11 Moody's SQL Interview Questions (Updated 2024)
Data Science, Data Engineering and Data Analytics employees at Moody's use SQL to analyze large financial datasets, including credit reports and market data, as well as maintain databases that facilitate efficient credit rating predictions by identifying high-risk borrowers. That is the reason why Moody's asks prospective hires SQL interview questions.
To help prep you for the Moody's SQL interview, here's 11 Moody's SQL interview questions in this blog.
11 Moody's SQL Interview Questions
SQL Question 1: Calculating Monthly Average Credit Score
As an analyst at Moody's, you are tasked with analyzing a database that contains credit score information about customers. Your task is to write a PostgreSQL query that will calculate the average credit score for each month for each customer. The credit score is assumed to be a number between 300 and 850.
The table is structured as follows:
Example Input:
| score_id | customer_id | date | credit_score |
|---|---|---|---|
| 1 | 1001 | 01/01/2022 | 650 |
| 2 | 1001 | 02/02/2022 | 670 |
| 3 | 1002 | 01/01/2022 | 720 |
| 4 | 1002 | 02/01/2022 | 740 |
| 5 | 1003 | 02/02/2022 | 800 |
| 6 | 1003 | 03/01/2022 | 810 |
We want to generate a report that shows the average credit score each month for each customer in the following format:
Example Output:
| Month | Customer | average_score |
|---|---|---|
| 1 | 1001 | 650 |
| 1 | 1002 | 720 |
| 2 | 1001 | 670 |
| 2 | 1002 | 740 |
| 2 | 1003 | 800 |
| 3 | 1003 | 810 |
Answer:
You can solve this by using the window function over the partition of the month and customer_id in PostgreSQL.
This answer calculates the monthly average credit score for each customer partitioned by customer_id and month. It does this by using the window function over this defined partition. The function is used to get the month from the date column, and this, along with the customer_id, is used to partition the data.
To solve another window function question on DataLemur's free online SQL code editor, solve this Google SQL Interview Question:
SQL Question 2: 2nd Highest Salary
Given a table of Moody's employee salaries, write a SQL query to find the 2nd highest salary amongst all the employees.
Moody's Example Input:
| employee_id | salary |
|---|---|
| 1 | 2500 |
| 2 | 800 |
| 3 | 1000 |
| 4 | 1200 |
Example Output:
| second_highest_salary |
|---|
| 1200 |
Write a SQL query for this interview question and run your code right in the browser:
Answer:
You can find a detailed solution with hints here: 2nd Highest Salary.
SQL Question 3: What are some similarities and differences between unique and non-unique indexes?
While both types of indexes improve the performance of SQL queries by providing a faster way to lookup rows of data, a unique index enforces the uniqueness of the indexed columns while a non-unique index allows duplicate values in the indexed columns.
Suppose you had a table of Moody's employees. Here's an example of a unique index on the column:
This index would ensure that no two Moody's employees have the same , which could be used as a unique identifier for each employee.
Here's a non-unique index example example on the column:
This index would not enforce uniqueness, but it could be used to improve the performance of queries that filter or sort the data based on the column. For example, if you want to quickly retreive all Data Scientists, the database can use the index to efficiently locate and retrieve the desired records without having to do a full table scan on all Moody's employees.
SQL Question 4: Managing Risk Ratings and Corporate Bonds
Moody's, being a major player in the financial industry, primarily deals with risk ratings, bonds, entities and the corresponding relationships. The company has to keep a track of the risk rating changes of various entities and its overall impact on the corporate bonds that these entities issue. There might be a requirement to find out the entities whose ratings have dropped in the last month and the details of the bonds they have issued in the past.
The problem can be modeled using three tables:
| entity_id | entity_name |
|---|---|
| 1 | Entity A |
| 2 | Entity B |
| id | entity_id | ratings | date_updated |
|---|---|---|---|
| 1 | 1 | AAA | 2022-09-01 |
| 2 | 1 | BBB | 2022-10-01 |
| 3 | 2 | AAB | 2022-09-01 |
| id | entity_id | issue_date | bond_name |
|---|---|---|---|
| 1 | 1 | 2022-01-01 | Bond 1 |
| 2 | 2 | 2022-02-01 | Bond 2 |
The task is to write an SQL query that fetches the names of entities whose ratings have dropped in the last month and the names of the bonds they issued. This information helps in analyzing the credit risk associated with these entities and their bonds.
Answer:
In this query, a CTE is created which fetches the entity_ids where the rating has dropped in the last month. This entity_ids is then used to fetch the corresponding bonds from table. We assume here that the ratings have a numerical mapping which allows for comparison.
SQL Question 5: Could you describe the function of UNION in SQL?
The operator merges the output of two or more statements into a single result set. The two SELECT statements within the UNION must have the same number of columns and the data types of the columns are all compatible.
