Interview guides · Msft

Msft Software Engineer Interview Guide

Msft Software Engineer interview guide: reported rounds, questions and how candidates describe the process.

The 60-second brief

As a Software Engineer at Msft, you are at the core of building and scaling technologies that impact billions of users worldwide. Whether you are working on Azure cloud infrastructure, Microsoft 365 productivity suites, or cutting-edge AI research, your work directly shapes the digital landscape. This role is not just about writing code; it is about solving complex, high-scale problems that require deep technical rigor, collaborative spirit, and a "learn-it-all" mindset. The work you perform here is highly visible and deeply integrated into the global economy. You will tackle challenges ranging from performance optimization in distributed systems to designing intuitive user interfaces for enterprise products. Msft prioritizes engineers who can balance technical excellence with an understanding of customer needs and business impact.

This guide is scoped to a Software Engineer candidate at Msft.

Focus areas

Data Structures & Algorithms (DSA)
Coding Interviews (Algorithmic Coding)
Problem Solving Approach

The Msft interview process

Msft candidates report 2 rounds over 2-4 weeks. The stages below are what candidates describe, not a published process.

Online Assessment

An early assessment to evaluate your coding proficiency.

What to demonstrate

An early assessment to evaluate your coding proficiency
Depth in Data Structures & Algorithms (DSA)

How to prepare

Answer aloud and timed: Reverse a linked list in place.
Answer aloud and timed: Find the kth largest element in an unsorted array.

Interview Loop

A series of 3–5 back-to-back interviews assessing various skills.

What to demonstrate

A series of 3–5 back-to-back interviews assessing various skills
Depth in Data Structures & Algorithms (DSA)

How to prepare

Answer aloud and timed: Implement a queue using two stacks.
Answer aloud and timed: Solve the "Climbing Stairs" (Dynamic Programming) problem.

Where candidates lose points

Think out loud

This is the most critical advice. Interviewers at Msft evaluate your thought process as much as the final code.

Ask clarifying questions

Never start coding immediately. Clarify constraints and requirements first to ensure you are solving the right problem.

Know your resume

Be prepared to dive deep into every project you list. You should be able to justify every technical choice you made.

Study recent patterns

Review common Msft-tagged problems on coding platforms, as these frequently appear in interviews.

Questions & practice

Reverse a linked list in place.

Medium · Data Structures and Algorithms

Approach

Restate the input: its shape, its size, and what is guaranteed about it.
Name the brute-force solution and its complexity before improving on it.
Choose the data structure from the access pattern, not from familiarity.
State the target complexity and say which constraint rules the naive version out.

Follow-up

How does this change if the input no longer fits in memory?
What is the worst case, and how likely is it on real data?

Find the kth largest element in an unsorted array.

Medium · Data Structures and Algorithms

Approach

Restate the input: its shape, its size, and what is guaranteed about it.
Name the brute-force solution and its complexity before improving on it.
Choose the data structure from the access pattern, not from familiarity.
State the target complexity and say which constraint rules the naive version out.

Follow-up

How does this change if the input no longer fits in memory?
What is the worst case, and how likely is it on real data?

Implement a queue using two stacks.

Medium · Data Structures and Algorithms

Approach

Restate the input: its shape, its size, and what is guaranteed about it.
Name the brute-force solution and its complexity before improving on it.
Choose the data structure from the access pattern, not from familiarity.
State the target complexity and say which constraint rules the naive version out.

Follow-up

How does this change if the input no longer fits in memory?
What is the worst case, and how likely is it on real data?

Solve the "Climbing Stairs" (Dynamic Programming) problem.

Medium · Data Structures and Algorithms

Approach

Restate the input: its shape, its size, and what is guaranteed about it.
Name the brute-force solution and its complexity before improving on it.
Choose the data structure from the access pattern, not from familiarity.
State the target complexity and say which constraint rules the naive version out.

Follow-up

How does this change if the input no longer fits in memory?
What is the worst case, and how likely is it on real data?

Detect cycles in a graph using BFS or DFS.

Medium · Data Structures and Algorithms

Approach

Restate the input: its shape, its size, and what is guaranteed about it.
Name the brute-force solution and its complexity before improving on it.
Choose the data structure from the access pattern, not from familiarity.
State the target complexity and say which constraint rules the naive version out.

Follow-up

How does this change if the input no longer fits in memory?
What is the worst case, and how likely is it on real data?

Keep soft-deleted accounts from blocking re-registration

Easy · soft delete · unique constraints · engine differences

app_user holds user_id, tenant_id, email CITEXT, password_hash (NULL for SSO principals), email_verified_at, auth_version, status ('invited','active','suspended','deactivated'), created_at, updated_at, deleted_at. Two live accounts for one address inside a tenant must be impossible, but an address freed by a soft delete must be reusable, and the same tenant may delete and re-register it repeatedly. Write the uniqueness DDL for PostgreSQL 16, then the equivalent for MySQL 8 where partial indexes do not exist, and say what each permits once three deleted rows already hold that address.

