# 10 Apple SQL Interview Questions

Updated on

January 22, 2024

From analyzing iPhone sales data, to managing the music catalog of Apple Music, SQL is used quite frequently at Apple. That's why Apple frequently asks SQL coding questions in interviews for Data Analytics, Data Science, and Data Engineering jobs.

To help you prepare, we've collected 10 Apple SQL interview questions to practice, which are similar to recently asked questions at Apple – how many of them can you solve?

## 10 Apple SQL Interview Questions

### SQL Question 1: Trade In Payouts

Apple has a trade-in program where their customers can return the old iPhone device to Apple and Apple gives the customers the trade-in value (known as payout) of the device in cash.

For each store, write a query of the total revenue from the trade-in. Order the result by the descending order.

#### Table:

Column NameType
transaction_idinteger
model_idinteger
store_idinteger
transaction_datedate

#### Example Input:

transaction_idmodel_idstore_idtransaction_date
111251201/01/2022
211351201/01/2022

#### Table:

Column NameType
model_idinteger
model_namestring
payout_amountinteger

#### Example Input:

model_idmodel_namepayout_amount
111iPhone 11200
112iPhone 12350
113iPhone 13450
114iPhone 13 Pro Max650

#### Example Output:

store_idpayout_total
512800

To solve this problem question on DataLemur's free interactive SQL code editor, try this Apple SQL Interview Question:

### SQL Question 2: Follow-Up Airpod Percentage

The Apple retention team needs your help to investigate buying patterns. Write a query to determine the percentage of buyers who bought AirPods directly after they bought iPhones. Round your answer to a percentage (i.e. 20 for 20%, 50 for 50) with no decimals.

Clarifications:

• The users were interested in buying iPhones and then AirPods, with no intermediate purchases in between.
• Users who buy iPhones and AirPods at the same time, with the iPhone logged first, can still be counted.

Tip:

• Multiply by 100 before you perform the rounding to make sure you get the same answer we did :)

#### Table:

Column NameType
transaction_idinteger
customer_idinteger
product_namevarchar
transaction_timestampdatetime

#### Example Input:

transaction_idcustomer_idproduct_nametransaction_timestamp
1101iPhone08/08/2022 00:00:00
2101AirPods08/08/2022 00:00:00
5301iPhone09/05/2022 00:00:00
7301AirPods09/07/2022 00:00:00

#### Example Output:

follow_up_percentage
50

If you want to try this problem yourself, join DataLemur premium to unlock this question:

### SQL Question 3: Analyzing Apple Product Performance based on Reviews

Given a table containing data related to product reviews submitted by users over time, write a SQL query to calculate the monthly average rating for each Apple product. The table has the following schema:

• : An integer that uniquely identifies each review
• : An integer that identifies the user who submitted the review
• : A datetime value that represents when the review was submitted
• : An integer that identifies the product being reviewed
• : An integer that indicates the number of stars the product received in the review (from 1 to 5)

Please note that for the purposes of this problem, you can assume that the corresponds to an Apple product, and the for each review is in the format "MM/DD/YYYY HH:MI:SS".

##### Example Input:
review_iduser_idsubmit_dateproduct_idstars
617112306/08/2022 00:00:00500014
780226506/10/2022 00:00:00698524
529336206/18/2022 00:00:00500013
635219207/26/2022 00:00:00698523
451798107/05/2022 00:00:00698522
##### Example Output:
mthproductavg_stars
6500013.50
6698524.00
7698522.50

The above query extracts the month from the using the function and groups by it along with the . The aggregate function is then used to calculate the average rating given by users on a monthly basis to each Apple product. The sorting is done first by month and then by product ID.

Pro Tip: Questions with aggregate functions like are super popular at Apple. Practice more of these on DataLemur:

![DataLemur SQL Questions](https://api.datalemur.com/assets/29766393-9c72-4bf6-a726-b227c80fd0bb

### SQL QUESTION 4: In database design, what do foreign keys do?

A foreign key is a column or group of columns in a table that refers to the primary key in another table. The foreign key constraint helps maintain referential integrity between the two tables.

For example, imagine you worked on Apple's People Analytics team, and wanted to analyze data from Apple's HR database:

:

employee_idfirst_namelast_namemanager_id
1AubreyGraham3
2MarshalMathers3
3DwayneCarter4
4ShawnCarter

In this table, serves as the primary key and functions as a foreign key because it links to the of the employee's manager. This establishes a relationship between Apple employees and their managers, allowing for easy querying to find an employee's manager or see which employees report to a specific manager.

The table may also have multiple foreign keys that reference primary keys in other tables. For example, and foreign keys could be used to connect each employee to their respective department and location.

### SQL Question 5: Average Sales of Apple Products

As a data analyst at Apple, you need to understand the sales performance of different Apple products over time to make informed business decisions, but also to make make pretty charts like this:

You're given two tables: and . In the table, each row represents a different product, identified by , sold by Apple. The table contains data about the sales of these products, including and .

Write a SQL query to compute the average quantity of each product sold per month for the year 2021.

Example Tables,

table:

product_idproduct_name
1iPhone 12
2Apple Watch
3MacBook Pro

table:

sales_idproduct_iddate_of_salequantity_sold
112021-01-10100
212021-01-15200
322021-01-2050
422021-02-1575
532021-02-1020

This query first inner joins and via . Afterwards, it groups by month and to compute the average quantity sold each month for distinct products. Filtering of year is done to only get data for 2021.

### SQL QUESTION 6: What's a stored procedure?

Stored procedures are like functions in Python – they can accept input params and return values, and are used to encapsulate complex logic.

