Acculynx Software Engineer Interview Guide

The 60-second brief

Acculynx builds software for contractors and service businesses. The role description candidates see mentions project management tools and financial management systems among the products an engineer works on. The Software Engineer role covers designing, coding, testing and maintaining those applications, taking part in code reviews, fixing defects, and working with product management and design to settle requirements. The same description mentions system performance and scalability as areas the work touches.

This guide is for candidates applying for a Software Engineer position at Acculynx. It covers the five stages candidates report, the reported question families (language and tooling questions, behavioral prompts, debugging and design scenarios, and coding or database questions), and a seven-day plan that practises each family. It does not cover Data Scientist or Machine Learning Engineer loops.

Candidates report five stages over roughly four to six weeks: Initial Screening, Technical Assessments, Behavioral Interviews, Discussions with Team Members and Final Assessments. The last stage may add more technical or behavioral evaluation.

Focus areas

• Software Design Tradeoffs
• Robustness
• Performance Engineering

Overview

The role description lists the everyday work as designing, coding and testing applications, reviewing teammates' code, resolving defects and agreeing requirements with product management and design. The reported questions follow that shape. Alongside coding questions you will meet questions about how you keep code maintainable, how you test, how you track down a failure in production, and how you decide between cleaning up code and shipping a feature. Prepare for each with a real project of your own in mind, not a general definition.

Candidates report several topic labels for this loop: software design tradeoffs, robustness, performance engineering, bug reduction and defect prevention, and UI/UX design considerations. The practical consequence is that a good answer names a trade-off and a failure mode. When you describe a design or a fix, say what it costs, how it can fail, and how you would notice that it failed. A testing answer that mentions regression tests for a past bug is stronger than one that quotes a coverage target.

The reported questions fall into four families: language and tooling questions (languages you use, Git, unit testing, code quality), behavioral prompts (a difficult teammate, leading a project, competing deadlines, feedback, learning new technology), problem-solving scenarios (production debugging, underperforming software, requirements gathering, project lifecycle, technical debt), and coding or database questions (reversing a linked list, picking a search approach for a given dataset, tuning a slow query, walking through a recent coding challenge). Most are open-ended, so the quality of your examples decides how your answers land.

The role description names Java, Python and JavaScript, web frameworks such as React and Angular, SQL and NoSQL databases, and Agile or Scrum practice. Cloud platforms (AWS, Azure), CI/CD and mobile development are listed as extras. You do not need all of them, but be ready to say which you have used, how, and where your experience stops. The project-lifecycle and production-debugging questions are the natural place for deployment, pipelines and release practice to come up.

The Acculynx interview process

The five stages below are what candidates describe, in the order they report them. Format details, such as whether the coding exercise is live or a take-home, vary, so confirm them with the recruiter.

Initial Screening

Candidates report an opening screen that looks at your background and how it fits the role. The role description names Java, Python or JavaScript, SQL and NoSQL databases, web frameworks and Agile or Scrum practice, so expect your history to be read against that list. Treat it as a conversation: map your recent work onto that stack with specifics, and use it to find out how the later stages are run.

What to demonstrate

• Your background and fit for the role, as candidates describe the stage
• How your experience lines up with the languages, frameworks and database work the role lists
• How clearly you describe what you have built and your part in it

How to prepare

• Write a short account of your last two projects naming the language, framework and database in each, so the match with Java, Python, JavaScript and SQL is visible.
• Prepare a direct answer to the question about which languages you are proficient in and which you pick for which kind of task.
• Ask the recruiter whether the coding stage is a live exercise or a take-home, since candidate reports mention both.

Technical Assessments

Candidates report technical assessments covering programming skill and software engineering principles. The coding stage is described as a live exercise or a take-home assignment. The question families to practise for a stage like this are the coding and database questions (linked list reversal, a search over a given dataset, a slow query), the practice questions on unit testing and code quality, and the scenario questions on debugging and performance.

What to demonstrate

• Working code in a language you choose, with correct handling of edge cases
• Understanding of software engineering practice: testing, maintainability, version control
• How you structure a debugging or performance investigation

How to prepare

• Implement reversal of a singly linked list iteratively and recursively from memory in your strongest language, checking empty, one-node and two-node inputs.
• Practise explaining a slow query from its execution plan: scan type, index used, estimated against actual rows, and the single change you would make first.
• Prepare one real example of a unit test that caught a defect, to anchor the code-quality and testing answers.

Behavioral Interviews

Candidates report behavioral interviews built on situational questions about soft skills, teamwork and adaptability. The reported prompts include working with a difficult teammate, leading a project, prioritizing under several deadlines, acting on feedback and learning new technologies. Answer each with one specific episode where your own decisions are visible.

