Indian Unicorn

GRiD / SDE-1, Runway, PM Intern, Data Analyst

Role-specific online assessment (coding / MCQ / SQL-case) + multiple interviews

Flipkart Interview Questions & Placement Prep (2026)

Flipkart does not run one common fresher exam. GRiD/SDE-1 is heavily DSA and design oriented, Runway is an early-career DSA program, and PM Internship and Data Analyst assessments lean on aptitude, product/business thinking, SQL and case analysis. Prepare for each program separately using the structures, syllabus and question banks below.

SDE-1: 2–3 medium–hard coding problems in 90–120 min · Runway: ~30 MCQs + 2 DSA interviews · PM & Data Analyst: aptitude, SQL, case study

Last updated: 12 August 2026

Exam Pattern

SDE-1: 2–3 medium–hard coding problems in 90–120 min · Runway: ~30 MCQs + 2 DSA interviews · PM & Data Analyst: aptitude, SQL, case study

Section	Questions	Time	Type	Difficulty
SDE-1 · Online coding assessment	2–3 problems	90–120 min	Coding	Medium–Hard
SDE-1 · Technical interview 1	1–2 problems	45–60 min	DSA + projects	Medium
SDE-1 · Technical interview 2	1–2 problems	45–60 min	Advanced DSA + CS fundamentals	Hard
SDE-1 · Hiring manager round	Varies	30–45 min	Behavioural / ownership	Medium
Runway · Online MCQ quiz	~30	Timed	MCQ (coding, DSA, OOP, OS)	Easy–Medium
Runway · Technical interviews (x2)	~2 DSA each	45 min each	Live coding	Medium
PM Intern · Aptitude + product case	Varies	Timed	Quant, prioritisation, written case	Medium
Data Analyst · Online assessment	Varies	45–90 min	Aptitude, SQL, data interpretation	Medium

Section-Wise Syllabus

Everything that appears in the assessment, grouped by section.

1. SDE-1 & Runway — DSA core

Arrays & strings

• Prefix sums
• Difference arrays
• Two pointers
• Sliding window
• Frequency maps
• Sorting-based optimisation
• Subarrays & subsequences
• String hashing
• Palindromes
• Anagrams
• Pattern matching
• Longest substring problems

Linked lists

• Reverse a list
• Reverse in groups of k
• Detect a cycle
• Find cycle-entry point
• Middle node
• Merge two sorted lists
• Merge k sorted lists
• Palindrome list
• Clone list with random pointers

Stacks & queues

• Balanced parentheses
• Min stack
• Next greater element
• Largest rectangle in histogram
• Sliding-window maximum
• Queue using stacks
• Stack using queues
• Monotonic stack patterns

Trees & BSTs

• Recursive & iterative traversals
• Level-order traversal
• Height & diameter
• Lowest common ancestor
• Validate BST
• Serialize & deserialize
• Build tree from traversals
• Boundary traversal
• Vertical order
• Maximum path sum
• Tree views
• BST insert/delete/search

Graphs

• BFS & DFS
• Connected components
• Cycle detection
• Bipartite graph
• Topological sort
• Course scheduling
• Shortest path (unweighted)
• Dijkstra
• Minimum spanning tree
• Disjoint-set union
• Number of islands
• Word ladder
• Strongly connected components

Dynamic programming

• 1D DP
• Grid DP
• 0/1 knapsack
• Unbounded knapsack
• Coin change
• Longest increasing subsequence
• Longest common subsequence
• Edit distance
• Partition DP
• Stock buy–sell
• House robber
• Interval DP basics
• Tree DP basics

Advanced (SDE-1)

• Heaps & priority queues
• Trie
• Segment tree
• Fenwick tree
• Binary search on answer
• Bit manipulation
• Greedy proofs
• Range queries
• Caching & LRU cache
• Object-oriented design
• Basic LLD
• API & database design

2. PM Internship — product sense & prioritisation

Product sense questions to master

• Who is the target user?
• What problem are they facing?
• How frequently does it occur?
• What is the current workaround?
• What is the expected business value?
• How would you measure success?
• What trade-offs exist?
• What would you build first?

