Advance Business Capital Software Engineer Interview Guide

The 60-second brief

The only company detail candidates report is a focus on the automotive and machinery sectors. Nothing reported names specific products, customers or internal teams. The Software Engineer role is described as covering both backend systems and user-facing applications. The reported day-to-day work is building and maintaining applications, gathering requirements with cross-functional teams, taking part in code reviews, and improving existing systems alongside new product work. Without more company detail, the stack named in the reported requirements is the best guide to what to revise: Java, Python or JavaScript, web frameworks, cloud, microservices, containers, and SQL and NoSQL.

This guide is for candidates interviewing for a Software Engineer position at Advance Business Capital. It covers the four reported rounds (Initial Screening, Technical Evaluation, Behavioral Evaluation, Final Discussions), the technical, coding, behavioral and case-study questions candidates report, and a seven-day plan. It does not cover pay, which candidates have not reported, or any question set for a specific team.

Candidates report four rounds over roughly three to five weeks: an Initial Screening, a Technical Evaluation, a Behavioral Evaluation and Final Discussions that may involve HR. The pace is said to vary with the team and the level.

Focus areas

• React
• Microservices
• JavaScript

Overview

The reported requirements point to a generalist engineer. Candidates list Java, Python or JavaScript, data structures and algorithms, web development frameworks and cloud technologies as must-haves. Agile practice, microservices, Docker and Kubernetes, and both SQL and NoSQL databases are listed as extras. The topics tagged on this loop are React, JavaScript, microservices, cloud technologies and front-end development. That mix means you should be able to talk about a browser-side component and the service behind it in the same conversation.

The reported questions fall into four groups. The technical and domain questions ask you to explain synchronous versus asynchronous programming, describe RESTful APIs and say how you would optimize a database query. The coding questions are three classics: reverse a linked list, implement binary search, and solve a dynamic programming problem such as knapsack. The case-study questions ask you to find the root cause of a performance problem and to design a scalable system for a high-traffic application. The behavioral questions cover a significant challenge, close teamwork and conflict resolution.

Candidates also mention that coding may happen on a whiteboard or on a coding platform, so practise both: typing in a plain editor, and writing legible code by hand with no autocomplete. The same reports list Agile methods, CI/CD and version control as things to be able to discuss, which fits the code-review work described for the role.

No public source describes the exact format of each round, so build breadth you can adapt. Get the three coding problems solid. Have crisp spoken answers to the REST, async and query questions. Have a repeatable method for diagnosing slowness and for sketching a service-based design. Prepare three behavioral stories you can reshape to fit a question.

The Advance Business Capital interview process

Candidates describe four stages: a first screen, technical and behavioral evaluations, and a closing conversation that may include HR. These stages come from candidate reports, not from a published process.

Initial Screening

Candidates describe the Initial Screening as a first check of basic qualifications and fit. Reports do not say who runs it or what format it takes, so prepare for a short conversation about your background and be ready to confirm logistics. Use it to show that your experience matches the stack the role lists: Java, Python or JavaScript, web frameworks and cloud technologies.

What to demonstrate

• Whether your background lines up with the listed requirements
• How clearly you summarise your own experience
• Basic fit for the role as candidates describe it

How to prepare

• Write a two-minute walk through your career. Highlight the languages and web frameworks you have shipped with, and one project that touched both backend and user-facing code.
• Prepare one sentence each on your experience with cloud platforms, microservices, containers and SQL or NoSQL databases, since all of them appear in the reported requirements.
• List your availability, any location constraints and your questions about the remaining rounds, so you can ask for the full sequence.

Technical Evaluation

Candidates report in-depth technical discussions together with coding assessments, and mention that code may be written on a whiteboard or on a coding platform. Practise with the question types gathered for this loop. On the discussion side: synchronous versus asynchronous programming, REST and query optimization. On the coding side: reversing a linked list, binary search and knapsack-style dynamic programming. The case-study questions on diagnosing slow systems and designing for high traffic belong in the same preparation. React and front-end development are among the tagged topics, so be ready in case a front-end question comes up too. Revise useEffect dependencies and cleanup, cancelling a fetch with AbortController, list keys, and lifting state up versus using context.

