The Software Engineer role as candidates describe it is general application development: build and maintain web applications, review teammates' code, and fix problems in production. The stack named is Java, Python or JavaScript, React or Spring Boot on the framework side, Git and CI/CD for delivery, and a database underneath with sensible data modeling. That points your preparation at fundamentals you can defend with a project, rather than at one narrow specialty.
The reported questions fall into clear groups. Two are coding problems: reverse a linked list, and implement binary search while explaining your reasoning. Three are design questions: a scalable web application, microservices versus a monolith, and database design for a new application. Two are case studies, one about a project with unclear requirements and one about diagnosing an underperforming system. Three are conversational technical prompts about the languages you know, object-oriented versus functional programming, and a hard problem you solved. Three are behavioral: a difficult team member, motivation and accountability, and prioritizing around deadlines.
Candidate-written example lists add more areas to cover: RESTful API design, how you keep code quality up, designing a user authentication service, load balancing, debugging a complex issue, learning a new technology quickly, working with non-technical stakeholders, and handling feedback. These come from online communities and actual questions vary by team, so use them as practice material and not as a prediction. The seven-day plan gives the coding problems and design questions their own days, then closes with behavioral stories and a full mock run.
HR Screening
reportedCandidates describe this stage as an initial screening of your background and fit for the role. Reports attach no technical questions to it, so treat it as the place where your CV has to hold together: which languages you actually use, what you built with them, and why this role. The language-proficiency question covers the same ground and is worth rehearsing for any early conversation. If a take-home or live coding exercise is part of the process, as some reports say, this is the stage to ask about it.
What to demonstrate
- Your background and fit for the role, as the stage is described
- How clearly you can summarise your own projects and the languages you have used
How to prepare
- Write a short summary for each CV project covering the problem, your part, the stack and the outcome, and say it aloud until it is brief and accurate.
- Compare your experience with the stack candidates describe (Java, Python or JavaScript, React or Spring Boot, Git, CI/CD, databases) and decide how to talk about any gap honestly.
- Prepare your reason for wanting this role and two or three questions about the team and the stack.
- Ask what the later rounds look like and whether coding is live or a take-home.
Technical Interviews
reportedCandidates report several technical interviews covering technical skills, problem-solving and system design. The reported questions of those types are two coding problems (reverse a linked list, binary search), design questions on a scalable web application, microservices versus a monolith and database design, case studies on unclear requirements and an underperforming system, and conversational prompts on languages, object-oriented versus functional programming and a hard problem you solved. Coding is reported as live exercises or take-home assignments. Prepare each type separately: working code with tests for the coding, a repeatable outline for the design questions, and a stated sequence of steps for the diagnosis case.
What to demonstrate
- Technical skills in the languages, frameworks and tools you claim
- Problem-solving on coding and case-study questions
- System design: structuring an application and explaining the trade-offs behind each choice
- Working code, which any live coding or take-home format tests directly
How to prepare
- Write iterative and recursive linked-list reversal and an inclusive-bounds binary search in your strongest language, and test empty, single-element and not-found inputs.
- Rehearse one design outline that you can reuse: requirements, API, data model, stateless application tier, cache, load balancer, queue, monitoring and failure handling.
- Prepare a microservices-versus-monolith answer that states what would justify a split and what a split costs.
- Write a diagnosis sequence for an underperforming system: reproduce, measure, find the bottleneck, change one thing, confirm the improvement.
Team Discussions
reportedCandidates describe this stage as interviews with team members about cultural fit and collaboration. The behavioral questions reported in this guide (a difficult team member, motivation and accountability, prioritizing multiple deadlines) are the natural material to rehearse, along with the example prompts about working with non-technical stakeholders and handling feedback. Come with stories in which your own decisions are visible and with questions of your own about how the team works.
What to demonstrate
- Cultural fit and collaboration skills, as the stage is described
- Communication with colleagues and stakeholders, which conversational formats rely on
How to prepare
- Write STAR outlines for the three reported behavioral prompts, using "I" for what you decided and did.