For example, if you worked as a Data Analyst in support of the Marketing Analytics team at Apple, a common task might be to find the conversion rate for your ads given a specific time-frame. Instead of having to write this query over-and-over again, you could write a stored procedure like the following:

To call this stored procedure, you'd execute the following query:

### SQL Question 7: Calculate Add-to-Bag Conversion Rate for Apple Store

Suppose you are working as a Data Analyst on the Apple Store Digital team. Your focused on improving the conversion of people who clicked on a product to those who added it to their bag (shopping cart).

You are given the following two tables:

#### Example Input:

click_idproduct_iduser_idclick_time
1500112306/08/2022 00:00:00
2600145606/10/2022 00:00:00
3500178906/18/2022 00:00:00
4700132107/26/2022 00:00:00
5500165407/05/2022 00:00:00

#### Example Input:

1500112306/08/2022 00:02:00
2600145606/10/2022 00:01:00
3500178906/18/2022 00:03:00
4700132107/26/2022 00:04:00
5500198507/05/2022 00:05:00

Your task is to write a SQL query that calculates the add-to-bag conversion rate, defined as the number of users who add a product to their bag (cart) after clicking on the product listing, divided by the total number of clicks on the product. Break down the result by product_id.

The SQL query starts by doing a left join of the table with the table, on the product_id and user_id. The case statement in the sum function counts the number of records that have a matching record in the table (i.e. the product was actually added to the bag). This is divided by the total number of clicks on each product to get the conversion rate.

To solve a similar click-through-rate SQL interview question on DataLemur's free interactive SQL code editor, try this Meta SQL interview question:

### SQL QUESTION 8: How is the constraint used in a database?

A is a field in a table that references the of another table. It creates a link between the two tables and ensures that the data in the field is valid.

Say for example you had sales analytics data from Apple's CRM (customer-relationship management) tool.

The FOREIGN KEY constraint ensures that the data in the field of the "opportunities" table is valid, and prevents the insertion of rows in the table that do not have corresponding entries in the 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 accounts table if there are still references to it in the opportunities table.

### SQL Question 9: Follow-Up AirPod Percentage

The Apple Customer Retention Data Science team needs your help to investigate buying patterns related to AirPods and iPhones.

Write a SQL query to determine the percentage of buyers who bought AirPods directly after they bought iPhones. Round your answer to a percentage (i.e. 20 for 20%, 50 for 50) with no decimals.

##### Table:
Column NameType
transaction_idinteger
customer_idinteger
product_namevarchar
transaction_timestampdatetime
##### Example Input:
transaction_idcustomer_idproduct_nametransaction_timestamp
1101iPhone08/08/2022 00:00:00
2101AirPods08/08/2022 00:00:00
5301iPhone09/05/2022 00:00:00
7301AirPods09/07/2022 00:00:00
##### Example Output:
follow_up_percentage
50

Of the two users, only user 101 bought AirPods after buying an iPhone. Note that we still count user 101, even though they bought both an iPhone and AirPods in the same transaction. We can't count customer 301 since they bought an iPad in between their iPhone and AirPods.

Therefore, 1 out of 2 users fit the problem's criteria. For this example, the follow-up percentage would be 50%.

To come to this solution, we broke down the problem into 3 steps:

1. Determine cases where AirPods were bought directly after iPhones
2. Find which users bought AirPods directly after iPhones
3. Calculate the percentage of total users who bought AirPods directly after iPhones

For an in-depth explanation of each step, and to try the problem yourself, subscribe to DataLemur Premium right here:

### SQL Question 10: iCloud Storage Analysis

Write a SQL query to find all users who have more than one type of device (e.g., both an iPhone and a MacBook) and are using more than 50GB of total iCloud storage across all their devices.

The output should include the UserID, UserName, total number of devices, and total storage used. Order the results by the total storage used in descending order.

Users

• UserID (INT): Unique identifier for each user.
• UserName (VARCHAR): Name of the user.
• Email (VARCHAR): Email address of the user.
• Country (VARCHAR): Country where the user is located.

Devices

• DeviceID (INT): Unique identifier for each device.
• UserID (INT): Identifier of the user to whom the device belongs.
• DeviceType (VARCHAR): Type of the device (e.g., iPhone, iPad, MacBook).
• PurchaseDate (DATE): Date when the device was purchased.

StorageUsage

• DeviceID (INT): Identifier of the device.
• StorageUsed (INT): Amount of iCloud storage used by the device (in GB).
• LastUpdate (DATE): Date when the last update was made to the storage usage in iCloud

The solutions uses joins to combine data from the Users, Devices, and StorageUsage tables based on related columns (UserID and DeviceID). It applies to aggregate data for each user, allowing us to calculate counts and sums per user. The clause is crucial as it filters users who have more than one type of device and use more than 50GB of iCloud storage.

Finally, the clause sorts the results by total storage used in descending order, highlighting users with the highest storage usage first.

A core part of this Apple SQL interview problem is joining the 3 tables together. Since joins come up frequently during Apple SQL interviews, try this interactive Snapchat Join SQL question for extra practice:

### Preparing For The Apple SQL Interview

The best way to prepare for the Apple SQL interview, besides making sure you have strong SQL fundamentals, is to practice a ton of real SQL questions that were asked in recent Apple job interviews.

In addition to solving the earlier Apple SQL interview questions, you should also solve the 200+ SQL query coding questions:

It can also be helpful to practice SQL problems from other Apple competitor tech companies like:

But if your SQL query skills are weak, forget about diving straight into solving questions – refresh your SQL knowledge with this free SQL tutorial.

This tutorial covers SQL topics like cleaning text data and Subquery vs. CTE – both of these come up frequently in Apple interviews.

### Apple Data Science Interview Tips

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

For the Apple Data Science Interview, in addition to SQL query questions, the other types of questions which are covered:

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

The best way to prepare for Apple 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+ reviews, 4.5-star rating)