What to demonstrate

• Working code for standard data-structure and algorithm problems
• Command of web fundamentals such as asynchronous behavior, REST and database access
• Structured reasoning about performance problems and service design

How to prepare

• Write the linked-list reversal, binary search and 0/1 knapsack solutions from memory in your chosen language, once in an editor and once on paper. State the time and space complexity of each.
• Prepare a spoken explanation of async versus sync with a concrete example, such as a JavaScript promise chain or a React effect that fetches data. Prepare a one-minute REST explanation covering resources, verbs, status codes and idempotency.
• Practise a fixed order for query tuning: read the plan, check indexes, trim columns and rows, then consider the schema.
• Sketch one high-traffic design that splits the system into services, and rehearse the questions you would ask before drawing anything.

Behavioral Evaluation

Candidates report that the Behavioral Evaluation assesses soft skills and cultural fit through behavioral questions. The reported prompts cover a significant challenge at work, close work within a team, and your approach to conflict. Adaptability also appears under the reported behavioral topics, so keep a story ready about a change you did not expect. Prepare stories, not slogans. Each one should name a real decision you made, the people involved and an outcome you can describe.

What to demonstrate

• Collaboration and the role you played in team work
• How you handle disagreement
• How you respond to change and unexpected difficulty

How to prepare

• Write three stories: one for a challenge you overcame, one for close teamwork and one for a disagreement. Give each the situation, your action, the result and what you would change.
• Include a technical conflict, such as a design or code-review disagreement, since the role includes code reviews.
• Rehearse each story aloud until it fits in a couple of minutes, and keep a short version of each in reserve.

Final Discussions

Candidates describe Final Discussions as a closing conversation about overall fit and next steps, possibly with HR. Reports give no further detail on the format. Use it to tie your earlier answers together, raise open questions about the role, and be ready to talk about next steps, including anything you want to clarify about working arrangements.

What to demonstrate

• Overall fit across the earlier rounds
• Your interest in the role and the questions you raise about it
• Readiness to move to next steps

How to prepare

• Review your behavioral stories and your strongest technical answers so you can refer back to them in conversation.
• Write four or five questions about the team's work, how code review and Agile practice run day to day, and what the first projects would be.
• Decide in advance what you want to clarify about remote or flexible working, since reports advise raising it directly.

Where candidates lose points

Reversing a linked list while losing the rest of the chain, or returning the wrong node

Hold the next node in a variable before you repoint the current one, and return the previous pointer at the end. Test an empty list, a single node and two nodes before you say you are done.

Writing binary search with an off-by-one error or an endless loop

Decide the invariant first (closed range or half-open range), write the loop condition and midpoint update to match, and compute the midpoint without overflow. Test a missing target, the first and last elements, and an array with one element.

Giving a definition of REST or async programming with no example

Pair each definition with a concrete case: a resource URL with verbs, status codes and idempotency for REST, and a promise or async call that frees the caller while I/O completes. Be ready to say where each approach goes wrong.

Naming a technology as the first step when asked to find a performance problem or design for high traffic

Start with questions and measurement: what changed, which requests are slow, where the time goes. For design, state expected traffic, consistency needs and failure behavior first, then split reads from writes and services from storage.

Telling behavioral stories in the team's voice, so your own decision is invisible

Write each story with a line that begins with I for your decision and its result. Keep one technical conflict story ready, since conflict, teamwork and challenge are all reported prompts.

Questions & practice

What are the key differences between synchronous and asynchronous programming?

Medium · Technical / Domain Questions

Approach

1. Define both by what the caller's thread does. With a synchronous call the caller blocks until the result is back, so the thread sits idle during I/O. With an asynchronous call the operation starts and the caller carries on. The result arrives later through a callback, a promise or future, or await, so the thread is free while it waits.
2. Separate concurrency from parallelism. In JavaScript, one thread runs the event loop and the runtime (libuv in Node) handles I/O, so async code overlaps waiting but not computing. A CPU-heavy loop inside an async handler still blocks every other request until it ends. Move that work to worker threads or another process.
3. Give a concrete pitfall. Using await inside a for loop runs the requests one after another, while Promise.all starts them together. Promise.all rejects as soon as any one input rejects, and Promise.allSettled waits for every result. In Python asyncio, calling time.sleep or a blocking HTTP client inside a coroutine stalls the whole loop. Use asyncio.sleep, an async client or run_in_executor.
4. State the trade-off. Async gives much more throughput on I/O-bound work with many open connections, such as API servers and calls to databases or other services. The cost is harder error handling and stack traces, unhandled rejections, and async spreading through every caller. Synchronous code is simpler and fine for CPU-bound or low-concurrency work.

