to these top-ranked products.

Pro Tip: Window functions are a popular SQL interview topic, so practice every window function problem on DataLemur

### SQL Question 2: Quality Control Per Machine

At KLA, one measure of machine efficiency and reliability is the average time spent on quality control checks. For this interview question, you are asked to find the average time (in minutes) spent per machine on quality control checks per week.

For this task, we have a table that tracks each quality control check for the machines.

##### Example Input:
check_idmachine_idcheck_datetime_spent (minutes)
1A107/14/202230
2A207/14/202245
3A107/15/202235
4A207/16/202250
5A107/17/202225
6A107/18/202240

You need to calculate the average time spent on quality control per machine per week.

The query gets the machine_id, the week_number of and the average from the table and groups them by and . The function extracts the week from the . Then, it orders the output by machine_id and week_number. The function is used to calculate the average time spent per machine per week.

To practice a very similar question try this interactive Amazon Server Utilization Time Question which is similar for calculating time of machine usage or this Tesla Unfinished Parts Question which is similar for tracking manufacturing process.

### SQL Question 3: Can you explain the difference between and ?

The clause serves as a filter for the groups created by the clause, similar to how the clause filters rows. However, is applied to groups rather than individual rows.

For example, say you were a data analyst at KLA trying to understand how sales differed by region:

This query retrieves the total sales for all products in each region, and uses the clause to only sales made after January 1, 2023. The rows are then grouped by region and the clause filters the groups to include only those with total sales greater than \$400,000.

### SQL Question 4: Clickthrough and Conversion Rates

As a data analyst at KLA, you are tasked with computing the click-through rate and conversion rates of digital ads and products. The click-through rate is defined as the number of clicks on a specific ad divided by the number of views of that ad. The conversion rate is defined as the number of times a product viewed was added to the cart divided by the number of views of that product.

For this problem, we would need two separate tables: and .

Here are the tables for sample input:

##### Example Input:
0012022-08-10101true
0022022-08-10102false
0012022-08-11103false
0012022-08-11104true
0022022-08-12105true
##### Example Input:
P012022-08-10201true
P022022-08-10202false
P012022-08-11203false
P012022-08-11204true
P022022-08-12205true

You are required to compute the click-through rates for each ad and the conversion rates for each product and provide the output for each ad and product respectively.

0010.50
0020.50
##### Example Output:
product_idconversion_rate
P010.669
P020.50

In this query, for both tables, we use the aggregate function to count the number of times the or field is true and divide it by the total count (represented by ) to get the click-through and conversion rates respectively. We use the statement to calculate these rates for each individual ad_id and product_id. The output is rounded to two decimal places to represent the percentage.

To solve a similar problem about calculating rates, solve this TikTok SQL Interview Question on DataLemur's interactive coding environment:

### SQL Question 5: What's the difference between a unique and 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.

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.

Unique Indexes are blazing fast. Non unique indexes can improve query performance, but they are often slower because of their non-unique nature.

### SQL Question 6: Average Inspection Time Per Machinery Type

In KLA Corporation, one of the leading semiconductor companies, equipment (machinery) inspection is a regular process. Different machinery types take different amounts of time to inspect. As the data analyst, your task is to find the average inspection time for each type of machinery in the machinery_inspection table.

Please provide the output for the month in which the inspection is maximum.

##### Example Input:
inspection_idinspection_datemachinery_idmachinery_typeinspection_time (in hours)
10101/05/2022 00:00:00201Type12
10203/08/2022 00:00:00202Type21.5
10305/01/2022 00:00:00205Type11
10405/14/2022 00:00:00204Type33
10503/12/2022 00:00:00204Type32
##### Example Output:
monthmachinery_typeavg_inspection_time
1Type12.00
3Type21.50
3Type32.00
5Type11.00
5Type33.00

This SQL query will group the inspections based on month and machinery type, then calculate the average inspection time for each grouped category. The result is then sorted in descending order based on the average inspection time, giving us the month and machinery type with the maximized inspection time first. undefined

### SQL Question 7: What does it mean to use a UNIQUE constraint in a database?

A UNIQUE constraint ensures that all values in a column are different. It is often used in conjunction with other constraints, such as NOT NULL, to ensure that the data meets certain conditions.

For example, if you had KLA employee data stored in a database, here's some constraints you'd use:

In the KLA employee example, the UNIQUE constraint is applied to the "email" field to ensure that each employee has a unique email address. This helps to ensure the integrity of the data in the database and prevents errors that could occur if two employees had the same email address.

### SQL Question 8: Filter Customer Records based on Name Pattern

KLA Inc. wants to filter their customer records to find which customers have names that start with the letter 'J'. They want you to write an SQL query that will take their customer's table and return the names and contact details of the customers whose names begin with 'J'.