- Prepare one story about working with product managers or designers, since the role description includes turning business requirements into technical specifications.
- Prepare a feedback story: what you were told, what you changed, and what happened afterward.
- Write questions for the team about code review, how production issues are handled, and how releases move through CI/CD.
PracHub editorial advice for the preparation topics above.
Reversing the linked list but losing the rest of it, because the next pointer is overwritten before it is saved
Store the next node first, then repoint the current one, then advance. Test an empty list, a single node and two nodes, state O(n) time and O(1) space, and be ready to write the recursive version too.
Writing binary search with an off-by-one error or a loop that never ends
Choose one invariant (inclusive low and high bounds, for example), compute the midpoint as low + (high - low) / 2, and always move a bound past the midpoint. Test one element, a missing target and duplicates, and say aloud that the input must be sorted.
Answering the scalable web application or microservices question with a list of buzzwords, or treating microservices as the default
Start from requirements and a simple deployable, then say what would trigger a split (independent deploys, separate scaling needs, team boundaries) and what it costs: network calls, distributed data and extra monitoring.
Listing Java, Python and JavaScript for the language question and then being unable to go deep on any of them
Pick the one or two you know best, tie each to a project with a decision you made, and prepare to show object-oriented versus functional style in that language with a short code example.
Telling behavioral stories that describe only what the team did, so your own decision and result are missing
Use STAR with most of the time on your action, name the result you can truthfully state, and add what you would change. Keep four or five stories that can be reused across the difficult-colleague, accountability and prioritization prompts.
Choose a category, try a prompt, then open its approach, worked solution or follow-up when you need it.
Write a function to reverse a linked list.
Write a function to reverse a linked list.
Approach
- Confirm the node shape (value and next) and that you return the new head. Iterative solution with three pointers: prev = null, curr = head. In a loop, save next = curr.next, set curr.next = prev, then advance prev = curr and curr = next. Return prev.
- Complexity: O(n) time and O(1) extra space, because each node's pointer is rewired exactly once.
- Mention the recursive version: reverse the rest, then set head.next.next = head and head.next = null. It is O(n) time but also O(n) call-stack space, and Python's default recursion limit is about 1000, so the iterative version is safer on long lists.
- Edge cases: an empty list, a single node, two nodes. Check that the old head's next becomes null, otherwise you create a cycle. Test by walking the result and comparing it with the reversed input values.
Follow-up
- Reverse only the nodes between positions m and n in one pass.
- Reverse the list in groups of k nodes, leaving any final group shorter than k unchanged.
- How does the solution change for a doubly linked list?
How would you implement a binary search algorithm? Explain your reasoning.
How would you implement a binary search algorithm? Explain your reasoning.
Approach
- State the precondition: the array is sorted, or more generally there is a predicate that is false and then true across the range. Each step halves the search space, so it runs in O(log n) time and O(1) space when iterative.
- Write it with an explicit invariant. Closed interval: lo = 0, hi = n - 1, while lo <= hi, mid = lo + (hi - lo) / 2. If a[mid] equals the target, return mid. If it is smaller, set lo = mid + 1, otherwise hi = mid - 1. Return -1 if not found. Computing mid as lo + (hi - lo) / 2 avoids int overflow in Java or C++; Python ints do not overflow.
- Give the lower-bound variant for duplicates: half-open [lo, hi) with hi = n and while lo < hi. If a[mid] < target, set lo = mid + 1, else hi = mid. lo ends at the first index whose value is >= target. If you mix interval styles, for example setting lo = mid where mid rounds down, the loop can run forever.
- Know the library calls. Python's bisect_left and bisect_right return insertion points. Java's Arrays.binarySearch returns -(insertion point) - 1 when the key is missing and makes no promise about which index it returns among duplicates.
- Edge cases to test aloud: empty array, one element, target below the minimum or above the maximum, and all elements equal.
Follow-up
- Find the first and last positions of a target that appears several times.
- Search for a target in a sorted array that has been rotated at an unknown pivot.
- Use binary search on the answer, for example to find the smallest ship capacity that delivers all packages within D days.