Follow-up

• A resolved promise callback and a setTimeout(0) callback are queued in the same tick. Which runs first, and why?
• How would you add a timeout and error handling to five concurrent API calls when one of them is allowed to fail?
• An async Node service gets slow under load even though the database is fast. What would you check first?

Write a function to reverse a linked list.

Medium · Coding / Algorithms

Approach

1. Write the iterative version with three pointers. Start with prev = null and curr = head. In the loop, save next = curr.next, set curr.next = prev, then advance prev = curr and curr = next. Return prev, which is the new head. It runs in O(n) time and O(1) extra space.
2. Explain why the order inside the loop matters. If curr.next is overwritten before it is saved, the rest of the list is lost. Returning curr instead of prev returns null.
3. Mention the recursive version: reverse the rest of the list, then set head.next.next = head and head.next = null. It is also O(n) time but uses O(n) call-stack space, so a very long list can overflow the stack. Python's default recursion limit is 1000 frames.
4. Test the edge cases out loud: an empty list, a single node, two nodes, and a longer list. Check that the old head now points to null, so no cycle was created, and walk the result to confirm the order.

Follow-up

• Reverse only the nodes between positions m and n, in one pass.
• Reverse the list in groups of k nodes, leaving any leftover tail as it is.
• Check whether a linked list is a palindrome using O(1) extra space.

How would you implement a binary search algorithm?

Medium · Coding / Algorithms

Approach

1. State the precondition: the array is sorted, or more generally the predicate is monotonic. Use lo = 0, hi = n - 1, and while lo <= hi compute mid = lo + (hi - lo) / 2. That form avoids int overflow in Java and C++. If the target is larger, set lo = mid + 1; if smaller, set hi = mid - 1; if equal, return mid. Return -1 when the loop ends. The cost is O(log n) time and O(1) space when iterative.
2. Name the loop invariant: if the target exists, it lies in [lo, hi]. Then show the half-open form, [lo, hi) with while lo < hi and hi = mid. Setting lo = mid while mid rounds down loops forever once the range has two elements, which is the most common bug.
3. Handle duplicates with a lower-bound variant that returns the first index where a[i] >= target. Python's bisect_left and C++ std::lower_bound do this. Java's Arrays.binarySearch returns -(insertion point) - 1 when the key is missing and does not say which duplicate it finds.
4. Check the edge cases: an empty array, one element, a target below the first or above the last element, and runs of equal values.
5. Point out that the technique also works on an answer space. Examples are the minimum capacity to ship packages within D days and an integer square root. With a monotonic feasibility check, the cost is O(n log range).

Follow-up

• Find a target in a sorted array that has been rotated at an unknown pivot.
• Return the first and last position of a target that appears many times.
• How would you compute the integer square root of a number using binary search?

Solve a problem involving dynamic programming, such as the knapsack problem.

Medium · Coding / Algorithms

Approach

1. Pin down the variant first. In 0/1 knapsack, each item has a weight w_i and a value v_i, can be used at most once, and the goal is the highest total value within capacity W. Brute force tries all 2^n subsets. Fractional knapsack is a different problem, solved greedily by value-to-weight ratio.
2. Define the state and recurrence. dp[i][c] is the best value using the first i items with capacity c. It equals max(dp[i-1][c], dp[i-1][c - w_i] + v_i), where the second option applies only when w_i <= c, and dp[0][c] = 0. Bottom-up this takes O(nW) time and space. Top-down memoization gives the same bound.
3. Reduce space to one array of size W + 1 and loop c from W down to w_i, so each item counts at most once. Looping c upward lets an item be reused, which solves unbounded knapsack instead.
4. Explain that O(nW) is pseudo-polynomial: it grows with the numeric value of W. When W is huge and values are small, run the DP over value instead (the minimum weight that reaches each value). When n is about 40, use meet-in-the-middle, about O(2^(n/2) * n).
5. To recover the chosen items, keep the 2D table and backtrack from dp[n][W]. Where dp[i][c] != dp[i-1][c], item i was taken, so subtract w_i from c. Test zero capacity, items heavier than W and zero-weight items.