What to demonstrate

• Teamwork and communication shown through a specific episode
• Adaptability: how you respond to changed priorities or feedback
• Ownership: what you decided and did, separate from what the team did

How to prepare

• Build a bank of five stories, one for each reported prompt, and write each as situation, your actions, result.
• Check every story for the word "we" doing the work of "I", and rewrite until your own decisions are clear.
• Practise stating the result in one sentence with a concrete measure from your own project.

Discussions with Team Members

Candidates report conversations with team members that look at cultural fit and how well you would collaborate. The role description mentions code reviews, knowledge sharing and working with product management and design, so expect questions about how you work day to day. Bring concrete examples of giving and receiving review comments, and questions of your own about how the team ships.

What to demonstrate

• Collaboration potential, as candidates describe the stage
• How you handle code review, disagreement and shared ownership
• Whether your working style fits a team that uses Agile or Scrum practice

How to prepare

• Recall two code reviews, one where you gave difficult feedback and one where you received it, and what changed afterwards.
• Prepare your answer on leading a project and on prioritizing competing deadlines, including how you told stakeholders about trade-offs.
• Write questions for the team about how they review code, test changes and release, so the conversation runs both ways.

Final Assessments

Candidates report that the final assessments may add further technical or behavioral evaluation before a decision is made. Prepare for either: keep your coding and debugging answers current, and keep your story bank ready for a second pass. Review where earlier stages felt weakest and rehearse those answers again.

What to demonstrate

• Any additional technical evaluation that follows the earlier technical stage
• Any additional behavioral evaluation that follows the earlier conversations
• Consistency between what you said in earlier stages and now

How to prepare

• List the answers you felt least sure of in earlier stages and rehearse each aloud, with a concrete example attached.
• Re-run one coding problem and one production-debugging scenario to keep both fresh.
• Prepare a short explanation of why this role interests you, connected to software for contractors and service businesses.

Where candidates lose points

Answering the programming languages question with a list of keywords

Pick the language you know best, say what you built with it and what you would reach for it to do, then say where another language from the role's list (Java, Python or JavaScript) would be your choice and why. End with the limits of your experience.

Treating the production debugging question as a hunt for the fix

Give the sequence: what changed and when, what the logs and metrics show, reproducing the issue, narrowing it with a bisect or rollback, mitigating first, then root cause and a regression test. Say what you would communicate to users along the way.

Answering the slow query question with only "add an index"

Start with the execution plan: scan type, row estimates against actuals, join order. Then change one thing, such as a composite index matching the filter and sort, a rewritten predicate that can use the index, or fewer selected columns. Re-measure and check that writes are not hurt.

Reversing a linked list and losing the rest of the list

Save the next node before you repoint the current one. Track previous and current pointers, return previous at the end, and test an empty list, a single node and two nodes. State the O(n) time and O(1) space for the iterative form, and O(n) stack depth for the recursive one.

Telling behavioral stories with "we" throughout and no result

Name your own decision in each story, name teammates for theirs, and finish with a concrete result you can support. Rehearse the difficult teammate, leading a project and feedback stories aloud so the result lands in the last sentence.

Questions & practice

Write a function to reverse a linked list. Explain your approach.

Medium · Coding / Algorithms

Approach

1. Iterative three-pointer version: prev = null, curr = head. While curr is not null: next = curr.next; curr.next = prev; prev = curr; curr = next. Return prev as the new head. O(n) time, O(1) extra space.
2. Explain the invariant: at every step, prev is the head of the already-reversed part and curr is the head of the untouched part. Saving next before overwriting curr.next is what keeps the rest of the list reachable.
3. Recursive alternative: newHead = reverse(head.next); head.next.next = head; head.next = null; return newHead, with base case head == null or head.next == null. Still O(n) time, but O(n) call stack, so a long list can overflow the stack (Python's default recursion limit is 1000).
4. Edge cases to test aloud: empty list, a single node, two nodes, and a check that the old head's next is null afterwards so the result has no cycle.
5. Verify by tracing 1 -> 2 -> 3 on paper, writing down the pointers after each loop iteration.

Follow-up

• Reverse only the nodes between positions m and n, in one pass.
• Reverse the list in groups of k nodes.
• How would you reverse a doubly linked list?

Given a specific dataset, how would you implement a search algorithm?

Medium · Coding / Algorithms

Approach

1. Clarify the dataset and the query before choosing: size, whether it is sorted, static or frequently updated, in memory or on disk, and the query type: exact key, range, prefix, full text or nearest match.
2. Exact-key lookups on data that changes: a hash map, O(1) average lookup after an O(n) build, O(n) worst case under heavy collisions. Sorted static data: binary search in O(log n) with no extra memory.
3. Write binary search carefully: lo = 0, hi = n - 1, loop while lo <= hi, mid = lo + (hi - lo) / 2 (avoids int overflow in Java), and move lo = mid + 1 or hi = mid - 1. For duplicates or ranges, use the lower-bound variant that returns the first index whose value is >= target.
4. Other query types: for prefix search, a trie or binary search on a sorted array for the range of matches. For full-text search, an inverted index (a database full-text index or a search engine). For data on disk, a B-tree index in the database. For graphs or grids, BFS for shortest paths in unweighted graphs.
5. Edge cases: empty input, target not found, all elements equal, target below the first or above the last element, and off-by-one errors in the loop condition.