Approach

Start from what is actually unique: not (tenant_id, email), but (tenant_id, email) among live rows. PostgreSQL says that directly — CREATE UNIQUE INDEX app_user_live_email ON app_user (tenant_id, email) WHERE deleted_at IS NULL. A full constraint over the same two columns burns the address permanently the first time someone deletes an account.
Keep case-insensitivity in the type or the index, never in the application: CITEXT as given, or UNIQUE (tenant_id, lower(email)) as an expression index where the extension is unavailable. A case-sensitive unique column is exactly how two accounts for one human appear.
For MySQL 8 the predicate has to move inside the key: add a discriminator column that is a constant 0 while the row is live and is set to user_id on delete, with UNIQUE (tenant_id, email, deleted_marker). Live rows share the constant and still collide; deleted rows differ from each other and stop colliding.
State the NULL variant and its dependency: leaving the marker NULL for deleted rows also works, because a unique index treats NULLs as distinct — true in MySQL, and true in PostgreSQL only under the default NULLS DISTINCT, which PostgreSQL 15 lets you reverse. Check the polarity against the three existing deleted rows: constant-on-live is what preserves the collision you want, and reversing it silently admits duplicate live accounts.

Follow-up

A deleted account re-registers with the same address the next day. Do the old resource rows follow the new user_id, and how does the API keep the two principals apart?
How do you honour an erasure request while resource_revision.actor_user_id still references this table?

Stop tag and share joins from fanning out a page

Medium · join fan-out · semi-joins · many-to-many

resource_tag is (resource_id, tag_id) with PK (resource_id, tag_id); resource_share is (resource_id, shared_with_user_id, permission). The tagged-and-shared listing inner-joins resource to both, filters tenant_id, tag_id = ANY($2) and shared_with_user_id = $3, orders by updated_at DESC and takes 50. Pages come back with fewer than 50 distinct resources and the total in the header is far too high. Explain the row multiplication, rewrite both the page query and the count query so each is correct, and name the index each one needs. PostgreSQL 16.

Approach

Do the arithmetic against the predicates that are actually there. An inner join emits one row per matching child row, and both joins are filtered: tag_id = ANY($2) admits only the requested tags, shared_with_user_id = $3 admits one user's share rows. So a resource holding three of the requested tags and shared with $3 once yields three rows, not one — the multiplier is its count of matching tags times its share rows for that single user, and that second factor is 1 unless the table admits duplicate (resource_id, shared_with_user_id) pairs. LIMIT 50 then limits rows rather than resources, and COUNT(*) counts pairs — the header is the product, not the population.
Reject DISTINCT as the fix. It deduplicates after the product has been built, so the planner must materialise and sort the fanned-out set before the LIMIT can apply, and it leaves any SUM or AVG in the same select list wrong.
Rewrite both filters as semi-joins, keeping resource as the only row source: AND EXISTS (SELECT 1 FROM resource_tag rt WHERE rt.resource_id = r.resource_id AND rt.tag_id = ANY($2)) and the same shape against resource_share. A semi-join stops at the first match per resource and preserves the driving index order, so ORDER BY updated_at DESC, resource_id DESC LIMIT 50 still stops after 50 rows.
Count with the same predicates and no join at all: SELECT count(*) FROM resource r WHERE r.tenant_id = $1 AND r.status = 'active' AND EXISTS (...) AND EXISTS (...). Nothing multiplies a resource, so the number is the population.

Follow-up

The filter changes from 'any of these tags' to 'all of these tags'. Rewrite it and state what it costs relative to the ANY form.
A resource can be shared with the same user twice under different permissions. Does your count change, and should it?

Design a scalable URL shortener like bit.ly.

Medium · System Design and Low-Level Design (LLD)

Approach

Fix the scope first: who calls this, how often, and what they do when it fails.
Name the read and write paths separately; they rarely have the same bottleneck.
Choose a partition key and say what query it makes expensive.
State the consistency you need, and where you are willing to be stale.

Follow-up

What breaks first when traffic grows ten times?
How does this behave when that dependency is down for an hour?

Design an LRU Cache with O(1) time complexity.

Medium · System Design and Low-Level Design (LLD)

Approach

Fix the scope first: who calls this, how often, and what they do when it fails.
Name the read and write paths separately; they rarely have the same bottleneck.
Choose a partition key and say what query it makes expensive.
State the consistency you need, and where you are willing to be stale.

Follow-up

What breaks first when traffic grows ten times?
How does this behave when that dependency is down for an hour?

Design a parking lot system (LLD).

Medium · System Design and Low-Level Design (LLD)

Approach

Fix the scope first: who calls this, how often, and what they do when it fails.
Name the read and write paths separately; they rarely have the same bottleneck.
Choose a partition key and say what query it makes expensive.
State the consistency you need, and where you are willing to be stale.

