# 10 Cellebrite SQL Interview Questions (Updated 2024)

Updated on

March 3, 2024

At Cellebrite, SQL is often used for analyzing digital forensic data from mobile devices and for helping law enforcement agencies get insights from their data. That's why Cellebrite frequently asks SQL coding questions during interviews for Data Analyst, Data Science, and BI jobs.

To help you study for the Cellebrite SQL interview, this blog covers 10 Cellebrite SQL interview questions – able to solve them?

## 10 Cellebrite SQL Interview Questions

### SQL Question 1: Average rating of products per month

In an attempt to understand how well our devices are performing in the market, we are interested in knowing the average rating of our products per month. Can you write a query which calculates this? You should look over the table, where each feedback row contains a , , (the rating of the device), and .

##### Example Output:

In PostgreSQL SQL:

This query works by utilizing SQL's GROUP BY clause to separate the rows by month and device_id. We get the month value from the feedback_date using the EXTRACT function available in Postgres. We then calculate the average of using the AVG() aggregate function for each group, resulting in the average rating for each product for each month. The ORDER BY clause helps us in ordering this resulting data by month and device_id for easier understanding and reading.

p.s. Window functions show up super often during SQL interviews, so practice the 27+ window function questions on DataLemur

### SQL Question 2: Analyzing Click-Through Conversion Rates

Cellebrite is a global leader in digital intelligence solutions that has customers from numerous businesses around the world. As part of running their digital business, Cellebrite is interested in knowing the click-through conversion rate of their digital products - specifically, the rate at which users view a product page and then add that product to their cart.

Given the two tables below and :

##### Example Input:
view_iduser_idview_dateproduct_idproduct_category
101ABC06/08/2022 00:00:00P1Forensics
202DEF06/10/2022 00:00:00P2Analytics
303ABC06/18/2022 00:00:00P1Forensics
404GHI07/26/2022 00:00:00P3Cybersecurity
505DEF07/05/2022 00:00:00P2Analytics
##### Example Input:
cart_iduser_idcart_dateproduct_id
901ABC06/08/2022 00:00:00P1
902JKL06/10/2022 00:00:00P4
903ABC06/18/2022 00:00:00P1
904GHI07/26/2022 00:00:00P3
905MNO07/05/2022 00:00:00P2

Please write a SQL query that determines the click-through conversion rate for each product, calculated as the number of times a product view leads to an add to cart action, divided by the total number of product views.

This SQL query calculates the click-through conversion rate of each product by joining the view data with the add to cart data on user_id and product_id. The ratio is calculated by counting the number of distinct cart actions and dividing it by the count of distinct product views. We are using a left join, as every add to cart action has to be preceded by a product view action. For accurate conversion rate calculation, a time condition is put in place to ensure the view date is before or same as cart date. The results are presented per product, also grouped by product category, and ordered by the click-through conversion rate from the highest to the lowest.

To solve another question about calculating rates, try this TikTok SQL question on DataLemur's interactive SQL code editor:

### SQL Question 3: How do relational and non-relational databases differ?

While both types of databases are used to store data (no duh!), relational databases and non-relational (also known as NoSQL databases) differ in a few important ways, most importantly on the way data is stored. Relational databases use a data model consisting of tables and rows, while NoSQL databases use a variety of data models, including document, key-value, columnar, and graph storage formats.

This added flexibilty makes NoSQL databases great for non-tabular data (like hierarchal data or JSON data), or data where the type/format is constantly evolving. With this added flexibility, comes one big weakness – you won't get ACID-compliance. That means, unlike relational databases which are typically adhere to the ACID properties (atomic, consistent, isolated, and durable), you don't get as strong guarentees with most non-relational databases.

### SQL Question 4: Top Rated Device Model

Cellebrite deals with digital intelligence and forensics tools for law enforcement, military and intelligence, and corporate customers. It is crucial for Cellebrite to understand which device models are highly rated by forensic analysts. Can you write an SQL query to find the average rating for each device model (both in the current month and overall)? Assume ratings are given on a scale of 1-5, with 5 being the best.