Product case framework

• Clarify user and objective
• Segment users
• Identify pain points
• Prioritise the biggest pain point
• Propose a solution
• Define MVP scope
• Select success metrics
• Discuss risks and follow-up experiments

Prioritisation

• Priority = (Reach × Impact × Confidence) / Effort
• RICE: Reach, Impact, Confidence, Effort
• Market-sizing estimation
• Root-cause analysis
• Written product proposal
• Business case interpretation

E-commerce metrics

• Conversion rate
• Add-to-cart rate
• Checkout completion rate
• Average order value
• Customer acquisition cost
• Customer lifetime value
• Repeat purchase rate
• Churn
• Cancellation rate
• Return rate
• Delivery success rate
• Search-to-purchase conversion
• Seller fulfilment rate
• Net promoter score

3. Data Analyst — SQL, Python & statistics

SQL

• SELECT, WHERE, ORDER BY
• GROUP BY & HAVING
• Inner, left & self joins
• Subqueries
• Common table expressions
• Window functions
• ROW_NUMBER, RANK, DENSE_RANK
• LAG & LEAD
• Date functions
• Null handling
• Conditional aggregation
• Duplicate detection
• Cohort analysis
• Funnel analysis

Python & pandas

• Lists, dictionaries, sets
• Functions & comprehensions
• NumPy arrays
• pandas Series & DataFrames
• read_csv
• groupby
• merge & join
• pivot_table
• Missing-value handling
• Duplicate removal
• Sorting & filtering
• Date conversion
• Aggregation
• Basic visualisation
• Descriptive statistics

Statistics

• Mean, median, mode
• Variance & standard deviation
• Percentiles
• Correlation vs causation
• Probability
• Sampling
• Confidence intervals
• Hypothesis testing
• A/B testing
• Statistical significance
• Type I & Type II errors
• Regression basics
• Outliers
• Skewness

4. Complexity targets (SDE-1)

Aim for these bounds

• Array traversal — O(n)
• Sorting-based solution — O(n log n)
• Pair / frequency problem — O(n) average
• Tree traversal — O(n)
• Graph traversal — O(V + E)
• Dijkstra with heap — O((V + E) log V)
• Range query / update — O(log n)
• Binary search on answer — O(n log R)

Most Asked Topics

Highest-frequency areas across recent test papers.

Smallest missing positive integer

A classic Flipkart SDE question — expected in O(n) time and O(1) extra space using in-place cyclic placement.

Sliding window & substring problems

Longest substring without repeating characters and minimum window substring repeat across slots.

House-robber style DP

Reported as a coupon-selection problem: maximise discount without picking two consecutive coupons.

LRU cache design

O(1) get and put using a hash map plus doubly linked list — also an LLD warm-up.

Graph shortest paths

Delivery-route problems with non-negative weights map to Dijkstra with a heap.

Two heaps / running median

Stream of product ratings, return median after each insertion.

SQL window functions (Data Analyst)

Ranking products within categories and repeat-purchase detection are staples.

Product prioritisation (PM)

RICE-style reasoning and metric selection matter more than a 'right' answer.

Free Resources

Curated practice links per section.

DSA practice

SQL & analytics

Product management

System design & LLD

50+ DSA Practice Problems

Solve in order — arrays and strings first, then sorting and recursion.

Arrays & strings

Smallest missing positive integer
Longest substring without repeating characters
Maximum subarray sum
Merge intervals
Group anagrams
Minimum window substring
Product of array except self
Subarray sum equals K

Linked lists, stacks & heaps

Reverse nodes in k-groups
Detect and remove cycle
Merge k sorted lists
LRU cache
Sliding-window maximum
Largest rectangle in histogram
Median from a data stream
Next greater element

Trees & graphs

Lowest common ancestor
Serialize & deserialize binary tree
Maximum path sum
Number of islands
Course schedule (topological sort)
Dijkstra shortest path
Word ladder
Redundant connection (DSU)

Dynamic programming

House robber (coupon selection)
Coin change
0/1 knapsack
Longest increasing subsequence
Edit distance
Partition equal subset sum
Best time to buy & sell stock III
Matrix chain / interval DP basics

Previous Year Questions & Patterns

Repeat coding questions with the expected approach.