Canonicalise a request body into a stable idempotency fingerprint
idempotency_key.request_fingerprint is a SHA-256 over the method, path and canonicalised body, and a retry whose fingerprint differs must be rejected with 422 rather than served the stored response. Write the canonicaliser. Bodies are JSON up to 256 KB nested at most 32 levels; clients vary key order, whitespace and unicode escaping, and some send 64-bit ids as JSON numbers. Produce a deterministic byte string such that semantically identical bodies match and any semantic difference does not. State your complexity and name two normalisations you refuse to perform.
Approach
- Parse once into a tree, then re-serialise under fixed rules: object keys sorted, array order preserved, one escaping convention, no insignificant whitespace. Parsing is O(n) and sorting keys is O(k log k) per object, so O(n log n) overall with O(depth) stack, and the 32-level cap is enforced during parsing because hostile nesting is how a canonicaliser becomes a stack overflow.
- Sort keys by their UTF-8 bytes and say why the obvious implementation is wrong in some runtimes: a default string comparison that orders by UTF-16 code units places surrogate pairs, meaning code points from U+10000 up, below U+E000 to U+FFFF, which is not UTF-8 byte order, so two services written in different languages disagree on the same document.
- Do not re-encode numbers through a double. IEEE-754 binary64 represents integers exactly only up to 2^53, so normalising a 19-digit id through a float changes it, and 1 against 1.0 cannot be reconciled without deciding whether they are the same value. Preserve the literal token, and require ids as strings at the API boundary if you want them comparable.
- Reject duplicate keys rather than picking one. JSON permits them and parsers disagree, most keeping the last, so any choice you make ties the fingerprint to a parser detail that the code handling the request does not necessarily share.
Follow-up
- A client sends the same logical request with an extra field your API ignores. Same key, different fingerprint, so you return 422. Is that the right answer?
- Where does the fingerprint get computed relative to request decompression and the body-size limit?
Find version gaps and relay lag with window functions
outbox_event holds event_id, aggregate_type, aggregate_id, aggregate_version, event_type, payload, status ('pending','published','dead'), attempts, created_at, published_at. A projection is missing rows and you must decide whether the relay skipped events or the consumer dropped them. Write three queries over the last seven days: one listing every aggregate_id whose published aggregate_version sequence has a hole, one giving per-day counts with a running total, and one returning the newest published event per aggregate. For each, say where the window function is evaluated relative to WHERE and LIMIT. PostgreSQL 16.
Approach
- Gaps: compute lead(aggregate_version) OVER (PARTITION BY aggregate_id ORDER BY aggregate_version) in a subquery, then filter next_version <> aggregate_version + 1 in the outer query. Window functions are evaluated after WHERE, GROUP BY and HAVING and before the outer ORDER BY and LIMIT, so the predicate cannot sit in the same WHERE clause and PostgreSQL 16 has no QUALIFY.
- Say what the seven-day filter does to the answer: it truncates every partition, so the first row per aggregate has no predecessor inside the window and a hole spanning the boundary is invisible. Widen the window, or join to resource.version as the authority for the true maximum.
- Running total: SELECT date_trunc('day', created_at) AS d, count() AS n, sum(count()) OVER (ORDER BY date_trunc('day', created_at) ROWS UNBOUNDED PRECEDING). An aggregate inside a window call is legal because grouping runs before windowing. The grouping key is unique per row here so ROWS and RANGE agree, but write the frame anyway — over ungrouped rows with tied timestamps the default RANGE frame pulls in every peer row and the total jumps.
- Newest per aggregate: DISTINCT ON (aggregate_id) ... ORDER BY aggregate_id, aggregate_version DESC is the cheap PostgreSQL-only form when an index matches that order; row_number() OVER (PARTITION BY aggregate_id ORDER BY aggregate_version DESC) = 1 is the portable form and needs a subquery for the same evaluation-order reason as the gap query.
Follow-up
- Relay failover redelivers events. Does a duplicate break the gap query, and how would you detect one from this table alone?
- Turn the gap check into a continuous monitor rather than a query someone runs after an incident. What does it watch?
Keep soft-deleted accounts from blocking re-registration
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?
What programming languages are you proficient in, and how have you applied them in past projects?
What programming languages are you proficient in, and how have you applied them in past projects?
Approach
- Lead with one or two languages you can defend in depth, not a list of ten. If they overlap with the languages listed for this role (Java, Python, JavaScript), start with those, and rate the others honestly as working knowledge.
- For each language, give one project: what you built, why that language fit it (ecosystem, team, runtime), and one measurable result, such as a latency, a throughput figure or a defect count.
- Show runtime depth with one concrete trade-off you hit. In standard CPython the GIL lets only one thread run Python bytecode at a time, so CPU-bound work goes to multiprocessing while threads or asyncio suit I/O. On the JVM, GC pauses can drive tail latency. In Node, synchronous CPU work blocks the event loop.
- Name the tooling you used with it (build tool, test framework, linter, profiler), because that shows production use rather than tutorial use.
Follow-up
- What is one limitation of your main language that you worked around in production, and how?
- How would you get productive in a language or framework you have not used before?
- Why did you pick that language for that project over the obvious alternative?
Can you explain the differences between object-oriented and functional programming?
Can you explain the differences between object-oriented and functional programming?
Approach
- Define both precisely. OOP models a program as objects that bundle state with the methods that act on it, and relies on encapsulation and polymorphism through subtype dispatch. FP builds programs from pure functions over immutable data, with functions as first-class values (map, filter, reduce, higher-order functions).
- Give the core trade-off (the expression problem). With classes it is easy to add a new type, since you add one subclass, and hard to add a new operation across all types. With algebraic data types and pattern matching, the opposite holds.
- Tie it to engineering outcomes. Pure functions are deterministic, so they are easy to unit test and to memoise. Immutable data can be shared across threads without locks. Mutable objects need synchronisation or careful ownership.
- Show that real code mixes the two: Java streams and records, Python comprehensions and frozen dataclasses, JavaScript array methods with React function components. Say when you reach for each, for example an FP-style pipeline for data transforms and objects for long-lived stateful components.
Follow-up
- What makes a function pure? Is a function that writes a log line pure?
- In a functional style, where do side effects such as database writes go?
- When does inheritance cause problems that composition avoids?
Describe a challenging technical problem you encountered and how you resolved it.
Describe a challenging technical problem you encountered and how you resolved it.
Approach
- Pick a problem with a concrete symptom and a cause that was not obvious at first: an intermittent failure, a memory leak, a race condition or a performance cliff. Avoid problems that were only hard because of a deadline.
- Tell it as an investigation: the symptom and its impact, the hypotheses you formed, the evidence that ruled each in or out (logs, metrics, a minimal reproduction, a profiler or debugger), and the root cause.
- Use first person singular for your own actions and name the technical fix exactly, for example 'I added a unique constraint and made the consumer idempotent', not 'we fixed the bug'.
- Close with a measured result and the prevention step you added, such as a regression test, an alert or a design change, so the story shows you closed the loop.
Follow-up
- Which hypothesis did you rule out first, and what evidence ruled it out?
- What would have caught this earlier: a test, a review or monitoring?
- Looking back, what would you do differently?
How would you design a scalable web application? What factors would you consider?
How would you design a scalable web application? What factors would you consider?
Approach
- Start with requirements and numbers: expected users and requests per second, read-to-write ratio, data size and growth, latency target, and which features must stay up when others degrade.
- Keep the app tier stateless so it scales horizontally behind a load balancer. Sessions go in a shared store such as Redis or in signed tokens. Static assets go to a CDN.
- Add caching deliberately, for example cache-aside with TTLs on hot reads. Say how entries are invalidated on writes, and how you avoid a stampede when a hot key expires (request coalescing or jittered TTLs).
- Scale the database in steps: indexes and query fixes first, then read replicas (accepting replication lag, so read-your-writes paths go to the primary), then partitioning or sharding by a key that spreads load. Say which queries the shard key makes cross-shard.
- Move slow work off the request path through a message queue with idempotent consumers. Add rate limiting, autoscaling and metrics on p95/p99 latency and error rate so you can see the next bottleneck.
Follow-up
- Traffic grows tenfold. Which component saturates first, and how would you know?
- A user updates their profile and then sees the old value. What caused it, and how do you fix it?
- How would you handle a sudden spike, for example a launch, without overprovisioning all year?
Explain the concept of microservices and when you would choose this architecture over monolithic design.
Explain the concept of microservices and when you would choose this architecture over monolithic design.
Approach
- Define microservices as small services that are deployed independently, each owning its data store, built around a business capability and communicating over APIs (HTTP/gRPC) or events. Contrast this with a monolith: one deployable unit, usually with one shared database.
- Make the case for them: teams can deploy independently, components with very different load can scale separately, and failures can be isolated. Each of these benefits needs clear domain boundaries to exist.
- Name the costs. Every call can now fail partially, so you need timeouts, retries with backoff and circuit breakers. Cross-service consistency needs sagas or a transactional outbox instead of one ACID transaction. Debugging needs distributed tracing and central logging, and CI/CD and ops overhead rise.
- Give a decision rule. For a small team or a domain that is still changing, prefer a modular monolith with clean internal boundaries. Extract a service when a boundary is stable and has a real scaling or ownership reason, and do it incrementally with the strangler-fig pattern.
Follow-up
- How would you keep an order and its payment consistent across two services without a distributed transaction?
- A downstream service becomes slow. How do you stop the slowdown from spreading to its callers?
- How would you decide where to make the first cut in an existing monolith?
How do you approach database design for a new application?
How do you approach database design for a new application?
Approach
- Start from the domain and the access patterns: the entities and their relationships, the queries that must be fast, read and write volume, and consistency needs. That decides relational versus document or key-value storage. Strong relationships and transactions usually point to a relational database.
- Normalise to around third normal form to avoid update anomalies, then denormalise only for measured read hot spots. Say how the copy stays in sync, for example through the same transaction, a trigger or an async job.
- Make the schema enforce correctness: primary keys (surrogate or natural, with the reasoning), foreign keys, UNIQUE, NOT NULL and CHECK constraints. Many-to-many relationships use a junction table with a composite key. Store timestamps in UTC.
- Design indexes from the queries. A composite index serves filters on its leftmost columns, so order the columns by how queries filter and sort. Every index adds write cost and storage.
- Plan for change: versioned migrations, and backward-compatible expand-then-contract steps (add the column, backfill, switch reads, drop the old column) so deploys do not need downtime.
Follow-up
- How would you model users, roles and permissions, and which queries would you index for?
- When would you choose a document store over a relational database for part of this application?
- How do you add a required column to a large table that is serving live traffic?
How would you approach a project that has unclear requirements?
How would you approach a project that has unclear requirements?
Approach
- Clarify the outcome before the features: who the users are, what problem they have, and how success will be measured. Find the person who can make decisions on scope.
- Write down your assumptions and open questions and get them confirmed in writing, for example in a short design document or ticket, so ambiguity becomes explicit decisions.
- Cut scope to a thin end-to-end slice with clear acceptance criteria. Ship it, or show a prototype or mock, early so feedback replaces guessing.
- Build so the uncertain parts are cheap to change: keep them behind interfaces or feature flags, avoid one-way-door decisions such as public APIs and data schemas until they are clearer, and keep a decision log.
- Back the method with a real example: what was unclear, which question you asked that changed the plan, and what got delivered.
Follow-up
- Two stakeholders give you conflicting requirements. What do you do?
- How do you decide you know enough to start building?
- Describe a time a requirement changed late. How did your design absorb it?
If a system you developed is underperforming, what steps would you take to diagnose and fix the issue?
If a system you developed is underperforming, what steps would you take to diagnose and fix the issue?
Approach
- Define 'underperforming' as a metric first: p50/p95/p99 latency, throughput, error rate or resource cost. Find out when it started and correlate that with deploys, config changes, traffic shifts and data growth.
- Localise the problem. For services, check rate, errors and duration (RED) per endpoint and use distributed tracing to find the slow span. For hosts, check utilisation, saturation and errors (USE) on CPU, memory, disk and network.
- Check the usual database causes: the slow query log and EXPLAIN ANALYZE for sequential scans or missing indexes, N+1 query patterns, lock contention, and connection-pool exhaustion.
- Profile the application if the database is clean: CPU flame graphs, GC pauses, memory growth, blocking calls on hot paths, and slow or missing caching on downstream calls.
- Change one thing at a time, compare against a baseline under the same load, roll out by canary or feature flag, then add an alert or a performance regression test so the problem cannot return unnoticed.
Follow-up
- Median latency is fine but p99 is bad. What are the likely causes?
- How would you detect an N+1 query problem from logs or traces?
- How do you show the fix worked and the improvement was not just lower traffic?
One log partition stops advancing while the others drain
Search results for a subset of tenants are hours stale; the rest are current. The projection consumer reports lag of zero on 15 of 16 partitions and 400,000 on one. Its error rate is flat and its CPU is idle. outbox_event has no pending rows older than a second, so the relay has published everything it holds. Identify the mechanism, give the ordered checks, and state what you do in the first ten minutes versus what you change permanently.
Approach
- Read the lag distribution first. A slow consumer lags everywhere; zero on fifteen partitions and 400,000 on one is not throughput. Idle CPU on the stuck partition means the consumer is not advancing its offset at all, which points at one message it cannot get past rather than at a rate problem.
- Exonerate the producer before touching the consumer. No pending outbox rows older than a second means the relay published, so the event exists in the log. This separates never sent from sent and never applied, which are different code paths and usually different owners.
- Read the message at the stuck offset and the handler's log lines for its event_id. A flat error rate with no progress has two explanations and you must distinguish them: the handler is throwing and the retry loop is swallowing it, or the handler is blocking on something and never returning. Idle CPU with no error lines favours the second.
- Mitigate before diagnosing further. Move the offending event to a dead-letter store and commit the offset past it. Adding consumers does nothing here, because a partition is consumed by exactly one member of the group, and the blast radius is every aggregate hashed to that partition, not only the aggregate that produced the bad event.
Follow-up
- The dead-lettered event carried aggregate_version 7 and the projection had applied 6. What must the replay do differently if 8 and 9 landed in the meantime?
- How do you show staleness to the user while the partition is behind, given the API already returns the projection's watermark?
The plan follows the loop: Day 1 prepares the screening and maps your stack, Days 2 and 3 cover coding and the language questions, Days 4 to 6 cover system design, database design and the case studies, and Day 7 covers Team Discussions stories and a full mock run. Several days also point to PracHub practice questions on idempotency, SQL and debugging.
Prepare, practise & reflect
One practical outcome each day. Spend longer where you need it.
0 / 7 done01Screening and stack map
- Write a short summary of each CV project: problem, your part, stack and outcome.
- Make a table of Java, Python, JavaScript, React, Spring Boot, Git, CI/CD and databases, and mark which ones you can support with a specific project.
- Answer the language-proficiency question aloud and tighten it to a version that names projects and decisions.
Deliverable: A project summary sheet, a stack-to-project table and a spoken answer to the language question.
Practice prompt ↗02Linked list and binary search
- Write iterative linked-list reversal, then the recursive version, in your strongest language and test empty, one-node and two-node lists.
- Write binary search with inclusive bounds, then the first-occurrence variant for duplicates.
- Explain each solution aloud, including time and space complexity and the sorted-input precondition.
Deliverable: Two tested functions (reverse list, binary search) plus a variant, and a spoken explanation of the reasoning for each.
Practice prompt ↗Practice prompt ↗03Paradigms and a hard problem
- Solve one small problem, such as computing order totals, in an object-oriented style and again in a functional style, and list the trade-offs in state, testing and extension.
- Outline your challenging technical problem story: the symptom, how you diagnosed it, the fix and the result.
- Work the PracHub practice question on canonicalising a request body into an idempotency fingerprint.
