SQL interview question walk-through (with reusable framework!)

It works, I promise 😊

I follow this EXACT framework for every SQL interview I have.

I know it works because it has helped me land Data Science & Analytics offers from 10 companies.

My unbeatable SQL framework (Step 6 is the biggest flex)

1. Reframe the question & ask any clarifying questions

2. State assumptions on the table metadata

3. Outline your approach

4. Write the SQL query

5. Review your answer

6. Provide suggestions for improving your query

In today’s newsletter, I’ll walk through exactly how to apply this framework to a SQL interview question. I also wrote about the framework itself in more detail in this article.

If you want to solve this question, before looking at my solution, you can do it here: https://www.interviewmaster.ai/question/whatsapp-call-and-group-chat-interface-usage

Question

What is the maximum number of group chats any user has participated in during the second quarter of 2024 and how does this compare to the average participation rate? This insight will guide decisions on simplifying the chat interface for both heavy and average users

Available tables:

• fct_user_calls(user_id, call_id, call_duration, call_date)

• fct_group_chats(chat_id, user_id, chat_name, chat_creation_date)

Step 1. Reframe the question & ask any clarifying questions

I would reframe this question in my own words. Something like this… “We need to find the maximum number of group chats any single user participated in during April toJune of 2024, and compare this to the average participation rate across all users during the same period.”

Some examples of clarifying questions I would ask

• Should we only count unique group chats per user, or does multiple participation in the same chat count multiple times?

• Are we looking at chat participation based on when the user joined (their first message/activity) or when the chat was created?

• Should we include users who participated in zero group chats in our average calculation?

Step 2. State assumptions on the table metadata

This is your chance to demonstrate expertise with relational databases. I’d state these assumptions to make sure I correctly understand the table structure.

Primary Keys:

• fct_user_calls: Likely (user_id, call_id) composite key

• fct_group_chats: Likely (chat_id, user_id) composite key

Foreign Keys:

• Both tables share user_id as a foreign key reference

Event Occurrence:

• A user can participate in multiple group chats (multiple records per user in fct_group_chats)

• A group chat can have multiple users (multiple records per chat_id)

Unique Values:

• user_id appears in both tables

• chat_creation_date indicates when each group chat was created

Record Representation:

• Each record in fct_group_chats represents one user's participation in one group chat

Step 3. Outline your approach

Normally, I would work backwards from the end, but when I wrote it out backwards… it is confusing to read. So here is the logical step-by-step approach for this question

1. Filter data for Q2 2024 - Focus on group chats created between April 1 and June 30, 2024

2. Count participations per user - Count distinct group chats each user participated in

3. Find the maximum - Identify the highest participation count among all users

4. Calculate the average - Compute mean participation across all users

5. Present comparison - Show both metrics together for analysis

Step 4. Write the SQL query

This is (or should be) easy now that you’ve already done Step 3.

WITH user_participation_q2 AS (
  SELECT 
    user_id,
    COUNT(DISTINCT chat_id) as group_chats_participated
  FROM fct_group_chats
  WHERE chat_creation_date >= '2024-04-01' 
    AND chat_creation_date < '2024-07-01'
  GROUP BY user_id
),
participation_stats AS (
  SELECT 
    MAX(group_chats_participated) as max_participation,
    AVG(group_chats_participated) as avg_participation,
    COUNT(user_id) as total_users
  FROM user_participation_q2
)
SELECT 
  max_participation,
  ROUND(avg_participation, 2) as avg_participation,
  total_users,
  ROUND(max_participation / avg_participation, 2) as max_to_avg_ratio
FROM participation_stats;

Step 5. Review your answer

Read the question again and walk through your answer.

✔️ Make sure you’re answering every part of the question

✔️ Confirm the logic of your query is correct

✔️ Check the syntax of your query

Step 6. Provide suggestions for improving your query

This is the BIGGEST flex you can do in any SQL interview. Go back and criticize your own answer.

I like to answer one of these 3 questions:

• What are edge cases that the query did not catch? E.g. We excluded users who exist in the system but didn't participate in any group chats during Q2 - they're excluded from the average calculation, which might skew results upward

• What are some ways to optimize your query?

  E.g. Use COUNT(*) instead of COUNT(DISTINCT chat_id) if we can guarantee no duplicate (user_id, chat_id) combinations

• What are some drawbacks to this approach?

  E.g. The query assumes chat_creation_date represents when users participated, but users might join chats after creation

And that’s all, folks.

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