Follow-up

• With the one-dimensional array, how would you still report which items were chosen?
• Change the problem to count the number of ways to make an amount from unlimited coins. What changes in the loop order?
• Capacity is 10^9 and there are 40 items. How do you solve it?

Describe how you would optimize a database query.

Medium · Technical / Domain Questions

Approach

1. Measure before changing anything. Find the slow statement with the slow-query log or pg_stat_statements, then run EXPLAIN (ANALYZE, BUFFERS) in Postgres or EXPLAIN ANALYZE in MySQL. Look for sequential scans on large tables, nested loops over many rows, sorts or hashes spilling to disk, and estimated row counts far from the actual ones (stale statistics, fixed by ANALYZE).
2. Index for the access pattern. Cover the WHERE, JOIN and ORDER BY columns. In a composite index, put equality columns first and the range column last, and remember the leftmost-prefix rule. A covering index (INCLUDE in Postgres) allows an index-only scan.
3. Know what stops an index from being used: a function on the column (WHERE LOWER(email) = ...) without a matching expression index, a leading-wildcard LIKE '%abc', and an implicit type cast. A single composite index cannot serve an OR across different columns. With a separate index on each column, though, Postgres can combine them with a BitmapOr and MySQL with an index-merge union, and rewriting the query as UNION ALL is another option. The planner may also skip an index when the filter matches a large share of the table.
4. Rewrite the query itself. Select only the columns you need instead of SELECT *, filter before joining or aggregating, and turn correlated subqueries into joins or EXISTS. Batch ORM lookups to remove N+1 query patterns, and replace deep OFFSET paging with keyset paging (WHERE id > last_seen ORDER BY id LIMIT n).
5. Name the costs and the bigger tools. Every index slows INSERT and UPDATE and takes storage. Beyond indexing come caching, materialized views, denormalization and partitioning of very large tables. Re-run EXPLAIN ANALYZE on production-sized data to confirm the gain is real and not just a warm cache.

Follow-up

• An index exists on the filtered column but the plan still shows a sequential scan. What are the possible reasons?
• How would you find and fix N+1 queries coming from an ORM?
• The table takes heavy write traffic. How do you decide whether a new index is worth it?

Replace offset paging on the resource feed with keyset

Easy · keyset pagination · composite index · sort order

resource holds resource_id, tenant_id, owner_user_id, title, body_ref, version, status ('draft','active','archived','deleted'), created_at, updated_at, deleted_at, with an index on (tenant_id, status, updated_at DESC, resource_id DESC). The listing endpoint returns active resources for one tenant, newest update first, 50 per page, today with LIMIT 50 OFFSET n. Tenants reach page 400 and rows are created while they read. Write the keyset query, define what the cursor carries and how it is encoded, and say which part of the index each predicate uses. Assume PostgreSQL 16.

Approach

1. Name the two failures separately. OFFSET 20000 makes the server produce and discard 20,000 rows, so page cost grows with depth rather than with page size. Independently, any write that changes how many rows sort above the offset moves the window between two fetches, and the direction decides which anomaly you get: an insert lands at the head of updated_at DESC and pushes already-returned rows down past the boundary, so they are returned a second time; a delete above the offset, or a row whose updated_at is bumped above the cursor, pulls rows up and one is never returned at all. Nothing in the response reveals either.
2. Write the seek: WHERE tenant_id = $1 AND status = 'active' AND (updated_at, resource_id) < ($2, $3) ORDER BY updated_at DESC, resource_id DESC LIMIT 50. The row-value comparison is one index range rather than a disjunction, and both columns are NOT NULL, which is what makes that comparison well defined.
3. Map each predicate onto the index: tenant_id and status are equality on the leading columns, (updated_at, resource_id) is the range, and the ORDER BY matches the index order so no Sort node appears and the scan stops after 50 rows. The DESC in the definition only matters for mixed directions — a plain ascending btree on the same columns is read backwards for this query.
4. Put both sort columns in the cursor and nothing the client can tamper with into another tenant: base64 of (updated_at, resource_id), validated server-side, with tenant_id taken from the principal.