For example, if you were a Data Analyst on the marketing analytics team at Moody's, this statement would return a combined result set of both Moody's's Google and Facebook ads that have more than 300 impressions:
SQL Question 6: Filter Customer Records With Certain Conditions
In the customer database of Moody's, a global integrated risk assessment firm, we store the following information for each customer: customer ID, first name, last name, email, country, risk rating, portfolio type and the sentiment.
Your task is to write an SQL query that filters only those customers that have a 'bearish' sentiment, a risk rating of less than 3 and belong to either 'fixed income' or 'equity' portfolio type. Sort the results by last name.
Example Input:
| customer_id | first_name | last_name | country | risk_rating | portfolio_type | sentiment | |
|---|---|---|---|---|---|---|---|
| 101 | John | Doe | johndoe@example.com | USA | 2 | equity | bearish |
| 102 | Jane | Smith | janesmith@example.com | UK | 3 | fixed income | neutral |
| 103 | Alice | Johnson | alicejohnson@example.com | Canada | 4 | commodity | bullish |
| 104 | Bob | Brown | bobbrown@example.com | Australia | 1 | fixed income | bearish |
| 105 | Charlie | Davis | charliedavis@example.com | Germany | 5 | real estate | neutral |
Example Output:
| customer_id | first_name | last_name | country | risk_rating | portfolio_type | sentiment | |
|---|---|---|---|---|---|---|---|
| 104 | Bob | Brown | bobbrown@example.com | Australia | 1 | fixed income | bearish |
| 101 | John | Doe | johndoe@example.com | USA | 2 | equity | bearish |
Answer:
This query selects all fields from the table where the sentiment is 'bearish', risk rating is less than 3, and the portfolio type is either 'fixed income' or 'equity'. The results are sorted in ascending order by customer's last name.
SQL Question 7: What's the major difference between and ?
Before we address vs. question, let's address the clause which is used in a statement to group rows into a set of summary rows. It's typically used in conjunction with aggregate functions such as:
- `MIN
The clause is used to filter the groups created by the clause. It's 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.
For example:
This query retrieves the total salary for each department and groups the rows by department. The clause then filters the groups to include only Moody's departments where the total salary is greater than $1 million
Note that the clause must be used after the clause and can only reference columns listed in the clause or aggregated columns selected by the statement.
SQL Question 8: Average Credit Score by Sector
As a Moody's analyst, you're interested in assessing the health of various industry sectors. Using the Moody's database, can you return a list of all industry sectors along with their average credit score?
Example Input:
| company_id | company_name | sector |
|---|---|---|
| 1 | Company A | Technology |
| 2 | Company B | Healthcare |
| 3 | Company C | Technology |
| 4 | Company D | Finance |
| 5 | Company E | Healthcare |
Example Input:
| score_id | company_id | date_of_score | credit_score |
|---|---|---|---|
| 10 | 1 | 06/08/2020 | 85 |
| 20 | 2 | 06/10/2020 | 65 |
| 30 | 3 | 06/18/2020 | 70 |
| 40 | 4 | 07/26/2020 | 90 |
| 50 | 5 | 07/05/2020 | 80 |
Example Output:
| sector | avg_credit_score |
|---|---|
| Technology | 77.50 |
| Healthcare | 72.50 |
| Finance | 90.00 |
Answer:
This query calculates the average credit score by industry sector. It first joins the table and the table on the company id. Then, it groups the rows by sector and calculates the average credit score for each group.
To practice a very similar question try this interactive Amazon Highest-Grossing Items Question which is similar for finding top entities in categorizations or this CVS Health Pharmacy Analytics (Part 1) Question which is similar for calculating averages of subgroups.
SQL Question 9: Analyzing Click-Through-Rates for Moody's Ad Campaigns
For a company like Moody's, which provides financial research on stocks and bonds, they can utilize click-through-data to improve their online ad campaigns. Consider a case where Moody's is promoting various financial services (product line) through digital ad campaigns. Each ad campaign has a unique id, and people seeing these ads are potential customers. A customer might click on the ad (click-through) and then proceed to request more information about the service (conversion).
Under these circumstances, management decides they want to analyze click-through conversion rates for their ad campaigns, comparing clickthrough rate from viewing the ad to requesting more info about a particular service.
You are given two tables - 'ads' storing ad data, and 'conversions' storing conversion data. Write a PostgreSQL query to calculate the click-through conversion rate for each product line.
Example Input:
| ad_id | campaign_id | product_line | impressions | clicks |
|---|---|---|---|---|
| 101 | 3001 | 'Equity Research' | 12000 | 800 |
| 102 | 3002 | 'Fixed Income Research' | 8000 | 200 |
| 103 | 3003 | 'Corporate Governance Services' | 10000 | 700 |
| 104 | 3004 | 'Credit Ratings' | 7000 | 400 |
Example Input:
| conversion_id | ad_id | conversion |
|---|---|---|
| 201 | 101 | 400 |
| 202 | 102 | 100 |
| 203 | 103 | 350 |
| 204 | 104 | 250 |
Answer:
This query joins the ads table and the conversions table based on the ad_id. It then calculates the click-through conversion rate as the total number of conversions divided by total clicks for each product line. The resulting click-through conversion rates are sorted in descending order.