GRiD / SDE-1 previous-year-style

Find the smallest missing positive integer in an unsorted array — O(n) time, O(1) extra space.
A customer picks coupons from an array but cannot pick two consecutive coupons — maximise the discount (house-robber DP).
Design an LRU cache with O(1) get and put (hash map + doubly linked list).
Find the shortest delivery route in a graph with non-negative edge weights (Dijkstra).
Given product prices and a budget, maximise the number of products purchased (sort + prefix sums).
Given a stream of product ratings, return the median after each insertion (two heaps).
Find the lowest common manager of two employees in an organisation tree.
Merge overlapping delivery time intervals.

Runway-style MCQs

Which traversal of a BST gives sorted order? → Inorder.
Average lookup complexity of a hash table? → O(1).
Which data structure is used for BFS? → Queue.
Which normal form removes partial dependency? → Second Normal Form (2NF).
int x = 3; for (int i = 1; i <= 3; i++) x *= i; → prints 18.
for (int i = 1; i <= n; i *= 2) → O(log n).

PM Internship cases

Cart abandonment increased — what would you investigate first? (payment failures, delivery charges, login issues, stock/price changes, coupon rejection, page performance)
Design a feature for users ordering groceries for the first time (segment → pain points → onboarding → simplified categories → trust signals → metrics).
Improve delivery speed or return convenience? Clarify segment, objective and data, then compare reach, impact, confidence and effort.
Why did conversion fall despite traffic increasing?
How would you measure the success of a new recommendation system?
Design a product for users with poor internet connectivity.

Data Analyst questions

Find customers who placed more than one order in the same week (group by user + week, HAVING COUNT(*) > 1).
How would you treat missing delivery dates? Diagnose why they are missing, distinguish MNAR from random, avoid blind imputation, report data quality separately.
Sales increased but margins declined — which dimensions would you analyse? (category mix, discounts, logistics cost, return rate, seller mix, CAC, product margins)
How would you identify fake reviews?
Delivery delays increased by 15% — how would you investigate?
How would you explain a dashboard to a non-technical stakeholder?

20+ SQL Query Bank

Write these out by hand — joins and subqueries are asked in both the test and the interview.

Aggregation & ranking

Second-highest salary

SELECT MAX(salary)
FROM employees
WHERE salary < (
    SELECT MAX(salary) FROM employees
);

Top-selling category

SELECT category,
       SUM(quantity) AS units_sold
FROM orders
GROUP BY category
ORDER BY units_sold DESC;

Rank products within categories

SELECT product_id,
       category,
       sales,
       RANK() OVER (
           PARTITION BY category
           ORDER BY sales DESC
       ) AS category_rank
FROM product_sales;

Customer behaviour

Repeat purchasers

SELECT user_id
FROM orders
GROUP BY user_id
HAVING COUNT(DISTINCT order_id) > 1;

Multiple orders in the same week

SELECT user_id,
       DATE_TRUNC('week', order_time) AS order_week,
       COUNT(*) AS orders
FROM orders
GROUP BY user_id, DATE_TRUNC('week', order_time)
HAVING COUNT(*) > 1;

DBMS & Core Interview Q&A

Short answers you should be able to say out loud.

Which normal form removes partial dependency?

Second Normal Form (2NF).

Difference between RANK and DENSE_RANK?

RANK leaves gaps after ties; DENSE_RANK assigns consecutive ranks.

When does HAVING run compared to WHERE?

WHERE filters rows before grouping; HAVING filters aggregated groups after GROUP BY.

What does a LEFT JOIN return when no match exists?

All left-table rows with NULLs for right-table columns.

How do you detect duplicates in SQL?

GROUP BY the candidate key and filter HAVING COUNT(*) > 1.

What is a CTE useful for?

Naming intermediate result sets to make multi-step analytics queries readable and reusable.

OOPs Concepts + 10 Code Snippets

Theory plus the exact snippets asked in the technical round.

Encapsulation

Bundle data with the methods that operate on it and expose only validated behaviour — key for cart, pricing and coupon classes.

Abstraction

Expose intent (PaymentMethod.pay()) and hide implementation (UPI, card, wallet).

