# 9 Arm Holdings SQL Interview Questions

Updated on

February 5, 2024

At Arm Holdings, SQL crucial for hardware performance data, and for managing data associated with chip manufacturing. Because of this, Arm Semiconductor asks SQL problems during interviews for Data Science and Data Engineering positions.

So, if you're trying to prepare for the SQL Interview, we've curated 9 Arm Holdings SQL interview questions to practice, which are similar to recently asked questions at Arm Semiconductor – how many can you solve?

## 9 Arm Holdings SQL Interview Questions

### SQL Question 1: Analyze Monthly Chip Sales

As a Data Analyst at Arm Semiconductor, you are tasked with analyzing chip sales. Write a SQL query which returns the top 3 chip models with the highest revenue for each month. For each month and chip, return the total revenue and unit counts. Assume the 'sales' table is structured as follows:

##### Example Input:
Sale_idDateChip_modelUnits_soldRevenue
101/01/2022Chip_A10500
202/01/2022Chip_B7350
301/01/2022Chip_B3150
402/01/2022Chip_A15750
501/01/2022Chip_C8400
603/01/2022Chip_B5250
701/02/2022Chip_C10500
803/02/2022Chip_B15750
903/01/2022Chip_A201000

Expected Output:

MonthChip_modelUnitsRevenue
01/2022Chip_A10500
01/2022Chip_B3150
01/2022Chip_C8400
02/2022Chip_A15750
02/2022Chip_B7350
03/2022Chip_A201000
03/2022Chip_B201000

This SQL query first creates a CTE (common table expressions) that calculates the number of units sold and total revenue for each chip model in each month. It uses the window function ROW_NUMBER() to assign a unique row number to each row within each partition (each month in this case), ordered by total revenue in descending order. The main query just selects the top 3 rows for each partition (month).

Pro Tip: Window functions are a frequent SQL interview topic, so practice all the window function problems on DataLemur

### SQL Question 2: Filtering Customer Records for ARM Semiconductor

Using the sample data provided, please write an SQL query that returns data about differently categorized customers from the table. A category is defined as follows: customers from the Technological Sector, who have been contacted in the last 6 months, and whose maximum purchase amount exceeds \$5,000. Please return their customer_id, name, sector, date_last_contacted, and max_purchase_amount.

##### Example Input:
customer_idnamesectordate_last_contactedmax_purchase_amount
5012FidelityFinancials2022-06-08\$6500
9842IBMTechnology2022-06-10\$7000
7359NvidiaTechnology2021-06-18\$3200
5623AccentureTelecommunications2021-07-26\$8000
8927InfosysTechnology2022-07-05\$5200

In this query, the WHERE clause is used to filter out customers from the Technology sector, who were contacted within the last 6 months, and whose maximum purchase amount exceeds \$5000. The '>' operator is used to filter out amounts greater than 5000, the AND keyword is used to ensure that all the conditions are met, and finally, the INTERVAL keyword is used in PostgreSQL to filter out dates that are within the last 6 months. undefined

### SQL Question 3: What is a cross-join?

A cross-join, also known as a cartesian join, is a type of join that returns the cross-product of two tables. In a cross-join, each row from the first table is paired with every row from the second table, resulting in a new table that has a row for each possible combination of rows from the two input tables.

For example, say you worked on the Marketing Analytics team at ARM Holdings, and needed to understand what advertising copy (text) goes best with what advertising creative (the photo that gets posted with the advertisement copy).

Here is an example of a cross-join between a table of ad_copy and a table of ad_creative:

You could this query to generate all possible combinations of ad copy and ad creative to help you create more effective ads for Arm Semiconductor. Just be careful: if you had 1,000 rows of ad copy and 1,000 different image creatives, the resulting cross-join would have 1 million rows!

### SQL Question 4: Analyzing Click-Through Rates at ARM Semiconductor

We would like to analyze the click-through rates on Arm's Ads. The goal is to find the total number of clicks per ad, the total number of impressions (how many times the ad was shown), and the click-through-rate (CTR) which is the number of clicks divided by the number of impressions.

Here are two tables to exemplify the situation:

##### Table:

The SQL query to solve this problem would be:

This query is designed to get the click-through rate for each ad. It starts by looking at each unique ad () and it counts the total impressions and total clicks. The ensures that we look at all impressions, even if there were no clicks. We multiply by 1.0 in order to get the result in decimal form. The resulting CTR is then calculated by dividing the number of clicks by the number of impressions for each ad. The results are then ordered in descending order of the click-through rate.

To practice another question about calculating rates, solve this TikTok SQL Interview Question within DataLemur's interactive coding environment:

### SQL Question 5: Why is database normalization a good idea?

There are several advantages to normalizing a database, including less redundancy, more flexibility, and better performance.

• Less Redundancy: Normalization reduces redundancy by breaking down a larger, more general table into smaller, more specific tables. This reduces the amount of data that needs to be accessed for queries.

• More Flexibility: Normalization makes it easier to modify the structure of the database, as there is less redundancy, so it allows you to make changes to one table without affecting others. This makes it easier to adapt the database to changing business needs (a very real reality at Arm Semiconductor!)

• Better Performance: Normalization can improve the performance of the database by reducing the amount of data that needs to be stored and accessed in a single table. This can result in faster query times and better overall performance.

### SQL Question 6: Maximum Sold Product in Different Regions

As an analyst at Arm Semiconductor, you are tasked with identifying the product that has the maximum sales in each region for the last year. You will base your analysis on the and tables.