To practice a related problem on DataLemur's free online SQL coding environment, solve this Facebook SQL Interview question:
SQL Question 10: Can you explain the meaning of database denormalization?
Denormalization is the practice of altering a database schema in a way that breaks the normalization rules (1st, 2nd, 3rd normal forms).
Denormalization is done to improve the performance of the database when it is being used for online analytics processing (OLAP), rather than online transaction processing (OLTP) use cases.
For these OLAP use cases, you're bottleneck frequently is joining multiple tables, but de-normalizing your database cuts these outs.
Denormalization can improve query performance by reducing the number of expensive joins required to retrieve data, but it comes with its own drawbacks too. By adding redundant columns, you incur more data storage costs, and there's need for more complex update and delete operations in order to maintain data integrity across all the duplicated data. Thus, it's important to carefully consider the trade-offs involved before implementing denormalization.
SQL Question 11: Average Rating of Bonds
As a data analyst for Moody's, an American business and financial services company specialising in credit ratings and research across bonds issued by commercial and government entities, you are tasked to examine the rating data.
Your task is to find out the average rating given to each bond per month. Use the "bond_ratings" and "dates" tables given below for your query. In the bond_ratings table, ratings are on a scale from 1 to 5, where 5 is the best.
Example Input:
| rating_id | rater_id | date_id | bond_id | rating |
|---|---|---|---|---|
| 101 | 457 | 20210101 | 2001 | 2 |
| 102 | 758 | 20210201 | 2001 | 5 |
| 103 | 328 | 20210201 | 2002 | 3 |
| 104 | 758 | 20210301 | 2002 | 4 |
| 105 | 457 | 20210301 | 2001 | 4 |
| 106 | 328 | 20210401 | 2001 | 3 |
| 107 | 758 | 20210401 | 2002 | 4 |
| 108 | 328 | 20210501 | 2002 | 5 |
| 109 | 457 | 20210501 | 2001 | 2 |
| 110 | 758 | 20210601 | 2001 | 4 |
Example Input:
| date_id | date | month | year |
|---|---|---|---|
| 20210101 | 2021-01-01 | 1 | 2021 |
| 20210201 | 2021-02-01 | 2 | 2021 |
| 20210301 | 2021-03-01 | 3 | 2021 |
| 20210401 | 2021-04-01 | 4 | 2021 |
| 20210501 | 2021-05-01 | 5 | 2021 |
| 20210601 | 2021-06-01 | 6 | 2021 |
Example Output:
| month | bond | avg_rating |
|---|---|---|
| 1 | 2001 | 2.00 |
| 2 | 2001 | 5.00 |
| 2 | 2002 | 3.00 |
| 3 | 2001 | 4.00 |
| 3 | 2002 | 4.00 |
| 4 | 2001 | 3.00 |
| 4 | 2002 | 4.00 |
| 5 | 2001 | 2.00 |
| 5 | 2002 | 5.00 |
| 6 | 2001 | 4.00 |
Answer:
This query joins the and tables on the column. It then groups the records by month and bond_id, and calculates the average rating using the function. Finally, the query sorts the results by month and bond_id.
Moody's SQL Interview Tips
The key to acing a Moody's SQL interview is to practice, practice, and then practice some more! In addition to solving the above Moody's SQL interview questions, you should also solve the 200+ DataLemur interview questions which come from companies like FAANG tech companies and tech startups.
Each exercise has hints to guide you, detailed solutions and best of all, there is an interactive coding environment so you can right in the browser run your SQL query and have it checked.
To prep for the Moody's SQL interview it is also wise to practice SQL questions from other financial services companies like:
- S&P Global SQL questions
- TransUnion SQL questions
- Dun & Bradstreet SQL questions
- Fiserv SQL questions
Learn how Moody's is harnessing the power of GenAI to revolutionize the financial industry and drive growth!
However, if your SQL coding skills are weak, forget about diving straight into solving questions – strengthen your SQL foundations with this free SQL for Data Analytics course.
This tutorial covers topics including Self-Joins and handling timestamps – both of which show up often in SQL job interviews at Moody's.
Moody's Data Science Interview Tips
What Do Moody's Data Science Interviews Cover?
Beyond writing SQL queries, the other types of questions tested in the Moody's Data Science Interview are:
- Statistics and Probability Questions
- Coding Questions in Python or R
- Product Data Science Interview Questions
- ML Interview Questions
- Behavioral Interview Questions centered on Moody's company values
How To Prepare for Moody's Data Science Interviews?
To prepare for Moody's Data Science interviews read the book Ace the Data Science Interview because it's got:
- 201 interview questions taken from Google, Microsoft & tech startups
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Don't ignore the behavioral interview – prep for that with this list of common Data Scientist behavioral interview questions.