10 FICO SQL Interview Questions (Updated 2024)

At FICO, SQL is used day-to-day for data extraction from financial transaction datasets, and for supporting FICO's Xpress optimization service. That's why FICO often tests SQL questions during interviews for Data Analyst, Data Science, and BI jobs.

As such, to help you ace the FICO SQL interview, here’s 10 FICO SQL interview questions – able to answer them all?

10 FICO SQL Interview Questions

SQL Question 1: Identify VIP Customers

FICO is a data analytics company focused on credit scoring services. Let's say you are given a database of customer transactions. The transactions show the customer_id, transaction_id, transaction_value, and transaction date.

A VIP customer would be a customer who has high transaction value exceeding $50,000 over the last 3 months. You are tasked to write a SQL query that can identify these VIP customers from our customer transaction database.

transactions Example Input:

The output of the SQL query should include the customer_id and the total transaction value of the customer in the last 3 months.

Answer:

SELECT customer_id, SUM(transaction_value) AS total_transaction_value
FROM transactions
WHERE transaction_date > CURRENT_DATE - INTERVAL '3 months'
GROUP BY customer_id
HAVING SUM(transaction_value) > 50000;

This query works by filtering out transactions that have happened within the last three months. It then groups the transactions by customer_id and aggregates the transaction values for each customer. The HAVING clause is then used to filter out the customers who have spent more than $50,000 in that period.

To practice a related super-user data analysis question on DataLemur's free interactive coding environment, try this recently asked Microsoft SQL interview question:

SQL Question 2: Average FICO Score Change Per Customer Per Month

Given a table containing customers' credit scores for different months, can you write a query to find out the average change in FICO score for each customer each month?

fico_scores Example Input:

Example Output:

Answer:

SELECT
   customer_id,
   month_year,
   fico_score - lag(fico_score) OVER (PARTITION BY customer_id ORDER BY month_year) as avg_fico_change
FROM
   fico_scores;

In the query above, we use the LAG function, which is a type of window function, that provides access to a row at a specified physical offset which comes before the current row. We use LAG with PARTITION BY to group customers separately. We then order by month_year to make the window slide with time (months). This lets us compare each row's fico_score with the fico_score of the previous month in order to calculate the change in FICO score per customer each month.

To practice another window function question on DataLemur's free interactive coding environment, try this Google SQL Interview Question:

SQL Question 3: What is a self-join?

A self-join is a type of JOIN where a table is joined to itself. To execute a self-join, you must include the table name twice in the FROM clause and assign a different alias to each instance. You can then join the two copies of the table using a JOIN clause, and use a WHERE clause to specify the relationship between the rows.

For instance, suppose you had website visitor data for FICO, exported from the company's Google Analytics account. To assist the web-dev team, you needed to analyze pairs of pages for UX or navigational issues. As part of that analysis, you wanted to generate all pairs of URLs, but needed to exclude pairs where both URLs were the same since that is not a valid pair."

You could use the following self-join:

SELECT page1.url AS page_url, page2.url AS referred_from
FROM google_analytics AS page1
JOIN google_analytics AS page2 ON page1.referrer_id = page2.id
WHERE page1.id <> page2.id;

This query returns the url of each page (page1.url) along with the url of the page that referred to it (page2.url). The self-join is performed using the referrer_id field, which specifies the id of the page that referred the visitor to the current page, and avoids any pages that referred themself (aka data anomalies).

SQL Question 4: Credit Score History Analysis

FICO (Fair Isaac Corporation) is a data analytics company based in San Jose, California focused on credit scoring services. It is best known for its FICO Score, a measure of consumer credit risk. Assume you are given a simplified schema of two tables 'Customer' and 'CreditScoreHistory'. The 'Customer' table contains basic information about the customers, and the 'CreditScoreHistory' table logs the changes of FICO scores for each customer.

Now, you need to find out the average monthly change in FICO score for all customers for the year 2020.