#### Table:

customer_idfirst_namelast_nameemailphone
1021JacobSmithjacobsmith@email.com555-198-234
1493MariaJohnsonmarijohnson@email.com555-627-829
1982JamesLeejameslee@email.com555-156-982
2104EmilyTayloremilytaylor@email.com555-635-729
3012JessicaBrownjessicabrown@email.com555-463-172
3157JasonMartinjasonmartin@email.com555-391-698

#### Expected Output:

first_namelast_nameemailphone
JacobSmithjacobsmith@email.com555-198-234
JamesLeejameslee@email.com555-156-982
JessicaBrownjessicabrown@email.com555-463-172
JasonMartinjasonmartin@email.com555-391-698

The SQL query uses the LIKE keyword with a wildcard character '%' to filter out names that start with the letter 'J'. The '%' character is used to represent zero, one or multiple characters in SQL. undefined

### SQL Question 9: Analyze Customer and Orders Data

For KLA company, we have two tables - and . The table holds information such as customer id, name and their region code. The table maintains order information like order id, customer id, product id, amount, and order date.

Write a SQL query to find the total amount spent by each customer along with their name and region code. Also, calculate the average amount spent by customers on their orders for each unique region.

##### (Sample Input)
customer_idnameregion_code
1John100
2Sarah200
3Maria100
4Mike200
5Carla300
##### (Sample Input)
order_idcustomer_idproduct_idamountorder_date
111005002022-01-01
222004002022-01-01
313007002022-01-02
432006002022-01-02
543008002022-01-02

In PostgreSQL:

The above query first creates a CTE that groups the joined and tables by customer name and region code and calculates the total amount of orders for each customer. Then, it creates another CTE , which calculates the average amount of orders for each region code from . Finally, these results are joined on the common .

Since joins come up frequently during SQL interviews, try this interactive Snapchat SQL Interview question using JOINS:

### SQL Question 10: What's a database view, and when would you use one?

Database views are created to provide customized, read-only versions of your data that you can query just like a regular table. So why even use one if they're so similar to a regular table?

Views are advantageous for several reasons:

• views allow you to create a simpler versions of your data for specific users (such as hiding extraneous columns/rows from business analysts since they're relics of the Data Engineering pipelines setup)
• views help you comply with data security requirements by hiding sensitive data from certain users (important for regulated industries like govermnet and healthcare!)
• views can improve performance for complicated queries by pre-computing the results and caching them in a view (which is often faster than re-executing the original query)

### SQL Question 11: Calculation of Average Power Consumption

KLA has multiple sites operating various equipment. Each equipment has different power consumption each day. Can you write an SQL query to calculate the average power consumption, rounded to 2 decimal places, for each equipment type per site for the year 2022? Also, find out the equipment with the highest average power consumption for each site.

###### Example Input:
usage_idsite_idequipment_typeusage_datepower_consumption
12451'a'01/05/2022 00:00:00350
13891'a'01/06/2022 00:00:00400
15981'b'01/07/2022 00:00:00480
21452'b'02/12/2022 00:00:00300
28762'a'02/14/2022 00:00:00430
34082'b'02/15/2022 00:00:00500

This query will first calculate the average power consumption by and for the year 2022 in the CTE. It is then used to find the maximum average power consumption per site in CTE. Finally the two CTEs are joined on and equals to obtain the equipment type with the highest average power per site. The average power consumption is rounded to 2 decimal places as requested.

To practice a very similar question try this interactive Amazon Highest-Grossing Items Question which is similar for finding top items in categories or this Alibaba Compressed Mean Question which is similar for calculating average in a scope.

### Preparing For The KLA 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. In addition to solving the above KLA SQL interview questions, you should also solve the 200+ SQL questions from real Data Science & Analytics interviews which come from companies like FAANG (FB, Apple, Amazon, Netflix, Google).

Each problem on DataLemur has multiple hints, full answers and most importantly, there's an online SQL code editor so you can right in the browser run your SQL query and have it graded.

To prep for the KLA SQL interview you can also be 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 – improve your SQL foundations with this free SQL tutorial.

This tutorial covers topics including advantages of CTEs vs. subqueries and Self-Joins – both of these pop up often during SQL interviews at KLA.

### KLA Data Science Interview Tips

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

For the KLA Data Science Interview, in addition to SQL query questions, the other types of questions to prepare for are:

• Probability & Stats Questions
• Python or R Programming Questions
• Open-Ended Data Case Studies
• Machine Learning Questions
• Resume-Based Behavioral Questions

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

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

• 201 Interview Questions from FAANG & startups
• A Crash Course on Stats, SQL & ML
• Amazing Reviews (900+ reviews, 4.5-star rating)