Free AI LeetCode Communication Coach — Detect duplicates

The problem

How would you detect duplicate records in a customer database? Talk through your thought process. Write from your own role — there is no single correct answer.

Example: two rows with ana@shop.com and different IDs → flagged as duplicate on email

Pick one key, one method, one trade-off.

seen = set()
for email in emails:
    if email in seen:
        return True
    seen.add(email)
return False

How to explain it

1. 1. Restate

   Say the problem in your own words.

   One or two sentences. Show you understood the input, the output, and the goal — not that you memorised the prompt.

2. 2. Approach

   Name the method before you code.

   Brute force first if you need it, then the structure you will use: hash map, two pointers, stack, binary search.

3. 3. Example

   Walk one concrete input.

   Pick small numbers. Say what you store, what you compare, and what you return. Interviewers follow an example more easily than abstract talk.

4. 4. Time and space

   One sentence each.

   After the example, before you claim you are done. “Time is O(n) because we scan once. Space is O(n) for the map.”

5. 5. Edge cases

   Name at least one unusual input.

   Empty input, duplicates, already sorted, overflow. Invite a follow-up: “I would also check …”

• “I will start with a high-level approach, then we can go deeper.”
• “My first idea is to treat email as the identity.”
• “The trade-off is exact match versus fuzzy matching.”

Practise out loud

1. 1. Restate

   I need a practical way to find duplicate customer records. I will pick one identity key first.

2. 2. Approach

   My first idea is email as identity. I would GROUP BY email and keep counts greater than one, or scan once into a hash set.

3. 3. Example

   Two rows with ana@shop.com and different IDs — that is a duplicate on email.

4. 4. Time and space

   Time is O(n). Space is O(n) for the set.

5. 5. Edge cases

   People reuse emails, and some duplicates are typos in the name. I would flag exact email matches first, then a second fuzzy pass if needed.

Common mistakes when explaining Detect duplicates

• Listing five tools instead of one identity key.
• Starting with fuzzy matching before an exact cut.