9 Salesforce SQL Interview Questions (Updated 2024)

Updated on

January 22, 2024

At Salesforce, SQL is used day-to-day for analyzing customer data to improve CRM solutions and for managing Salesforce Marketing Cloud data. That's why Salesforce asks SQL interview questions during Data Analytics, Data Science, and Data Engineering job interviews.

So, if you're stressed about an upcoming SQL Interview, we've curated 9 Salesforce SQL interview questions to practice, which are similar to commonly asked questions at Salesforce – able to answer them all?

Salesforce SQL Interview Questions

9 Salesforce SQL Interview Questions

SQL Question 1: Calculate the Rolling Average Sales of Each Product

As a data analyst at Salesforce, your task is to analyze the sales data and calculate the rolling 3-month average sales of each product. Given the sales data table, write a SQL query to calculate the rolling 3-month average sales for each product. The rolling average should calculate the current month and the previous 2 months. If there is not enough data for three months, take the average of what is available.

Consider the below sales data -

Example Input:
Example Output:


This SQL query uses a window function to calculate the rolling average of product sales. By partitioning data by product_id and ordering by date, we calculate the average quantity for each row, including two preceding rows. If there are not enough preceding rows, the function will calculate the average with available data.

To solve a similar window function interview problem which uses RANK() on DataLemur's free interactive coding environment, solve this Amazon SQL Interview Question: Amazon Window Function SQL Interview Problem

SQL Question 2: Filter Customer Records Based on Specific Conditions

Given a database of customer interactions with the Salesforce system, write an SQL query to retrieve all records of customers who have either logged in to the system or made a transaction more than 50 times in the past month, but have not contacted customer support in that time period.

The database has the following two tables:

Example Input:
101John Doejohndoe@example.com
102Jane Smithjanesmith@example.com
103Sarah Johnsonsarahjohnson@example.com
104Matthew Taylormatthewtaylor@example.com
Example Input:


For this SQL query, we are focusing on customers who are frequently using the system (either logins or transactions) but haven't contacted support. The subquery filters the activity data for those customers using the system more than 50 times in the last month, excluding those who've contacted support. The outer query then retrieves the relevant customer records.

SQL QUESTION 3: Are the results of a UNION ALL and a FULL OUTER JOIN usually the same?

No, in almost all cases, and for all practical purposes, and do NOT produce the same result.

While both are similar, in that they combine two tables, you can think of joins as increasing the width of the resulting table (you'll have more columns in the result set for a left/inner/right join), whereas a union is used to combine rows which increases the height of the result set but keeps the column count the same.

SQL Question 4: Average Close Time of Tickets in Salesforce

As a customer relationship management solution, Salesforce keeps track of help tickets created by users. For this problem, let's consider you are being asked to find the average number of days it takes to close a ticket.

Example Input:

The dates are in the format YYYY-MM-DD.

Here we're considering only tickets that have been closed, which are the ones that have a .

Your task is to write a SQL query that will provide the average close time of tickets on a monthly basis.

Example Output:


This query first filters out the tickets that have been closed. Then, it forms groups of tickets for each month based on the , and calculates the average close time in days for each group. The result is ordered by the month. Please note that this is a simplified query and might not work perfectly for tickets closing in a different month from their creation or for months with more than 31 days. It serves as a basic demonstration of the use of AVG function. Actual production queries would handle these edge cases as well.

To practice a very similar question try this interactive Amazon Average Review Ratings Question which is similar for calculating averages or this Facebook Average Post Hiatus (Part 1) Question which is similar for manipulating date data.

SQL QUESTION 5: Can you describe the different types of joins in SQL?

In SQL, a 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.

There are four distinct types of JOINs: , , , and .

(INNER) JOIN: Retrieves records that have matching values in both tables involved in the join.

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

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

FULL (OUTER) JOIN: Retrieves all the records where there is a match in either the left or right table.

SQL Question 6: Analysing Click-through-Rate for Salesforce

Salesforce is interested in understanding the click-through rates of their marketing emails. They want to establish a correlation with a specific user and a product. They want to know how many users clicked on each email for a specific product, how many viewed the specific product page after clicking on the email, and how many eventually added the product to their cart.

Salesforce keeps track of the following two tables:

Example Input:
100133201/01/2023 08:32:007812
100248801/01/2023 09:13:007812
100333201/02/2023 10:45:007813
100448801/02/2023 12:30:007813
100533201/03/2023 08:32:007812
Example Input:
500133201/01/2023 08:33:007812Yes
500248801/01/2023 09:15:007812No
500333201/02/2023 10:50:007813Yes
500448801/02/2023 12:33:007813No
500533201/03/2023 08:35:007812Yes

Write a query showing the number of emails clicked, number of product page views, and how many products were added to cart for each product.