The table has the following columns:

• (INT): The sale ID.
• (INT): The ID of the product sold.
• (INT): The ID of the region where the product was sold.
• (INT): The quantity of the product sold.

The table has the following columns:

• (INT): The product ID.
• (VARCHAR): The name of the product.
##### Example Input
sale_idproduct_idregion_idquantity
110011200
210021180
310012220
410032240
510023150
##### Example Input
product_idproduct_name
1001ARMv8 Processor
1002Neoverse V1
1003Cortex-M55

The PostgreSQL query you can use to answer this question is as follows:

With this query, you first join the and tables based on the . Then you group the results by and . Finally, you select the product with the maximum quantity sold in each region. undefined

### SQL Question 7: When would you use the constraint?

A is a column or set of columns in a table that references the primary key of another table. It is used to link the two tables together, and to ensure that the data in the foreign key column is valid.

The constraint helps to ensure the integrity of the data in the database by preventing the insertion of rows in the foreign key table that do not have corresponding entries in the primary key table. It also helps to enforce the relationship between the two tables, and can be used to ensure that data is not deleted from the primary key table if there are still references to it in the foreign key table.

For example, if you have a table of Arm Semiconductor customers and an orders table, the customer_id column in the orders table could be a that references the id column (which is the primary key) in the Arm Semiconductor customers table.

### SQL Question 8: Analyzing Customer and Product Interaction

As the database analyst for Arm Semiconductor, you've been tasked to identify the most commonly bought products by each customer. You have two tables available.

• The first table is which consists of , and .
• The second table is that shows , , , and .

Write a SQL query that shows the product that each customer buys most frequently. If there's a tie, show all. Save the result as a new table named .

##### Example Input:
customer_idfirst_namelast_name
456PeterWilliams
789LindaBrown
123JohnSmith
##### Example Input:
sale_idcustomer_idproduct_idsale_datequantity
1004569912022-06-012
1014569922022-06-021
1027899912022-06-013
1031239932022-06-051
1041239932022-06-074
1057899912022-06-082

This PostgreSQL query first generates a table of customers along with the products they buy most frequently. Then it compares the frequency of each product bought by each customer, and selects the product(s) with the highest count. If a customer has multiple 'favorite' products with tie counts, all are selected. The result is saved as a new table .

Because joins come up so often during SQL interviews, try an interactive Spotify JOIN SQL question:

### SQL Question 9: Semiconductor Production Calculations

At Arm Semiconductor, you are given a table that lists various models of chips produced, the power they consume in watts, the voltage they run at in volts, and the current at which they are tested in amps. The table also lists the number of chips produced every day. Your task is to write a SQL query that retrieves the total power dissipated by each model of chip across all the chips made each day and then calculate the average power dissipated over the past 30 days. Round your results to two decimal places.

Moreover, we are also interested in the number of chips whose operational power exceeds twice their nominal power usage. Nominal power can be calculated using P = IV (Power = Current * Voltage). Use this formula for your calculations.

##### Table:
chip_idchip_modelproduction_datepower_wattvoltage_voltcurrent_ampquantity
1ModelA08/10/2022 00:00:002.550.51000
2ModelB08/10/2022 00:00:004.022.01500
3ModelA09/10/2022 00:00:002.550.51200
4ModelC09/10/2022 00:00:001.51.51.0800
5ModelB10/10/2022 00:00:004.022.01400

This SQL query retrieves the average power dissipated by each chip model over the past 30 days, and counts the number of chips whose operational power exceeds twice of their nominal power usage, calculated using the P = IV formula, for each chip model. The ROUND function is used to round the average power to two decimal places. The date clause in the WHERE condition is used to restrict the results to the past 30 days.

To practice a very similar question try this interactive Amazon Server Utilization Time Question which is similar for total calculation across distinct entities or this Tesla Unfinished Parts Question which is similar for examining production calculations.

### Preparing For The Arm Semiconductor SQL Interview

Assuming that you've already got basic SQL skills, the next best tip we have to prepare for the Arm Semiconductor SQL interview is to solve as many practice SQL interview questions as you can! Beyond just solving the above Arm Semiconductor SQL interview questions, you should also solve the 200+ FAANG SQL Questions on DataLemur which come from companies like FAANG (FB, Apple, Amazon, Netflix, Google).

Each DataLemur SQL question has hints to guide you, detailed solutions and crucially, there's an online SQL code editor so you can instantly run your SQL query and have it graded.

To prep for the Arm Semiconductor SQL interview you can also be useful to solve SQL problems from other semiconductor companies like:

But if your SQL foundations are weak, forget about diving straight into solving questions – refresh your SQL knowledge with this SQL tutorial for Data Scientists & Analysts.

This tutorial covers topics including joining multiple tables and GROUP BY – both of these come up frequently during Arm Semiconductor interviews.

### Arm Holdings Data Science Interview Tips

#### What Do Arm Semiconductor Data Science Interviews Cover?

For the Arm Semiconductor Data Science Interview, besides SQL questions, the other types of questions to practice:

• Statistics and Probability Questions
• Python or R Coding Questions
• Data Case Study Questions
• Machine Learning and Predictive Modeling Questions
• Behavioral Based Interview Questions

#### How To Prepare for Arm Semiconductor Data Science Interviews?

To prepare for Arm Semiconductor Data Science interviews read the book Ace the Data Science Interview because it's got:

• 201 interview questions sourced from FAANG, tech startups, and Wall Street
• a refresher on Stats, SQL & ML
• over 900+ 5-star reviews on Amazon