At BlackBerry, SQL is typically used for analyzing and managing cybersecurity data trends, and querying databases for product performance metrics and user engagement insights. Unsurprisingly this is why BlackBerry almost always evaluates jobseekers on SQL questions during interviews for Data Analytics, Data Science, and Data Engineering jobs.
So, to help you study for the BlackBerry SQL interview, here’s 9 BlackBerry SQL interview questions – able to answer them all?
Identify the top users from the BlackBerry's customer database who have sent the maximum number of emails using their BlackBerry devices over some period.
Consider two tables -
user_id | first_name | last_name | device_model |
---|---|---|---|
123 | John | Doe | Classic |
265 | Lisa | Smith | Bold |
362 | Mark | Thomas | Curve |
192 | Anna | White | Key2 |
981 | Jacob | Brown | Pearl |
email_id | user_id | send_date |
---|---|---|
101 | 123 | 06/08/2022 |
102 | 123 | 06/10/2022 |
103 | 123 | 06/18/2022 |
104 | 192 | 07/26/2022 |
105 | 981 | 07/05/2022 |
106 | 981 | 07/06/2022 |
This PostgreSQL query joins and based on . It calculates the number of emails each user has sent (email_count) and sorts the results in descending order of . Only the top 5 users are returned, representing the top BlackBerry users based on the number of emails sent.
To practice a similar VIP customer analysis question on DataLemur's free interactive coding environment, try this Microsoft Azure Cloud SQL Interview Question:
Given a reviews table that has been maintained by BlackBerry to track the reviews of its products, you are required to summarize the monthly average rating of each product.
Each row in the table is a record of a review completed by a user and contains the following fields: review_id: ID of the review, user_id: ID of the user who submitted the review, submit_date: Date and time the review was submitted, product_id: ID of the product being reviewed, and stars: Numeric rating (1-5) given to the product by the user.
For this task, write a SQL query to calculate the average rating (stars) for each BlackBerry product on a monthly basis.
review_id | user_id | submit_date | product_id | stars |
---|---|---|---|---|
1 | 10 | 2022-06-01 00:00:00 | 1001 | 4 |
2 | 20 | 2022-06-02 00:00:00 | 1002 | 4 |
3 | 30 | 2022-06-15 00:00:00 | 1001 | 3 |
4 | 40 | 2022-07-10 00:00:00 | 1002 | 3 |
5 | 50 | 2022-07-15 00:00:00 | 1001 | 2 |
month | product_id | average_rating |
---|---|---|
6 | 1001 | 3.5 |
6 | 1002 | 4.0 |
7 | 1001 | 2.0 |
7 | 1002 | 3.0 |
The query applies the AVG function, which calculates the average rating of the products, grouped by product_id and month. The result is ordered by product_id and month for a better overview.
To practice a similar window function question on DataLemur's free interactive SQL code editor, try this Google SQL Interview Question:
To clarify the distinction between a primary key and a foreign key, let's examine employee data from BlackBerry's HR database:
:
+------------+------------+------------+------------+ | employee_id| first_name | last_name | manager_id | +------------+------------+------------+------------+ | 1 | Aubrey | Graham | 3 | | 2 | Marshal | Mathers | 3 | | 3 | Dwayne | Carter | 4 | | 4 | Shawn | Carter | | +------------+------------+------------+------------+
In this table, serves as the primary key. It uniquely identifies each employee and cannot be null.
functions as a foreign key, linking to the of the employee's manager. This establishes a relationship between BlackBerry 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.
BlackBerry, originally well-known for their mobile phones, is now a company providing security software and services. Let's assume they still have phones and we have a database of call records. For a specific phone model, they may wish to determine the average duration of outgoing calls.
Your task is to write a SQL query that returns the average of the call duration for each phone model in the database.
call_id | call_date | call_duration(minutes) | phone_model | call_type |
---|---|---|---|---|
101 | 2022-08-06 09:00:00 | 15 | BB1 | outgoing |
102 | 2022-08-06 09:30:00 | 30 | BB1 | incoming |
103 | 2022-08-06 10:00:00 | 45 | BB2 | outgoing |
104 | 2022-08-06 10:30:00 | 60 | BB2 | incoming |
105 | 2022-08-06 11:00:00 | 25 | BB1 | outgoing |
phone_model | avg_duration |
---|---|
BB1 | 20 |
BB2 | 45 |
In this SQL query, we are first filtering out the outgoing calls from our table using the WHERE clause . Then we are grouping by and calculating the average of for each phone model using the AVG function. This gives us the average duration of outgoing calls for each phone model.
To practice a very similar question try this interactive Verizon International Call Percentage Question which is similar for requiring calculation of call-related percentages or this Amazon Average Review Ratings Question which is similar for requiring calculation of an average for each identifier.
The clause is used to filter rows from the result set of a , , or statement. It allows you to specify a condition that must be met for a row to be included in the result set.
The clause is used to filter groups created by the clause. It is similar to the clause, but it is used to specify conditions on the groups created by the clause, rather than on the individual rows of the table.
Say you were working on a social media analytics project for BlackBerry.
Here is an example of a SQL query that you might write which uses both the WHERE and HAVING clauses:
This query retrieves the total impressions and average conversions for each platform in the table, the date of the campaign is in January 2023. The rows are grouped by platform and the clause filters the groups to include only those with more than 5000 impressions and an average conversion rate above 0.2.