Taking the email clicks and product views tables, we join them on the user id and product id, with additional condition that the click and view date corresponds to the same date. We then use aggregate functions to retrieve the number of unique email clicks, unique product views and the number of times the product was added to cart. This query gives you the click-through rates with cart conversion for each product.

To practice a related SQL interview question on DataLemur's free interactive SQL code editor, attempt this SQL interview question asked by Facebook: Facebook App CTR SQL Interview question

SQL QUESTION 7: What's the difference between an inner and a full outer join?

A full outer join returns all rows from both tables, including any unmatched rows, whereas an inner join only returns rows that match the join condition between the two tables.

For an example of each one, say you had sales data exported from Salesforce's Salesforce CRM stored in a datawarehouse which had two tables: and .

: retrieves rows from both tables where there is a match in the shared key or keys.

This query will return rows from the sales and tables that have matching customer id values. Only rows with matching values will be included in the results.

: retrieves all rows from both tables, regardless of whether there is a match in the shared key or keys. If there is no match, NULL values will be returned for the columns of the non-matching table.

Here is an example of a SQL full outer join using the sales and tables:

To practice a related SQL interview question on DataLemur's free interactive SQL code editor, attempt this SQL interview question asked by Facebook: Facebook App CTR SQL Interview question

SQL Question 8: Filtering Customer Records with LIKE keyword

As a Salesforce administrator, you are tasked with examining the customer records specifically looking at customers' email addresses. The use of Salesforce has recently expanded into Europe and you’ve noticed a significant uptake from french businesses. You need to filter all the customers that have email addresses ending with '.fr', which is a common French domain.

Example Input:
1001Todd Murphytodd.murphy@example.fr
1002Amanda Shankamanda.shank@example.com
1003Adria Richardsadria.richards@example.co.uk
1004Ellen Connellyellen.connelly@example.fr
1005Zack Yateszack.yates@example.fr
Example Output:
1001Todd Murphytodd.murphy@example.fr
1004Ellen Connellyellen.connelly@example.fr
1005Zack Yateszack.yates@example.fr


The SQL query below can be used to find all users with '.fr' emails.

This query selects the , , and from users where the ends with '.fr'. The '%' symbol is a wildcard in SQL that matches any number of characters, so '%.fr' matches anything that ends with '.fr'. The LIKE keyword in SQL is used to search for a specified pattern in a column. In this case, we are searching for emails that end with '.fr'.

SQL Question 9: Calculate Salesperson Performance

Salesforce uses a point system to quantify salesperson performance. A salesperson gets 1 point each time they make a sale, 2 points for each sale they make that is over 1000,and3pointsforeachsaletheymakethatisover1000, and 3 points for each sale they make that is over 5000. Can you write a SQL query to determine the total points for each salespeople and find out the percentage of their points that comes from sales over 1000andsalesover1000 and sales over 5000?

Example Input:


This query will give you the total number of points each salesperson earned and the percentage of their points that come from sales over 1000andsalesover1000 and sales over 5000. We use the CASE WHEN clause to assign the correct number of points for each sale, then use the SUM() function to calculate the total points. For the percentage calculations, we use division and the ROUND() function to limit the result to two decimal places.

To practice a very similar question try this interactive Amazon Highest-Grossing Items Question which is similar for calculating top scoring items or this Wayfair Y-on-Y Growth Rate Question which is similar for finding sales growth rate.

p.s. in case this question was tricky, strengthen your SQL foundations with this free SQL tutorial which has 30+ lessons including one on math in SQL.

How To Prepare for the Salesforce SQL Interview

The key to acing a Salesforce SQL interview is to practice, practice, and then practice some more! Beyond just solving the above Salesforce SQL interview questions, you should also solve the 200+ FAANG SQL Questions on DataLemur which come from companies like FAANG tech companies and tech startups. DataLemur Question Bank

Each problem on DataLemur has hints to guide you, fully explained answers along with a discussion board to see how others solved it and best of all, there is an online SQL code editor so you can instantly run your SQL query and have it graded.

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

But if your SQL foundations are weak, don't worry about jumping right into solving questions – improve your SQL foundations with this free SQL tutorial or checkout the SOQL Trailhead path if you are a Salesforce developer.

SQL tutorial for Data Scientists & Analysts

This tutorial covers topics including SUM/AVG window functions and CASE/WHEN/ELSE statements – both of which come up routinely in SQL job interviews at Salesforce.

Salesforce Data Science Interview Tips

What Do Salesforce Data Science Interviews Cover?

For the Salesforce Data Science Interview, in addition to SQL query questions, the other types of questions which are covered:

  • Probability & Stats Questions
  • Python or R Programming Questions
  • Business Sense and Product-Sense Questions
  • Machine Learning Questions
  • Behavioral Interview Questions

How To Prepare for Salesforce Data Science Interviews?

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

  • 201 Interview Questions from Facebook, Google & startups
  • A Crash Course covering SQL, Product-Sense & ML
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Ace the Data Science Interview by Nick Singh Kevin Huo