Customer Sample Input:

CreditScoreHistory Sample Input:

Answer:

SELECT
EXTRACT(MONTH FROM csh.date) AS mth,
avg(csh.fico_score - lag(csh.fico_score) OVER (PARTITION BY csh.customer_id ORDER BY csh.date)) AS avg_monthly_change
FROM
CreditScoreHistory csh
WHERE
EXTRACT(YEAR FROM csh.date) = 2020
GROUP BY mth

This query uses the lag() window function to get the previous month's FICO score for each customer and then calculates the difference between the current and previous month's score to get the monthly change. The outer query finds the average of these monthly changes for each month.

SQL Question 5: Could you clarify the difference between a left and a right join?

In SQL, both a left and right join is used to combine rows from different tables based on a shared key or set of keys, resulting in a single merged table of data. However, here's the difference:

LEFT (OUTER) JOIN: Retrieves all the records/rows from the left and the matched records/rows from the right table.

SELECT *
FROM Table_A A
LEFT JOIN Table_B B
ON A.col = B.col;

RIGHT (OUTER) JOIN: Retrieves all the records/rows from the right and the matched records/rows from the left table.

SELECT *
FROM Table_A A
RIGHT JOIN Table_B B
ON A.col = B.col;

SQL Question 6: Find High Credit Risk Customers

At Fair Isaac Corporation (FICO), the firm uses the FICO Score, which is a type of credit score created by the Fair Isaac Corporation, to determine the credit risk of potential customers. The FICO Score ranges from 300-850, with higher scores indicating lower risk.

For this question, assume you are given a customers table with some sample data related to FICO's customers: the customer_id which is a unique identifier of each customer, the first_name and last_name of each customer, and the fico_score which indicates the customer's credit risk. The task is to write a SQL query to determine customers that are high risk, i.e., those with a FICO score less than 580.

customers Example Input:

Example Output:

Answer:

SELECT * 
FROM customers 
WHERE fico_score < 580;

This query works by selecting all columns from customers table where the fico_score is less than 580. The output of this query will be a list of customers that are considered high risk due to their low FICO score.

SQL Question 7: What are the main differences between foreign and primary keys in a database?

To explain the difference between a primary key and foreign key, let's inspect employee data from FICO's HR database:

fico_employees:

+------------+------------+------------+------------+ | 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, employee_id could be a primary key. It is unique for each row in the table and cannot contain null values.

manager_id could be a foreign key. It references the employee_id of the manager of each employee in the table, establishing a relationship between the employees and their managers. This foreign key allows you to easily query the table to find out who a specific employee's manager is, or to find out which employees report to a specific manager.

It is also possible for a table to have multiple foreign keys that reference different primary keys in other tables. For example, the fico_employees table could have additional foreign keys for the department_id of the department where each employee works, and the location_id of the location where each employee is based. Absolutely, let's create a question regarding click-through rates for FICO. It might be little different from the usual product-click context since FICO deals primarily with financial products, but the concept remains the same.

SQL Question 8: Calculate the Click-Through Rates for FICO Ads

FICO has started a digital marketing campaign to promote its products. They want to check the effectiveness of these ads. To calculate this, you have been tasked to find out the click-through rates (CTR) i.e., the number of unique users who clicked on the ad after seeing it.

We have two tables. The 'ad_impressions' table records each time a user is shown an ad. The 'ad_clicks' table logs whenever a user clicks on an ad.

ad_impressions Example Input:

ad_clicks Example Input:

Answer:

We can solve this problem by joining the two tables on the user_id and product_id. We will count the number of unique users who were shown the ad (impressions) and the number of unique users who clicked on the ad (clicks). The CTR is then calculated as clicks / impressions.