Follow-up

• Return the first and last positions of a target in a sorted array with duplicates.
• Search a sorted array that has been rotated at an unknown pivot.
• How would you support searches that tolerate typos?

Can you walk through a coding challenge you recently completed?

Medium · Coding / Algorithms

Approach

1. Choose a problem you can rebuild from memory and write again live. Recent and medium difficulty beats impressive and half-remembered.
2. Walk through it in order: the problem and its constraints (input size, value ranges), the brute-force solution and its complexity, then the insight that improved it, such as a hash map for lookups, two pointers on sorted data, a heap for top-k, or memoisation for overlapping subproblems.
3. State the final time and space complexity and explain why the constraints made the brute force unacceptable (for example, n = 10^5 rules out O(n^2)).
4. List the edge cases you tested and any bug you hit along the way. A real off-by-one or overflow you caught is more convincing than a flawless story.
5. End with what you would change now: cleaner naming, a different data structure, or how the solution would behave if the input grew or arrived as a stream.

Follow-up

• Rewrite the core loop of that solution now.
• What would change if the input did not fit in memory or arrived as a stream?
• Where is the bottleneck in your final solution?

What algorithms are you most comfortable with, and can you explain their applications?

Medium · Coding / Algorithms

Approach

1. Name a few algorithms you can actually write and give each one's deciding properties and a use. Merge sort: O(n log n) worst case, stable, O(n) extra memory. Quicksort: O(n log n) average, O(n^2) worst case, in place, not stable. Insertion sort: O(n) on nearly sorted input, which is why hybrids such as Timsort use it.
2. Connect them to libraries: Python's sorted() and Java's Arrays.sort for objects use Timsort (stable), while Java's Arrays.sort for primitives uses a dual-pivot quicksort.
3. Graphs: BFS gives shortest paths in unweighted graphs in O(V + E). Dijkstra with a binary heap runs in O((V + E) log V) and needs non-negative edge weights. Topological sort (Kahn's algorithm) orders tasks with dependencies, such as build steps or job scheduling.
4. Searching and hashing: binary search in O(log n) on sorted data, hash tables with O(1) average lookups. Dynamic programming, for example edit distance in O(m * n), for problems with overlapping subproblems.
5. For each one, be ready to say why it fits the application (stability, memory, worst-case guarantee, input shape) and to implement it on request.

Follow-up

• When does Dijkstra give wrong answers, and what would you use instead?
• Why would a library choose a stable sort for objects?
• Implement one of the algorithms you named, now.

How would you optimize a database query that is running slowly?

Medium · Coding / Algorithms

Approach

1. Reproduce with the exact query and parameters, then read the execution plan (EXPLAIN ANALYZE in PostgreSQL, EXPLAIN in MySQL, the actual execution plan in SQL Server). Look for full scans of large tables, estimated row counts far from actual, nested loops over large inputs, and sorts or hashes spilling to disk.
2. Fix access paths. Add a composite index that matches the filter and join columns, with equality columns before range columns in a B-tree, or a covering index that also holds the selected columns. Remember that every index slows writes and takes storage.
3. Make predicates sargable. A function on an indexed column (WHERE LOWER(email) = ...), a leading wildcard (LIKE '%abc') or an implicit type cast can stop the index being used. Rewrite the predicate or add an expression index.
4. Cut the work done: select only the needed columns, replace large OFFSET pagination with keyset pagination, fix N+1 query patterns from the ORM, and refresh stale statistics (ANALYZE) if the estimates are wrong.
5. Re-run and compare plan and timing before and after. If the query is still too slow, consider a materialized view, a summary table or a cache, and state the staleness that brings.

Follow-up

• The index exists but the planner ignores it. Why might that be?
• The query is fast in development and slow in production. What differs?
• The table is write-heavy. How does that change your indexing choices?

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

1. 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.
2. 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.
3. 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.
4. 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 which do you prefer for specific tasks?