Deliverable: Two implementations of one problem with a trade-off list, a story outline, and a worked practice solution.
Practice prompt ↗Practice prompt ↗Practice prompt ↗04Scalable web app and microservices
- Design a scalable web application with the reusable outline: requirements, API, data model, stateless app tier, cache, load balancer, queue, monitoring and failure handling.
- Write the criteria you would use to choose microservices over a monolith and the costs of splitting.
- Sketch the components of a user authentication service and say where load balancing fits.
Deliverable: One design diagram, a split-or-stay decision list, and an authentication service component sketch.
Practice prompt ↗Practice prompt ↗05Database design and SQL practice
- Design a schema for a new application of your choice: entities, keys, indexes, constraints and how you would run a migration.
- Work the PracHub practice question on keeping soft-deleted accounts from blocking re-registration.
- Work the PracHub practice question on finding version gaps and relay lag with window functions.
Deliverable: A schema with indexes and constraints, plus working solutions to both SQL practice questions.
Practice prompt ↗Practice prompt ↗Practice prompt ↗06Case studies and diagnosis
- For the unclear-requirements question, write the clarifying questions you would ask and the smallest slice you would deliver first.
- Write a diagnosis runbook for an underperforming system: reproduce, check metrics, profile, inspect database queries, change one thing, verify.
- Work the PracHub practice question on a log partition that stops advancing while the others drain.
Deliverable: A clarifying-question list, a diagnosis runbook and debugging notes for the practice question.
Practice prompt ↗Practice prompt ↗Practice prompt ↗07Team Discussions stories and full mock
- Write STAR outlines for the difficult-team-member, motivation and accountability, and deadline-prioritization prompts, using "I".
- Prepare one stakeholder-collaboration story and one feedback story.
- Run a full mock aloud: one coding problem, one design question and the three behavioral prompts.
- Write your questions for the team about code review, production issues and releases.
Deliverable: Three STAR outlines, two extra stories, a recorded mock run and a list of team questions.
Practice prompt ↗Practice prompt ↗Practice prompt ↗Expand any day for tasks and deliverables. Your progress is saved on this device.
The Team Discussions round is described as interviews with team members about cultural fit and collaboration. The behavioral prompts candidates report cover a difficult colleague, motivation and accountability, and prioritizing around deadlines, and the example lists add non-technical stakeholders, feedback and learning a technology quickly. Build four or five stories with a visible decision and a stated result, and reuse them across prompts.
Describe a time when you had to work collaboratively with a difficult team member. How did you handle the situ
Describe a time when you had to work collaboratively with a difficult team member. How did you handle the situation?
Approach
- Choose a real friction with a specific cause, such as disagreement over a design, missed handoffs or slow reviews, and describe the behavior and its effect on the work in neutral terms rather than as a character judgement.
- Spend most of the answer on your actions: you spoke to the person directly first, asked about their constraints, and agreed on something concrete such as a written interface contract, a review turnaround, or a shared checklist.
- Say when and why you involved a manager or another colleague, and make clear that it came after a direct attempt to resolve it.
- End with a result you can state plainly (the feature shipped, review cycles shortened, the working relationship improved) and one thing you changed in your own behavior.
- The mistake that sinks this answer is a story where the other person was simply wrong, or where nothing was resolved. Show that you took part in the fix.
Follow-up
- What would you have done if the person had not changed their behavior?
- At what point did you involve your manager, and what did you ask them to do?
- What did the situation teach you about your own working style?
What motivates you in your work, and how do you keep yourself accountable?
What motivates you in your work, and how do you keep yourself accountable?
Approach
- Answer both halves. For motivation, anchor it in specific work you have done, such as tracing a slow query to its cause or shipping a feature people used, and tie it honestly to the work in this role: building applications, reviewing code and fixing production issues.
- For accountability, describe mechanisms and not character: commitments written in tickets, work split into small deliverables with visible status, early warning when a date is slipping, tests and CI as a check on yourself, and retrospective notes after something goes wrong.
- Include one short story where a commitment was at risk, you raised it early, and you can say what happened as a result.