SELECT 
  I.product_id,
  COUNT(DISTINCT I.user_id) AS impressions,
  COUNT(DISTINCT C.user_id) AS clicks,
  (COUNT(DISTINCT C.user_id)::decimal / COUNT(DISTINCT I.user_id))*100 AS click_through_rate
FROM 
  ad_impressions I
LEFT JOIN 
  ad_clicks C
ON 
  I.user_id = C.user_id AND I.product_id = C.product_id
GROUP BY I.product_id

Here, ::decimal is used to convert the count (an integer by default) to a decimal allowing us to get a precise percentage for the click-through rate. The LEFT JOIN ensures that we consider all ads, even those which didn't receive any clicks.

To practice a similar SQL problem on DataLemur's free online SQL coding environment, try this SQL interview question asked by Facebook:

SQL Question 9: Maximum and Minimum Credit Scores by Customer Country

Suppose FICO (Fair Isaac Corporation), a credit scoring company, would like to analyze its data to determine which countries have the customers with the highest and lowest credit scores. This could contribute to understanding global credit score patterns and informing risk analysis processes.

For this question, assume we have access to an accounts table, which links together customers and their credit scores, and a customers table, which provides demographic information about customers, including their country of residence.

You are required to write an SQL query that groups the data by country and identifies the customer with the maximum and minimum credit scores in each country.

accounts Example Input:

customers Example Input:

Example Output:

Answer:

The following SQL query will solve this problem:

SELECT
    c.country,
    MAX(a.credit_score) AS max_credit_score,
    MIN(a.credit_score) AS min_credit_score
FROM 
    accounts a
JOIN 
    customers c 
ON 
    a.customer_id = c.customer_id
GROUP BY
    c.country;

This query uses a JOIN to combine relevant information from accounts and customers. It also uses GROUP BY to group this information by country. Crucially, it uses MAX and MIN aggregate functions to find the highest and lowest credit scores within each group of countries, which gives us the desired output.

SQL Question 10: What does EXCEPT / MINUS SQL commands do?

The MINUS/EXCEPT operator is used to remove to return all rows from the first SELECT statement that are not returned by the second SELECT statement.

Note that EXCEPT is available in PostgreSQL and SQL Server, while MINUS is available in MySQL and Oracle (but don't worry about knowing which DBMS supports which exact commands since FICO interviewers aren't trying to trip you up on memorizing SQL syntax).

For a tangible example of EXCEPT in PostgreSQL, suppose you were doing an HR Analytics project for FICO, and had access to FICO's contractors and employees data. Assume that some employees were previously contractors, and vice versa, and thus would show up in both tables.

You could use EXCEPT operator to find all contractors who never were a employee using this query:

SELECT first_name, last_name
FROM fico_contractors

EXCEPT

SELECT first_name, last_name
FROM fico_employees

Preparing For The FICO SQL Interview

The key to acing a FICO SQL interview is to practice, practice, and then practice some more! Besides solving the above FICO SQL interview questions, you should also solve the 200+ FAANG SQL Questions on DataLemur which come from companies like Google, Uber, and Microsoft.

Each problem on DataLemur has hints to guide you, detailed solutions and crucially, there is an interactive SQL code editor so you can instantly run your SQL query and have it executed.

To prep for the FICO SQL interview it is also useful to solve SQL questions from other fintech companies like:

• Experian SQL questions

• Equifax SQL questions

• TransUnion SQL questions

However, if your SQL foundations are weak, forget about jumping right into solving questions – refresh your SQL knowledge with this DataLemur SQL Tutorial.

This tutorial covers topics including CASE/WHEN/ELSE statements and Subquery vs. CTE – both of which pop up often during SQL job interviews at FICO.

FICO Data Science Interview Tips

What Do FICO Data Science Interviews Cover?

In addition to SQL query questions, the other topics tested in the FICO Data Science Interview are:

• Statistics and Probability Questions
• Python Pandas or R Coding Questions
• Product Data Science Interview Questions
• ML Interview Questions
• Behavioral Interview Questions

How To Prepare for FICO Data Science Interviews?

The best way to prepare for FICO 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 Product Analytics, SQL & ML
• Great Reviews (900+ 5-star reviews on Amazon)