Follow-up

What breaks first when traffic grows ten times?
How does this behave when that dependency is down for an hour?

Design a rate limiter for an API.

Medium · System Design and Low-Level Design (LLD)

Approach

Say who the caller is and what they do when the call fails halfway.
Define the identity of a request so a retry cannot double-apply it.
Separate accepted, pending, failed and confirmed; they are different facts.
Design the error taxonomy before the success shape; callers branch on it.

Follow-up

What happens if the caller retries after a timeout?
How does a client discover it is on an old version of this contract?

Design a subscription service for managing family members.

Medium · System Design and Low-Level Design (LLD)

Approach

Fix the scope first: who calls this, how often, and what they do when it fails.
Name the read and write paths separately; they rarely have the same bottleneck.
Choose a partition key and say what query it makes expensive.
State the consistency you need, and where you are willing to be stale.

Follow-up

What breaks first when traffic grows ten times?
How does this behave when that dependency is down for an hour?

Edge instances grow 400 MB per hour until the nightly restart

Hard · memory leak · heap analysis · metric cardinality

Edge API instances start at 700 MB resident and grow about 400 MB/hour; a nightly rolling restart has hidden it for weeks. Growth continues unchanged when request rate halves overnight, p99 degrades in the last hours before an instance is recycled, and heap used immediately after a forced full GC rises monotonically. The service holds no product state. Name the discriminating measurement that separates the plausible causes, give the most likely cause, and give the fix and how you would verify it.

Approach

Separate resident memory from live heap first, because they fail differently. Resident size can grow from fragmentation, native buffers or thread stacks while the heap is flat; heap used after a full GC rising monotonically is the measurement that says objects are reachable and not being released. You already have it, so this is retention, not fragmentation, and that closes off half the candidate list.
Use the rate's independence from traffic as the discriminator. Growth that continues at half the request rate rules out per-request objects that are merely slow to collect and points at a structure that grows with distinct values observed rather than with call volume. Write the candidates that have that property: a metrics registry keyed on a high-cardinality label, an unevicted cache, an interner, a per-key lock map.
Take two heap snapshots an hour apart and diff by retained size, reading the dominator tree, not by allocation count or instance count. Expect one root holding a map with millions of entries, then follow the reference chain to the code that inserts and never removes. Allocation profilers point at churn, which is the wrong signal here.
The candidate that fits this service is an observability label carrying an identifier, such as a request path recorded before templating so that /v1/resources/48213 becomes its own metric series. That grows with distinct ids seen, is independent of rate, and explains the late p99 degradation, since GC cost rises with the size of the live set.

Follow-up

Post-GC heap is now flat but resident size still creeps. What are you looking at, and does it matter?
How would you have detected this before an OOM, given the nightly restart masked the trend?

Suggested practice checklist

Built from the rounds and topics Msft candidates report.

Day 1 — Map the Msft loop

Write out the reported sequence: Online Assessment, Interview Loop.
For each round, write one sentence on what it is judging, from the description above, and mark the one you are least ready for.

Deliverable: A one-page map of the 2 reported rounds, with the weakest marked.

Day 2 — Work Data Structures & Algorithms (DSA)

Spend the session on Data Structures & Algorithms (DSA), which Msft candidates report being tested on.
Write one worked example in Data Structures & Algorithms (DSA) and time yourself on it.

Deliverable: One timed worked example in Data Structures & Algorithms (DSA).

Day 3 — Work Coding Interviews (Algorithmic Coding)

Spend the session on Coding Interviews (Algorithmic Coding), which Msft candidates report being tested on.
Write one worked example in Coding Interviews (Algorithmic Coding) and time yourself on it.

Deliverable: One timed worked example in Coding Interviews (Algorithmic Coding).

Day 4 — Work Problem Solving Approach

Spend the session on Problem Solving Approach, which Msft candidates report being tested on.
Write one worked example in Problem Solving Approach and time yourself on it.

Deliverable: One timed worked example in Problem Solving Approach.

Day 5 — Answer out loud: Data Structures and Algorithms

Answer aloud, timed: Reverse a linked list in place.
Answer aloud, timed: Find the kth largest element in an unsorted array.

Deliverable: Spoken answers to 2 reported Data Structures and Algorithms question(s), under time.

Day 6 — Answer out loud: System Design and Low-Level Design (LLD)

Answer aloud, timed: Design a scalable URL shortener like bit.ly.
Answer aloud, timed: Design an LRU Cache with O(1) time complexity.

Deliverable: Spoken answers to 2 reported System Design and Low-Level Design (LLD) question(s), under time.

Day 7 — Answer out loud: Behavioral and Leadership

Answer aloud, timed: Tell me about the most difficult project you have ever worked on.
Answer aloud, timed: Describe a time you had a conflict with a colleague; how did you resolve it?

Deliverable: Spoken answers to 2 reported Behavioral and Leadership question(s), under time.