Follow-up

• The client asks for 'jump to page 400'. What do you offer instead, and what does the honest version cost?
• Sort order becomes user-selectable across four columns. How many indexes is that, and which would you refuse to add?

Explain the concept of RESTful APIs and how they are used.

Medium · Technical / Domain Questions

Approach

1. Start with the model. Resources are nouns addressed by URIs (/customers/42/invoices), and HTTP methods act on them: GET reads, POST creates, PUT replaces, PATCH partially updates, DELETE removes. Say which methods are safe (GET, HEAD) and which are idempotent (GET, PUT, DELETE). POST is not idempotent, and PATCH is not guaranteed to be.
2. Name the constraints that matter in practice. Statelessness means every request carries its own auth, such as a bearer token, so any server can handle it. Responses can be cached with Cache-Control, and ETag plus If-None-Match returns a 304 when nothing changed. Interfaces stay uniform and layered.
3. Use status codes precisely: 200, 201 with a Location header, 204, 400 for malformed input, 401 when not authenticated versus 403 when authenticated but not allowed, 404, 409 for a conflict, 429 for rate limiting, and 5xx for server faults. Return a consistent JSON error body.
4. Cover the design decisions for a real endpoint. Paginate collections with a cursor or keyset rather than large offsets. Put filters and sorting in query parameters. Version with /v1/ in the path or with a header. Accept an Idempotency-Key header on POST so a client can retry after a timeout without creating a duplicate.
5. Contrast with the alternatives. GraphQL lets the client pick fields and avoids over-fetching and under-fetching. gRPC suits typed, low-latency calls between internal services. REST stays the default for public and browser-facing APIs because HTTP caching and tooling work with it as they are.

Follow-up

• A client's POST to create a record times out. How do you make the retry safe?
• How would you add a required field to a response without breaking existing clients?
• When would you pick gRPC or GraphQL over REST for a service?

Discuss how you would design a scalable system for a high-traffic application.

Medium · Problem-Solving / Case Studies

Approach

1. Start with requirements and rough numbers, and make the exercise concrete. A practice scenario that fits the reported automotive and machinery focus is a hypothetical portal where dealers submit applications, upload documents and track status. Ask about peak submissions, the read-to-write ratio (status checks far outnumber submissions), document volume and latency targets. Application and payment state must be strongly consistent, so keep it in a relational primary with transactions. Status pages can tolerate a few seconds of staleness.
2. Set the baseline. Run stateless application servers behind a load balancer and scale them horizontally, keeping session state in a shared store like Redis or in signed tokens. Serve static front-end assets from a CDN and put uploaded documents in object storage, not the database.
3. Scale the data layer one step at a time. Cache hot reads such as status lookups with a cache-aside Redis layer, TTLs and invalidation on write, and guard against stampedes. Never serve application or payment state from a stale cache. Add read replicas next, which brings replication lag and read-your-writes problems. Shard by a key only once one primary cannot take the writes, and say which queries the shard key makes expensive and how you would handle hot keys.
4. Move slow or bursty work behind a message queue (Kafka, RabbitMQ or SQS): document scanning, status notifications by email or SMS, and calls to outside systems. Because delivery is at least once, consumers must be idempotent, for example by keying each notification on application id and status. Apply backpressure so a spike fills the queue instead of overloading the database.
5. Cover resilience and the cost of splitting. Use timeouts, retries with exponential backoff and jitter, circuit breakers, rate limiting, deployment across several availability zones, and metrics, logs and traces. Argue when splitting a monolith into microservices (for example applications, documents and notifications) pays off, and when the extra network hops and operational load are not worth it.

Follow-up

• Traffic grows tenfold overnight. Which component fails first, and what do you change?
• A cached record is updated. How do you keep readers from seeing stale data, and how much staleness is acceptable?
• How does the system behave when a downstream dependency is down for an hour?

Given a scenario where a system is experiencing performance issues, how would you identify the root cause?