You are given a table 'sales' representing the sale record of BlackBerry phones. The table has fields 'sale_id' - a unique identifier for each sale, 'sale_date' - the date of sale, 'model' - the model of the BlackBerry phone sold, and 'price' - the amount for which the phone model was sold.
For each model, calculate the average selling price. Show the data for the top five models with the highest average selling price.
Sample 'sales' table:
sale_id | sale_date | model | price |
---|---|---|---|
10001 | 01/06/2022 | BlackBerry KEY2 | 400 |
10002 | 03/06/2022 | BlackBerry KEYone | 350 |
10003 | 05/06/2022 | BlackBerry Passport | 200 |
10004 | 07/06/2022 | BlackBerry KEY2 | 450 |
10005 | 09/06/2022 | BlackBerry KEYone | 300 |
10006 | 12/06/2022 | BlackBerry Classic | 150 |
10007 | 14/06/2022 | BlackBerry Passport | 220 |
10008 | 16/06/2022 | BlackBerry Classic | 150 |
10009 | 18/06/2022 | BlackBerry KEYone | 340 |
10010 | 20/06/2022 | BlackBerry KEY2 | 420 |
This query first groups all sales records by 'model'. For every model, it calculates the average price at which the model was sold (AVG(price)). It then orders the models by the average selling price in descending order, and finally, limits the output to top five models.
The primary key of a table is a column or combination of columns that serves to uniquely identify each row in the table. To define a primary key in a SQL database, you can use the constraint.
For instance, consider a table of :
In this example, the column is the primary key of the BlackBerry employees table.
Primary keys are important in databases for several reasons:
BlackBerry requires a report on all the customers in specific locations. Could you write a SQL query to filter all customers who are located in regions starting with the term 'North' from the customer records database? Assume each record has 'customer_id', 'customer_name', 'location' as column headers. Use the LIKE command to filter the records based on the string condition.
The sample data is given below:
customer_id | customer_name | location |
---|---|---|
1 | John Smith | North Carolina |
2 | Sarah Connors | South California |
3 | Tim Roberts | North Dakota |
4 | Nancy Jones | East Texas |
5 | Alex Williams | North Norway |
You can accomplish this with a simple SELECT query that leverages the LIKE keyword to filter results.
This query should return all records where the 'location' field begins with the term 'North'.
The expected output should be as follows:
customer_id | customer_name | location |
---|---|---|
1 | John Smith | North Carolina |
3 | Tim Roberts | North Dakota |
5 | Alex Williams | North Norway |
The SELECT statement is used to select data from a database. The data returned is stored in a result table. The LIKE operator is used in a WHERE clause to search for a specified pattern in a column. The '%' wildcard is used to match any sequence of characters, therefore 'North%' would match anything that starts with 'North'.
As a Data Engineer at BlackBerry, you've been asked to calculate the monthly standard deviation of sales for different products in different regions over the past year. This information is to be used to ascertain the volatility and predictability of sales.
They find this calculation to be a great measure of risk and volatility, and absolutely necessary for making forward-looking decisions.
Your task is to return a list sorted by standard deviation in descending order. For each product and region pair, return the product name, region name, and the standard deviation of monthly sales, rounded to two decimal places.
Assume you are provided with two tables:
sales_id | product_id | region_id | sales_volume | sales_date |
---|---|---|---|---|
101 | 1 | 1 | 50 | 2020-06-01 |
102 | 1 | 1 | 75 | 2020-07-05 |
103 | 2 | 1 | 100 | 2020-06-20 |
104 | 2 | 2 | 150 | 2020-06-10 |
105 | 2 | 2 | 200 | 2020-07-15 |
product_id | product_name |
---|---|
1 | BlackBerry Key2 |
2 | BlackBerry Key2 LE |
region_id | region_name |
---|---|
1 | Americas |
2 | Europe |
This query initially calculates the average of monthly sales for each product and region. Then it uses this average to produce the variance (the square of the standard deviation) by averaging the squares of the differences between each volume and the average volume. The square root of that average gives the standard deviation. It is important to note that we assume the sales dates within a month can be treated equivalitely. Otherwise, choose different granularity for sales date. It is then sorted in descending order.
To practice a very similar question try this interactive Wayfair Y-on-Y Growth Rate Question which is similar for using SQL to analyze sales data or this Amazon Average Review Ratings Question which is similar for requiring calculation over different products and periods.
Assuming that you've already got basic SQL skills, the next best tip we have to prepare for the BlackBerry SQL interview is to solve as many practice SQL interview questions as you can! Besides solving the earlier BlackBerry 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 DataLemur SQL question has multiple hints, full answers and crucially, there is an interactive coding environment so you can right in the browser run your SQL query and have it graded.
To prep for the BlackBerry SQL interview it is also a great idea to solve SQL questions from other tech companies like:
In case your SQL coding skills are weak, forget about going right into solving questions – improve your SQL foundations with this SQL interview tutorial.
This tutorial covers SQL topics like handling NULLs in SQL and working with string/text data – both of these show up often during BlackBerry SQL assessments.
Beyond writing SQL queries, the other types of questions to prepare for the BlackBerry Data Science Interview are:
The best way to prepare for BlackBerry Data Science interviews is by reading Ace the Data Science Interview. The book's got: