8 Alkami Technology SQL Interview Questions (Updated 2024)
At Alkami Technology, SQL does the heavy lifting for extracting and analyzing data from Alkami's mobile banking platform and modifying databases to enhance the functionality of their digital banking solutions. So, it shouldn't surprise you that Alkami Technology asks SQL problems during interviews for Data Science, Data Engineering and Data Analytics jobs.
As such, to help you practice for the Alkami Technology SQL interview, here’s 8 Alkami Technology SQL interview questions – can you answer each one?
8 Alkami Technology SQL Interview Questions
SQL Question 1: User Login Analysis
Alkami Technology heavily relies on analyzing its user behaviors on their platform. Your task is to answer the following question: On any given day, how many unique users logged in to the system? Please provide a daily breakdown of user login for the last 7 days.
First let's define our tables:
Example Input:
| id | user_id | login_date |
|---|---|---|
| 101 | 1 | 2022-10-01 |
| 102 | 2 | 2022-10-01 |
| 103 | 3 | 2022-10-01 |
| 104 | 1 | 2022-10-02 |
| 105 | 2 | 2022-10-02 |
| 106 | 4 | 2022-10-02 |
| 107 | 1 | 2022-10-02 |
| 108 | 5 | 2022-10-03 |
| 109 | 1 | 2022-10-04 |
| 110 | 5 | 2022-10-04 |
| 111 | 6 | 2022_10_04 |
| 112 | 1 | 2022_10_05 |
| 113 | 2 | 2022_10_05 |
| 114 | 5 | 2022_10_05 |
| 115 | 2 | 2022_10_06 |
| 116 | 3 | 2022_10_06 |
| 117 | 4 | 2022_10_07 |
Answer:
To answer this, we will use the ROW_COUNT() function, which is a type of SQL window function.
This query first creates a subquery where it adds a row number within each partition of user_id, sorted by login_date. Since we only want to look at unique logins, we filter for only the first row within each user (where row_num = 1). This ensures that if a user logged in to the system multiple times in a day, they are only counted once. We then group by login_date to get the count of unique users for each day. The WHERE clause in the subquery limits our search to the last 7 days.
For more window function practice, solve this Uber SQL problem on DataLemur's online SQL coding environment:
SQL Interview Question 2: Calculate Average Daily Usage Per User
At Alkami Technology, a company that provides digital banking solutions, you're asked to find out the average daily usage per user of their banking application. Specifically, you need to calculate the average number of transactions each user makes per day.
Assuming you have a table named which stores all the transactions that users make each day, and a table called which stores the information of each user. The table has columns - transaction_id, user_id and transaction_date; and the table has columns - user_id and user_name.
Example Input:
| transaction_id | user_id | transaction_date |
|---|---|---|
| 1 | 100 | 2020-12-01 |
| 2 | 101 | 2020-12-01 |
| 3 | 100 | 2020-12-02 |
| 4 | 102 | 2020-12-02 |
| 5 | 100 | 2020-12-03 |
Example Input:
| user_id | user_name |
|---|---|
| 100 | John Doe |
| 101 | Jane Doe |
| 102 | Sam Smith |
Expected output would be the average daily transactions for each user.
Example Output:
| user_id | avg_daily_transactions |
|---|---|
| 100 | 1.67 |
| 101 | 1.00 |
| 102 | 1.00 |
Answer:
To find the average number of daily transactions per user, a SQL query could be written as follows,
This SQL query first groups the transactions table by user_id and transaction_date to calculate the number of transactions each day for each user. This data is nested within another query that groups by user_id to calculate the average number of transactions per day for each user. Please note that the AVG function in SQL provides the average value of a numeric column.
To practice a very similar question try this interactive Twitter Histogram of Tweets Question which is similar for requiring analysis of individual user behavior or this Twitter Tweets' Rolling Averages Question which is similar for involving calculations for each user over a time period.
SQL Question 3: Can you explain the distinction between an inner and a full outer join?
An inner join only includes rows from both tables that satisfy the join condition, whereas a full outer join includes all rows from both tables, regardless of whether they match the join condition or not.
To demonstrate each kind, Imagine you were working on a Advertising Analytics project at Alkami Technology and had two database tables: an table that contains data on Google Ads keywords and their bid amounts, and a table with information on product sales and the Google Ads keywords that drove those sales.
An retrieves rows from both tables where there is a match in the shared key or keys. For example, an INNER JOIN between the table and the table could be performed using the keyword column as the shared key. This would retrieve only the rows where the keyword in the table matches the keyword in the table.
A retrieves all rows from both tables, regardless of whether there is a match in the shared key or keys. If there is no match, values will be returned for the columns of the non-matching table.
SQL Question 4: Retrieving Average Cost and Transaction Volume for Each Product
As a data analyst in Alkami technology, you have been tasked to analyze customer transaction behavior with Alkami products. Extract insightful information about the average transaction amount and total transaction volume for each product in the year 2023. Join necessary tables to achieve your goal.
For instance, you have the and tables.
Example Input:
| transaction_id | user_id | transaction_date | product_id | amount |
|---|---|---|---|---|
| 101 | 823 | 01/08/2023 00:00:00 | 20501 | 400 |
| 102 | 461 | 02/10/2023 00:00:00 | 30512 | 150 |
| 103 | 789 | 02/18/2023 00:00:00 | 20501 | 450 |
| 104 | 254 | 03/26/2023 00:00:00 | 30512 | 200 |
| 105 | 345 | 04/05/2023 00:00:00 | 20501 | 350 |
Example Input:
| product_id | product_name |
|---|---|
| 20501 | Alkami Savings |
| 30512 | Alkami Checking |
The output should yield average amounts and total transaction volumes for each product for the year 2023.