Polymorphism

One interface, many implementations — discount strategies, notification channels, delivery modes.

Composition over inheritance

Prefer 'has-a' for order + pricing + delivery so behaviour can be swapped without deep hierarchies.

SOLID

Single responsibility, open/closed, Liskov, interface segregation, dependency inversion — asked directly in LLD rounds.

LRU cache skeleton (hash map + doubly linked list)

cpp

class LRUCache {
    int cap;
    list<pair<int,int>> dq;                       // front = most recent
    unordered_map<int, list<pair<int,int>>::iterator> mp;
public:
    LRUCache(int capacity) : cap(capacity) {}

    int get(int key) {
        if (!mp.count(key)) return -1;
        dq.splice(dq.begin(), dq, mp[key]);
        return mp[key]->second;
    }

    void put(int key, int value) {
        if (mp.count(key)) { dq.erase(mp[key]); }
        else if ((int)dq.size() == cap) {
            mp.erase(dq.back().first);
            dq.pop_back();
        }
        dq.push_front({key, value});
        mp[key] = dq.begin();
    }
};

Smallest missing positive integer — O(n) / O(1)

cpp

int firstMissingPositive(vector<int>& a) {
    int n = a.size();
    for (int i = 0; i < n; ++i) {
        while (a[i] > 0 && a[i] <= n && a[a[i] - 1] != a[i])
            swap(a[i], a[a[i] - 1]);
    }
    for (int i = 0; i < n; ++i)
        if (a[i] != i + 1) return i + 1;
    return n + 1;
}

Category revenue summary (pandas)

sql

-- pandas equivalent used in Data Analyst rounds
-- sales_by_category = (
--     df.groupby("category", as_index=False)["sales"]
--       .sum()
--       .sort_values("sales", ascending=False)
-- )
SELECT category, SUM(sales) AS sales
FROM orders
GROUP BY category
ORDER BY sales DESC;

Operating System Basics

Scheduling, deadlock and memory management essentials.

Concepts asked in SDE interviews

• Process vs thread
• Context switching
• Deadlock conditions and prevention
• Mutex vs semaphore
• Paging & virtual memory
• Producer–consumer problem

Systems knowledge for design rounds

• Caching layers and eviction policies
• Idempotency for payment retries
• Rate limiting
• Consistency vs availability trade-offs
• Database indexing basics

4-Week Preparation Roadmap

From zero to test-ready in 28 days.

Week 1

Arrays, strings & complexity

6–8 array/string problems
Revise complexity analysis
1 timed 45-min set

Goal: Fluent with hashing, sorting-based optimisation and complexity reasoning.

Week 2

Two pointers, sliding window, prefix sums, binary search

6 pattern problems
1 binary-search-on-answer problem

Goal: Recognise window/prefix patterns from problem wording alone.

Week 3

Linked lists, stacks, queues & heaps

5 problems
1 design problem (LRU / min stack)

Goal: Write pointer-heavy code without bugs on the first attempt.

Week 4

Trees, BSTs, recursion & backtracking

5 tree problems
1 backtracking problem

Goal: Cover traversals, LCA, views, serialize/deserialize.

Week 5

Graphs, BFS/DFS, topological sort & DSU

5 graph problems
Revise Dijkstra & MST

Goal: Model any real-world problem as a graph quickly.

Week 6

Dynamic programming & greedy

5 DP problems
1 greedy proof exercise

Goal: Solve knapsack, LIS, LCS, edit distance and stock DP from scratch.

Week 7

Tries, range queries, LLD & system-design basics

3 advanced problems
1 LLD design write-up

Goal: Be able to design a small module end-to-end with classes and APIs.

Week 8

Timed contests, mocks & project revision

1 timed 90-min contest
1 mock interview
Project deep-dive prep

Goal: Simulate the real assessment and polish project storytelling.

Exam Day Strategy

Score targets and time allocation.

Section targets

Coding assessment (90–120 min): Read all problems first, solve the easiest fully, then attack partial scoring on the hardest.
Hidden test cases: Handle empty input, single element, duplicates, negatives and overflow before submitting.
Technical interview: Clarify constraints → brute force → optimise → complexity → dry run → edge cases → clean code.
PM case: Clarify user & objective, segment, prioritise one pain point, propose MVP, define metrics, close with risks.
Data Analyst round: Clarify the metric definition, check data quality, state approach, validate, convert insight into a recommendation.