Here are some possible test data:

##### Example Input:
rating_idanalyst_idsubmit_datedevice_modelrating
78321008/12/2022 00:00:00UFED Touch24
89632708/15/2022 00:00:00UFEDPA5
25633307/30/2022 00:00:00UFED Touch24
54621908/19/2022 00:00:00UFEDPA5
33873707/10/2022 00:00:00UFED Touch24

To find the average rating for each device model for the current month:

To find the average rating for each device model overall:

This query averages the ratings for each device model. The first query considers only ratings submitted in the current month, while the second query considers all ratings. The clause is used to group the ratings by device model.

##### Example Output for first SQL query:
device_modelavg_rating
UFED Touch24
UFEDPA5
##### Example Output for second SQL query:
device_modelavg_rating
UFED Touch24
UFEDPA5

### SQL Question 5: When would you use a clustered index vs. a non-clustered index?

Here's an example of a clustered index on the column of a table of Cellebrite payments table:

This creates a clustered index on the column, which will determine the physical order of the data rows in the table.

Here is an example of a non-clustered index on the column of the same table:

This will create a non-clustered index on the column, which will not affect the physical order of the data rows in the table.

In terms of query performance, a clustered index is usually faster for searches that return a large number of records, while a non-clustered index is faster for searches that return a small number of records. However, updates to a clustered index are slower, as they require the data rows to be physically rearranged, while updates to a non-clustered index are faster, as they only require the index data structure to be updated.

### SQL Question 6: Using SQL LIKE to Search Client Records

As a data analyst at Cellebrite, you're required to search through the company's client database and identify clients from law enforcement agencies. Your task is to fetch all the records where the client's name starts with 'Federal' or 'State'. Assume that the database contains a table named consisting of columns client_id, name, email, and sign_up_date.

##### Example Input:
client_idnameemailsign_up_date
0071Federal Bureau of Investigationfbi@example.com2018-02-15
0232Oregon State Policeosp@example.com2019-06-20
2671XYZ Corpxyzcorp@example.com2020-08-15
3456State Farm Insurancestatefarm@example.com2018-10-11
4478Acme Corporationacme@example.com2019-04-02

This query will return all client records where the client name starts with 'Federal' or 'State'. 'LIKE' is used in conjunction with '%' (wildcard character) to search for specific patterns in the SQL database. The '%' is a placeholder for 0 or more characters, which is why it's placed after the words 'Federal' and 'State' in the query.

### SQL Question 7: What do foreign key's 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. The table with the foreign key is called the child table, while the table with the candidate key is called the parent or referenced table.

For example, consider a database with two tables: and customer_idcustomer_idcustomer_id` column in Cellebrite customers table.

### SQL Question 8: Joining Customer and Product Purchases Information

Consider we have two tables - and . The table contains the details of every customer including 'customer_id', 'first_name', 'last_name', 'email', and 'signup_date'. The table contains 'purchase_id', 'customer_id' (which corresponds to the 'customer_id' in the table), 'product_id', 'quantity' and 'purchase_date'.

You are required to write a SQL query that provides the 'first_name', 'last_name', and an aggregate total quantity of each product bought by every customer.

##### Example Input:
customer_idfirst_namelast_nameemailsignup_date
1JohnDoejohndoe@email.com2019-01-08
2JaneSmithjanesmith@email.com2020-02-09
3MarkJohnsonmarkjohnson@email.com2021-03-10
##### Example Input:
purchase_idcustomer_idproduct_idquantitypurchase_date
5011800122021-06-11
5022800232022-06-12
5031800112022-06-14
5043800322022-06-15
5052800222022-06-15

The output should contain 'first_name', 'last_name', and 'total_quantity' for each product.

This PostgreSQL query starts by selecting the desired fields - 'first_name' and 'last_name' from the 'customers' table, and 'product_id' and a new aggregate field 'total_quantity' from the 'purchases' table. It then joins the 'customers' and 'purchases' tables using the 'customer_id' as the common field. Finally, it groups the results by the 'first_name', 'last_name', and 'product_id' fields, summarising the quantity of each product for each customer.

Because joins come up so often during SQL interviews, try this Snapchat Join SQL question:

### SQL Question 9: Calculate Statistical Metrics for Revenue

Cellebrite is a digital intelligence company that has a variety of products generating revenue. The CEO of the company wants to quickly analyze the revenue generated from each product category, with statistical metrics such as average, standard deviation and coefficient of variation (which is the ratio of the standard deviation to the mean) for each product category.

For simplification, we only consider the absolute revenue generated from each product category and ignore the cost aspect.

Given the following tables:

##### Example Input:
product_idcategory
1"Mobile Forensics"
2"Cloud Analytics"
3"Field Solutions"
##### Example Input:
month_yearproduct_idrevenue_amt
"2020-JAN"1200
"2020-FEB"1400
"2020-MAR"1600
"2020-JAN"2100
"2020-FEB"2200
"2020-MAR"2300
"2020-JAN"3500
"2020-FEB"31000
"2020-MAR"31500
"2020-JAN"4150
"2020-FEB"4300
"2020-MAR"4450

The expected output should show the following metrics (average revenue, standard deviation, coefficient of variation) for each product category.

This query first joins the and tables on . Then for each product category, it calculates the average revenue (using AVG()), standard deviation of revenue (using STDDEV()), and the coefficient of variation (computed as the ratio of standard deviation to the average).

To practice a very similar question try this interactive Amazon Highest-Grossing Items Question which is similar for analyzing revenue per category or this Wayfair Y-on-Y Growth Rate Question which is similar for calculating statistical metrics.

### SQL Question 10: What is denormalization, and in what situations might it be a useful?

Database denormalization is when you add redundancy to a database, and break typical normalization rules (specified by 1st, 2nd, 3rd normal forms). There's a few reasons to denormalize a database:

• Improved performance: Denormalization often reduces the the number of costly join operations that are needed to retrieve data. This is helpful when the database is being used for OLAP (Online Analytical Processing) use cases, as joins can be expensive and slow.

• Simplification: Denormalization can also be used to simplify the design of a database by reducing the number of tables and relationships that need to be managed. This can make it easier to understand and maintain the database.

• Ease of use: Denormalization can also make it easier for users to work with a database by providing them with a more intuitive and straightforward data model.

While denormalization can be a useful tool for improving performance and scalability, it's important to keep in mind that it can make update and delete operations more complex. This is because denormalization can create data duplicates, which can make it harder to maintain data integrity. So, to avoid any headaches, it's a good idea to start with a well-normalized database design and then consider denormalization only if it's absolutely necessary for your specific performance and scalability needs. In other words, denormalization can be a bit of a wild card, so it's best to handle it with care!

### Preparing For The Cellebrite SQL Interview

The best way to prepare for a Cellebrite SQL interview is to practice, practice, practice. Besides solving the above Cellebrite SQL interview questions, you should also solve the 200+ tricky sql questions on DataLemur which come from companies like FAANG tech companies and tech startups.

Each interview question has multiple hints, step-by-step solutions and best of all, there is an online SQL coding environment so you can right online code up your SQL query answer and have it graded.

To prep for the Cellebrite SQL interview you can also be helpful to solve SQL problems from other tech companies like:

But if your SQL coding skills are weak, forget about going right into solving questions – improve your SQL foundations with this free SQL for Data Analytics course.

This tutorial covers SQL topics like handling timestamps and UNION – both of these pop up frequently during Cellebrite SQL interviews.

### Cellebrite Data Science Interview Tips

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

Beyond writing SQL queries, the other types of questions covered in the Cellebrite Data Science Interview are:

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

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

• 201 Interview Questions from FAANG tech companies
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