Answer:
This query joins the table with the table using the common column . After the join, it filters out the transactions not in 2023. Then it groups the transactions by to find the average transaction amount (using ) and total transaction volumes (using ) for each product. The aggregation functions AVG and COUNT ignore NULL values, so there is no need to worry about transactions without an amount or product_id.
Since join questions come up routinely during SQL interviews, take a stab at an interactive SQL join question from Spotify:
SQL Question 5: What's the difference between a foreign and primary key?
To explain the difference between a primary key and foreign key, let's start with an example Alkami Technology sales database:
:
+------------+------------+------------+------------+ | order_id | product_id | customer_id| quantity | +------------+------------+------------+------------+ | 1 | 303 | 1 | 2 | | 2 | 404 | 1 | 1 | | 3 | 505 | 2 | 3 | | 4 | 303 | 3 | 1 | +------------+------------+------------+------------+
In this table, could be a primary key. It is unique for each row in the table and cannot contain null values.
and could both be foreign keys. They reference the primary keys of other tables, such as a Products table and a Customers table, respectively. This establishes a relationship between the table and the other tables, such that each row in the sales database corresponds to a specific product and a specific customer.
SQL Question 6: Aggregate and Math Operations
Alkami Technology is building a financial dashboard for a client. They are looking at transactions made by their customers and would like to monitor the daily average and standard deviation of transactions of different currencies. The goal is to identify any unusual spikes in transactions which may signify fraudulent activities.
They use two tables: and as below:
example input:
| trans_id | cust_id | trans_date | currency_id | amount |
|---|---|---|---|---|
| 5132 | 319 | 05/14/2022 | 1 | 2300 |
| 6203 | 108 | 05/16/2022 | 2 | 1800 |
| 7429 | 409 | 05/21/2022 | 1 | 4600 |
| 6564 | 282 | 05/31/2022 | 2 | 2500 |
| 8176 | 118 | 06/02/2022 | 3 | 1300 |
example input:
| currency_id | currency_name |
|---|---|
| 1 | USD |
| 2 | EUR |
| 3 | GBP |
Can you write a PostgreSQL query that shows the daily total, average, the absolute difference from the grand average and the square root of each transaction amount by currency for the past 30 days?
Answer:
This query first joins the table with the table on their shared key, . It then filters out the transactions that occurred in the last 30 days. The query calculates the daily total and average transaction amount per currency, the absolute difference of daily average from the grand average, and the square root of each transaction amount. The result is ordered by the transaction date in ascending order.
To practice a very similar question try this interactive Stripe Repeated Payments Question which is similar for handling transaction data or this Uber User's Third Transaction Question which is similar for analyzing transactions.
SQL Question 7: What's denormalization, and when does it make sense to do it?
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!
SQL Question 8: Detect Unusual Banking Activity
Alkami Technology primarily provides digital banking solutions. So, an example question could involve detecting unusual customer banking activity based on transaction timestamps and amounts.
Let's assume there's a transactions table at Alkami with the following structure:
Example Input:
| transaction_id | account_id | timestamp | amount |
|---|---|---|---|
| 9283 | 556 | 2022-08-01 07:24:00 | 150.00 |
| 7632 | 128 | 2022-08-01 09:35:00 | 270.00 |
| 8651 | 556 | 2022-08-01 11:12:00 | 350.00 |
| 9452 | 345 | 2022-08-01 15:43:00 | 200.00 |
| 6382 | 128 | 2022-08-02 17:50:00 | 180.00 |
| 5231 | 556 | 2022-08-02 23:55:00 | 140.00 |
The task is to write an SQL query that displays accounts with multiple transactions within a short period of time. These could potentially be fraudulent activities.
The output should show all accounts that had more than 3 transactions within any given 24 hour period over the past week.
Example Output:
| account_id | date | transaction_count |
|---|---|---|
| 556 | 2022-08-01 | 3 |
| 128 | 2022-08-01 | 2 |
Answer:
This query groups by account_id and date and checks the count of transactions in each grouping. Using the HAVING clause, it filters out those groupings where the count of transactions is more than 3. The WHERE clause ensures it only considers the past week's data. This query can help to quickly locate suspicious activity by identifying accounts with unusually high transaction counts.
Preparing For The Alkami Technology SQL Interview
Assuming that you've already got basic SQL skills, the next best tip we have to prepare for the Alkami Technology SQL interview is to solve as many practice SQL interview questions as you can!
Besides solving the earlier Alkami Technology SQL interview questions, you should also solve the 200+ FAANG SQL Questions on DataLemur which come from companies like Google, Microsoft and Silicon Valley startups.
Each problem on DataLemur has hints to guide you, step-by-step solutions and crucially, there's an interactive coding environment so you can right online code up your query and have it executed.
To prep for the Alkami Technology SQL interview it is also useful to practice SQL problems from other tech companies like:
However, if your SQL coding skills are weak, don't worry about jumping right into solving questions – refresh your SQL knowledge with this SQL tutorial for Data Analytics.
This tutorial covers SQL topics like CTE vs. Subquery and LEAD/LAG window functions – both of these come up routinely during Alkami Technology interviews.
Alkami Technology Data Science Interview Tips
What Do Alkami Technology Data Science Interviews Cover?
In addition to SQL interview questions, the other question categories tested in the Alkami Technology Data Science Interview are:
- Statistics Interview Questions
- Python Pandas or R Coding Questions
- Business Sense and Product-Sense Questions
- Machine Learning and Predictive Modeling Questions
- Behavioral Based Interview Questions
How To Prepare for Alkami Technology Data Science Interviews?
The best way to prepare for Alkami Technology 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 Refresher covering Python, SQL & ML
- Amazing Reviews (900+ 5-star reviews on Amazon)