- Avoid generic lines such as "I am passionate about technology" or "I work hard". Neither names a mechanism or an example, so neither can be checked.
Follow-up
- Tell me about a time you missed a commitment. What did you do about it?
- What do you do when motivation drops during routine or repetitive work?
- How does your approach to accountability change when others depend on your part of a project?
How do you prioritize tasks when faced with multiple deadlines?
How do you prioritize tasks when faced with multiple deadlines?
Approach
- State a method with named criteria before the story: list every task with its deadline, size and the cost of slipping, then rank by impact, urgency and whether others are blocked on it.
- Tell a concrete case in which two or three deadlines collided. Say what you did first, what you cut, reduced or delayed, and which colleague (a product manager, designer or teammate) you told.
- Show negotiation and not only effort: you raised the conflict early, offered options such as reduced scope or a moved date, and let the owner of the priority choose.
- Describe the outcome and how you adjusted afterward, for example by estimating with more buffer or checking priorities on a regular rhythm.
- The weak versions are "I work longer hours" and "I do whatever the loudest person asks". Neither shows a decision.
Follow-up
- What do you do when two stakeholders both insist their request is the top priority?
- How do you handle a production issue that interrupts work you had already planned?
- Which task did you decide not to do in that story, and how did you tell the person affected?
- 01
Describe a time when you had to work collaboratively with a difficult team member. How did you handle the situation?
- 02
What motivates you in your work, and how do you keep yourself accountable?
- 03
How do you prioritize tasks when faced with multiple deadlines?
- 04
Describe a project where you had to collaborate closely with non-technical stakeholders.
- 05
How do you handle feedback on your work?
- 06
Describe a time when you had to learn a new technology quickly to solve a problem.
How many rounds are there and how long does the process take?
Candidates report three rounds: HR Screening, Technical Interviews and Team Discussions, over about 3-5 weeks. Another set of reports gives 2-4 weeks, so plan for the longer range and ask the recruiter for the actual schedule.
Beelix Software Engineer candidate reports ↗Is the coding live or a take-home?
Reports say coding is done through live exercises or take-home assignments. Ask which applies to you. For live coding, practise writing and testing in a plain editor. For a take-home, include tests and a short README on your approach and trade-offs.
PracHub Software Engineer practice ↗Which languages and tools should I prepare?
The requirements candidates describe name Java, Python and JavaScript, React and Spring Boot, Git and CI/CD, and databases with data modeling. AWS or Azure, DevOps tools and agile methods appear as pluses. Choose one language for coding and be ready to give project examples for the rest of your stack.
PracHub Software Engineer practice ↗What system design topics should I cover?
The reported questions are a scalable web application, microservices versus a monolith and database design for a new application. Example lists add RESTful API design, a user authentication service and load balancing. Prepare one reusable outline and practise it on each of those prompts.
PracHub Software Engineer practice ↗What experience level does the role assume?
The requirements candidates describe mention roughly 2-5 years of software development experience. Choose projects that show cross-functional work, production troubleshooting and code review, and be ready to discuss decisions you made yourself.
PracHub Software Engineer practice ↗Is the role remote or in person?
Reports mention a hybrid work model. Confirm the arrangement for your team and location with the recruiter during the HR Screening.
PracHub Software Engineer practice ↗How should I structure behavioral answers?
Use STAR: situation and task in a couple of sentences, then the actions you took, the result and what you would change. Prepare four or five stories that fit the difficult-colleague, accountability, prioritization, stakeholder and feedback prompts.
PracHub Software Engineer practice ↗Sources & methodology 3 sources ↗
No official company page is cited. Rounds and questions come from candidate reports and PracHub editorial material; each source shows the date it was read.
- 01Beelix Software Engineer candidate reports ↗
Company-reported rounds, questions and FAQ.
Candidate reports · Accessed 2026-09-22 - 02PracHub Software Engineer practice ↗
PracHub practice material, not company-reported.
PracHub page · Accessed 2026-09-22 - 03PracHub preparation framework ↗
PracHub preparation guidance.
PracHub page · Accessed 2026-09-22
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