Coding time split (50 min)

Problem 1 (easiest): 20–25 min — aim for full score
Problem 2 (medium): 30–35 min — optimise only after a working solution
Problem 3 (hard): 30–40 min — get partial credit with a brute force if needed
Last 10 min: re-run edge cases and resubmit

Interview Process

What follows the online test.

SDE-1 / GRiD

• Online coding assessment
• Technical interview 1 — DSA, projects, internships
• Technical interview 2 — advanced DSA, optimisation, CS fundamentals
• Hiring manager — behavioural, ownership, product thinking
• HR / final discussion — role fit, location, joining

Runway

• Online MCQ quiz (~30 questions: coding, DSA, OOP, OS)
• Technical interview 1 — ~2 DSA problems
• Technical interview 2 — ~2 DSA problems
• Program or internship offer

PM Internship

• Application & resume screening
• Aptitude / case assessment
• Product case or written response
• Product interview — users, metrics, trade-offs
• Hiring manager / HR

Data Analyst

• Online assessment — aptitude, SQL, data interpretation
• Technical interview — SQL, Python, statistics
• Business case interview — e-commerce metrics
• HR / manager discussion

Mock Test Plan

Four simulations to run before the real test.

Mock 1 — SDE-1 assessment simulation

• 3 problems in 90 minutes
• 1 easy, 1 medium, 1 hard
• No editorial until submission
• Review complexity after each problem

Mock 2 — Runway quiz

• 30 MCQs in 30 minutes
• Coding output, DSA, OOP and OS mix
• Target 80%+ accuracy

Mock 3 — PM case

• 45-minute product case
• Written proposal in 20 minutes
• Metric definition and MVP scoping

Mock 4 — Data Analyst

• 6 SQL queries in 45 minutes
• 1 pandas cleaning task
• 1 e-commerce case with recommendation

Final Checklist

Tick all of these before test day.

Can solve smallest-missing-positive and LRU cache from memory

Comfortable with Dijkstra, topological sort and DSU implementations

Know the complexity target table for each problem type

Have 2 projects ready with architecture, trade-offs and hardest bug

Practised 3 full-length timed coding assessments

PM: RICE prioritisation and 15 e-commerce metrics memorised

Data Analyst: window functions, cohort and funnel queries written from scratch

Prepared behavioural stories for ownership, conflict and failure

Recent Interview Experiences

First-hand write-ups from students who actually sat the Flipkart process.

Be the first to share a Flipkart interview experience.

Frequently Asked Questions

Is there one common Flipkart fresher exam?

No. GRiD/SDE-1, Runway, PM Internship and Data Analyst are separate role-specific programs with different assessment formats, so prepare for the one you are applying to.

How hard is the SDE-1 online assessment?

Reports describe two or three medium-to-hard DSA problems in 90–120 minutes with hidden test cases and partial scoring — some slots use three problems in 90 minutes.

What does the Runway quiz test?

Roughly 30 easy-to-medium MCQs on coding, DSA, OOP and operating systems, followed by two DSA-focused technical interviews.

How much DSA do PM and Data Analyst roles need?

Far less. Replace advanced DSA practice with product cases, SQL, Python/pandas, statistics and e-commerce metrics.

How is Flipkart different from IT-services mass hiring?

It is substantially more DSA-heavy: expect medium-to-hard coding problems and multiple technical interviews instead of a single aptitude-led test.

Want the full structured prep for Flipkart?

The Premium Vault covers Aptitude, Core CS, DSA, SQL and HR rounds with module-wise courses and drill sheets.

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Core CS subjects for this pattern

DBMS & SQL Essentials

Normalisation, keys, transactions and the SQL queries asked in written rounds.

OOPs Essentials for Interviews

The four pillars with the exact code snippets interviewers ask you to explain.

Operating System Essentials

Processes, scheduling, deadlock and memory management in interview-ready form.

Recommended practice path

Most Asked DSA Questions Sheet

High-frequency problems for candidates targeting Flipkart.