Medium · Problem-Solving / Case Studies

Approach

1. Define the symptom precisely before forming a theory. Ask which metric moved (p50 or p99 latency, throughput, error rate), on which endpoints, for all users or a subset, and since when. Line that up against deploys, config changes, traffic spikes and data growth.
2. Check every resource with the USE method (utilization, saturation, errors): CPU, memory and garbage collection, disk I/O, network, database connection pools, thread pools and queues. Check every service with RED (rate, errors, duration) to see where the slowdown starts.
3. Follow one slow request end to end with tracing or APM spans to see which hop dominates: a database query, a downstream call, a lock wait or time in a queue. Then go deeper with the right tool: EXPLAIN ANALYZE for the query, a CPU flame graph for hot code, GC logs for pauses, and pool metrics for exhaustion.
4. Test the hypothesis by changing one variable: roll back, toggle a feature flag, or reproduce in staging with production-like data. Confirm the metric recovers. A deploy may only expose a cause that was already there, such as a missing index that a new query path now hits.
5. Separate mitigation from the fix. While users are affected, roll back, scale out or rate-limit first. Then fix the root cause, add an alert or load test that would have caught it, and write a short postmortem.

Follow-up

• Latency creeps up over several days and a restart fixes it. What do you suspect, and how do you confirm it?
• Average latency looks normal but p99 has doubled. Where do you look?
• How do you tell whether the database or the application code is the bottleneck?

Suggested practice checklist

The plan follows the reported rounds: the stack and screening on day one, the three coding problems, then the technical and case-study questions, React and cloud revision, and behavioral stories on the last day.

Day 1 — Map the loop and the stack

• Write the four reported rounds in order, and next to each one note what you most need to show.
• List the languages and tools from the reported requirements and tagged topics (Java, Python or JavaScript, React, microservices, Docker and Kubernetes, SQL and NoSQL, cloud) and mark honestly where you are strong and weak.
• Pick the one language you will use for coding questions and set up an editor with no autocomplete.

Deliverable: A one-page loop map, a stack self-assessment and the chosen coding language.

Day 2 — Linked list and binary search

• Write the iterative linked-list reversal, then a recursive version, and state the complexity and stack cost of each.
• Write binary search with a clearly stated invariant, then a variant that returns the first position of a value.
• Test both on empty input, one element, duplicates and a missing target, then rewrite both on paper.

Deliverable: Two tested functions per problem, in the editor and on paper, with a written edge-case list.

Day 3 — Dynamic programming with knapsack

• Define the state, the transition and the base case for 0/1 knapsack, then write the two-dimensional table version.
• Reduce it to a one-dimensional array and explain why the capacity loop must run backwards.
• Trace a small instance by hand and explain how you would recover the chosen items.

Deliverable: A working knapsack in both forms, plus a hand-traced table and an explanation of the recovery step.

Day 4 — Async, REST and query tuning

• Write and say aloud a sync versus async explanation with a short code example in your language, including what happens to the thread or event loop.
• Design a small REST API for one resource with verbs, status codes, pagination and idempotency notes, and explain how REST differs from RPC.
• Take a slow query, read its plan, add an index, and rerun the plan to confirm the change.

Deliverable: A spoken async answer, a REST resource sheet and a before-and-after query plan with notes.

Day 5 — Slow systems and high-traffic design

• Write a numbered diagnosis checklist for a slow system: what changed, which requests, where the time goes, then fix and verify.
• Sketch a high-traffic design for a dealer application portal with a load balancer, stateless services, cache, relational database, object storage and queue, and mark the first component to saturate.
• Explain how you would split the system into microservices and what you would keep together.

Deliverable: A diagnosis checklist and an annotated architecture diagram with a ranked list of bottlenecks.

Day 6 — React, cloud and delivery revision

• Build a small React page that loads data from a REST endpoint with loading, error and empty states. Cancel the request with AbortController in the useEffect cleanup so a slow or out-of-order response cannot update an unmounted component.
• Revise React points out loud: what belongs in a useEffect dependency array, why list keys must be stable, when to lift state up versus use context, and when memo or useMemo actually helps.
• Revise cloud basics: a managed versus self-hosted database, object storage for documents, autoscaling and health checks, how a container image gets from CI to a cluster, and where secrets and configuration live.
• Explain one deploy pipeline you have used from commit to production, including version control, code review and CI/CD steps.

