Flipkart Interview Experience for BA intern
Interview Experience
Overview of Interview Process:Initial ScreeningThe process began with an online assessment consisting of 45 MCQs covering aptitude, verbal ability, and core computer scien...
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Overview of Interview Process: Initial Screening The process began with an online assessment consisting of 45 MCQs covering aptitude, verbal ability, and core computer science topics. There were also 5 SQL coding questions, mostly based on JOINs, which were of easy to medium difficulty. All rounds in the process were eliminatory.
Technical Round 1 The interview started with brief introductions and then moved into a discussion about one of my projects from a Myntra hackathon. The interviewer turned this into a case study-style question, asking about the project’s objective and how I would measure its success using metrics like traffic, engagement, and conversion rates. We also spoke about a few of my other hackathon projects. Next, I was given a medium-level SQL question that required using joins and window functions such as RANK. I was asked to explain the difference between RANK, DENSE_RANK, and ROW_NUMBER. The interviewer pointed out a few mistakes and gave hints along the way, and I was able to solve it by the end. Practicing questions on platforms like DataLemur was definitely useful for this part. Towards the end, there was a product case question based on Instagram, and the scenario involved proposing a new feature that lets users react in multiple ways, similar to LinkedIn, instead of only liking a post. I had to explain how I would convince the Meta team to add this feature using data-backed reasoning, focusing on engagement, retention, and conversion metrics. I received the link for the
next round about an hour and a half after the this one ended.
Technical Round 2 This round also involved discussions around my projects and two case study questions. The first was about Flipkart setting up a dark store (like Blinkit) in my city with limited storage space. I had to explain how I would decide what items to stock using Flipkart’s purchase data. The second case study was about evaluating the success of a new loyalty program and distinguishing whether improvements were due to the program itself or seasonal factors. From what I heard from other candidates, the questions varied a lot depending on the interviewer since some were asked probability or guesstimate questions as well.
About This Question
This is a candidate experience report from a flipkart interview for a swe role (intern level) during the oa round reported in 2025.
It covers the following topics: Sql, Probability Stats .
Difficulty rating: Easy
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About Flipkart Interview Reports
This question was reported by a candidate who interviewed at Flipkart. LeakCode aggregates interview reports from 10+ sources, including 1Point3Acres, Glassdoor, LeetCode Discuss, Blind, Reddit, Indeed, and Nowcoder. Each report is translated where necessary, deduplicated against existing entries, and tagged by company, role, round type, and reporting date.
Use this question as one calibration data point, not a memorization target. Companies typically rotate their question pools every 2-4 months; the exact wording of a 2024 question may differ from what you encounter today. The underlying pattern, difficulty level, and follow-up depth at Flipkart are the higher-signal extractions to take from this report.
For broader preparation context, the Flipkart interview process typically includes a recruiter screen, one or two technical phone screens, and a 4-5 round on-site loop covering coding, system design (at L4+ levels), and behavioral. Reports tagged on LeakCode show the round-by-round distribution and typical difficulty calibration. To browse questions filtered by round type and seniority, use the company hub linked above.
How To Practice This Type of Question
Solve similar problems on LeetCode under timed conditions (25-35 minutes per medium difficulty). The goal is pattern recognition: recognize the underlying technique (sliding window, two-pointer, BFS, memoized recursion, etc.) within 60-90 seconds of reading. Strong candidates verbalize their hypothesis out loud before coding, then iterate based on feedback. Weak candidates dive into implementation immediately, lose time on the wrong approach, and run out of time for follow-ups.
Companies update their question pools every 2-4 months. The exact wording of any given question may have been retired by the time you interview. Focus your prep on the pattern, not the specific problem. The patterns that appear in Flipkart reports consistently are the ones worth investing in; one-off niche problems are not.
During Your Flipkart Round
Apply the standard interview round template: clarify requirements (2-3 minutes), state your approach out loud and confirm direction with the interviewer (3-5 minutes), code with narration (15-25 minutes), test with concrete examples including edge cases (5 minutes), discuss optimization or trade-offs if time permits (5 minutes). This template is universally accepted across FAANG and adjacent companies; deviating from it produces weaker interviewer feedback signal.
The single most predictive failure mode in Flipkart reports tagged "no hire": not asking clarifying questions. Interviewers are explicitly trained to weight this. Strong candidates ask 3-5 clarifying questions even on problems that look obvious; weak candidates dive into code immediately. The clarifying-question check is often the first signal recorded in the interviewer's written notes.