Deliverable: A working React component with cancellation, a half-page of React and cloud notes, and a spoken walk through one deploy pipeline.

Day 7 — Behavioral stories and final conversation

• Write three stories with situation, action, result and reflection: a significant challenge, close teamwork and a conflict. Add a fourth on adapting to an unexpected change.
• Rehearse each aloud and cut it to a version you can tell in a couple of minutes.
• Write five questions for the final conversation about the team, code review and the first projects.

Deliverable: Four rehearsed stories and a list of questions for the closing round.

Behavioral stories for the third round

Candidates report that the Behavioral Evaluation covers soft skills and cultural fit. The reported prompts concern a significant challenge, close teamwork and conflict, and adaptability is listed among the behavioral topics. For a Software Engineer, build stories around shipped work such as code reviews, cross-team requirements and production problems. The first three prompts below are reported. The last two are extra practice prompts, not reported questions.

1. Extra practice prompt (not reported): Tell me about a time a requirement changed late and how you adapted.
2. Extra practice prompt (not reported): Describe a code review that led to a disagreement and how it ended.

Tell me about a time when you faced a significant challenge at work. How did you handle it?

Medium · Behavioral / Leadership

Approach

1. Choose a challenge with a technical or delivery core that you personally owned, for example a performance regression, a migration under a deadline, or a service integration that kept failing. A story where the main difficulty was workload or other people's mistakes gives you little to show.
2. Spend two sentences on the situation, then most of the time on the options you weighed. Say which one you chose, why, and which constraint (deadline, legacy code, unclear requirements) forced the trade-off.
3. Describe the specific actions you took in order, using I for your own decisions, and mention how you kept teammates or stakeholders informed while you worked.
4. Give a result you can defend: a latency, error rate, ticket count or delivery date, with how you measured it before and after. If the outcome was only partial, say so and explain what you learned.
5. Close with what you would do differently. A story with no reflection sounds rehearsed.

Follow-up

• What would you do differently if you faced the same challenge again?
• How did you know your change, and not something else, produced the improvement?
• Who disagreed with your approach, and how did you handle that?

Describe a situation where you had to work closely with a team. What was your role?

Medium · Behavioral / Leadership

Approach

1. Pick a project where the team had to coordinate, such as splitting a feature between front end and back end, agreeing an API contract, or sharing a release. Say in the first sentence how many people were involved and what the shared goal was.
2. Define your role precisely: the part you owned, who depended on your output and what you depended on from others. State it in terms of work, such as the endpoint, component, review process or on-call rotation.
3. Show how the collaboration worked: how you agreed interfaces, divided tasks, reviewed each other's code and handled a blocked teammate. Name tools or practices you actually used, such as pull requests or stand-ups.
4. Use I for your own actions and name teammates' contributions separately. The common mistake is saying the team did everything, which leaves your role unclear.
5. End with the outcome and one thing the collaboration taught you that changed how you work.

Follow-up

• What part of the work was hardest to coordinate, and how did you fix it?
• What would the team have missed if you had not been there?
• How did you handle a teammate who was behind schedule?

How do you approach conflict resolution in a team setting?

Medium · Behavioral / Leadership

Approach

1. Answer in two parts: first state your general method in a few sentences, then back it with one real example. The question asks how you approach conflict, so a bare anecdote is not enough.
2. A reliable method is to listen until you can restate the other person's position fairly, find what each side is optimising for, and move the discussion from opinion to evidence, such as a benchmark, a small prototype or a written list of trade-offs.
3. Agree on who makes the call when evidence does not settle it, and commit to that decision afterwards, even if you argued for another option.
4. Choose an example with technical stakes, like a design disagreement, a code-review dispute or a conflict over an estimate, and describe the outcome, including what happened to the working relationship.
5. Avoid casting the other person as the problem. Make clear you stayed professional and kept the focus on the shared goal.

Follow-up

• What do you do when the other person will not move even after seeing the evidence?
• Tell me about a time you were the one who turned out to be wrong.
• How do you handle a disagreement with someone more senior than you?