Medium · Technical / Domain Questions

Approach

1. Lead with one or two languages you can defend in depth, not a long list. The requirements candidates describe for this role name Java, Python or JavaScript, so if you know any of them, start there and give a project where you shipped production code in it.
2. Match languages to tasks and give the property behind each choice. Java suits long-lived typed backend services: static types, mature JVM tooling, real thread concurrency. Python suits scripts, data jobs and quick services. JavaScript or TypeScript suits browser UI, for example with React or Angular.
3. Show depth with one specific detail per language. In Java, objects that are equal must return the same hashCode or HashMap lookups break. In standard CPython, the GIL stops threads running Python bytecode in parallel, so CPU-bound work goes to multiprocessing. JavaScript runs one event loop, so long synchronous work blocks the UI.
4. Name a trade-off you have actually hit, such as dynamic typing speeding up a prototype but letting type errors through until runtime, and say what you did about it (type hints with mypy, or TypeScript).
5. Be honest about level: separate the languages you would write live code in from the ones you have only read or maintained.

Follow-up

• Why would you pick Java over Python for a service, or the other way round? Answer for a concrete workload.
• What is a bug or pitfall specific to your main language, and how have you guarded against it?
• How does memory management or garbage collection work in the language you prefer?

Can you describe a challenging technical problem you faced and how you resolved it?

Medium · Technical / Domain Questions

Approach

1. Pick a problem with a real technical root cause that you diagnosed yourself, such as a race condition, a memory leak, a slow query or a data corruption bug. Avoid a story that is mostly about a deadline.
2. Structure it as symptom, constraints, hypotheses, fix, result. Name the symptom and its impact, then each hypothesis you tested and the evidence that ruled it out. This part shows how you reason.
3. Explain the fix and at least one alternative you rejected, with the reason (risk, cost, time to ship, or a fix that only hid the symptom).
4. Close with a measurable result (error rate, latency, incidents per month) and what you did to stop it coming back: a regression test, an alert, a lint rule or a design change.
5. Keep the background to a couple of sentences and give the technical detail room. Someone should be able to follow the mechanism of the bug from your account.

Follow-up

• How did you confirm that your fix addressed the cause and not just the symptom?
• What would you do differently if you met the same problem again?
• Which hypothesis did you chase that turned out wrong, and what did it cost?

How do you ensure code quality and maintainability in your projects?

Medium · Technical / Domain Questions

Approach

1. Answer in layers, starting with what is automated: formatters and linters, static typing where the language allows it, and unit plus integration tests running in CI as a required check before merge.
2. Code review: small, single-purpose pull requests, a clear description of the reason for the change, and review focused on correctness, naming, error handling and test coverage of the changed behaviour, with style left to the formatter.
3. Design for change: small functions and modules with one responsibility, dependencies passed in rather than created inside (so they can be tested), and clear boundaries between the UI, business logic and data access.
4. Know the trade-offs. A coverage percentage used as a gate can be gamed with assertion-free tests. Too much abstraction hurts maintainability as much as duplication. Measure outcomes such as defects found after release or the share of changes that cause incidents.
5. For legacy code without tests, write characterization tests that pin current behaviour before refactoring, and improve the area you are already changing rather than rewriting it.

Follow-up

• What do you look for first when you review someone else's pull request?
• How would you bring a large untested module under control without stopping feature work?
• When is duplicating code the better choice than abstracting it?

Explain the importance of unit testing and how you implement it in your work.

Medium · Technical / Domain Questions

Approach

1. Why it matters: fast feedback, a safety net for refactoring, regressions caught before release, and tests that document the expected behaviour of a unit.
2. How I write them: Arrange-Act-Assert, test behaviour through the public interface rather than private details, one reason to fail per test, and descriptive names such as returns_zero_total_for_empty_invoice.
3. Keep them deterministic. Inject the clock, seed or inject randomness, and replace network and database calls with test doubles. Know the difference between a stub (returns canned data) and a mock (verifies an interaction), and avoid over-mocking, which ties tests to the implementation.
4. Cover the edges: empty and null input, boundaries (zero, one, maximum), invalid input and error paths, plus a regression test for every bug fixed. Name the tools for your language: JUnit 5 for Java, pytest for Python, Jest for JavaScript.
5. Place unit tests in the test pyramid: many fast unit tests, fewer integration tests against a real database or API, and a small number of end-to-end tests. Mention red-green-refactor if you practise TDD.

Follow-up

• How do you unit test code that depends on the current time or on a database?
• Is 100% line coverage worth pursuing? What does coverage fail to tell you?
• When would you write an integration test instead of a unit test?

Suppose you need to design an application feature that involves multiple stakeholders. How would you gather re

Medium · Problem-Solving / Case Studies

Suppose you need to design an application feature that involves multiple stakeholders. How would you gather requirements?

Approach

1. List the stakeholders and what each owns: who uses the feature, who pays for it, who supports it, and who has the final say on scope. Settle the decision owner before collecting wishes.
2. Interview stakeholders separately about the problem rather than the solution: what they do today, the workaround they use, how often it happens and how they would measure success. Watching a real user do the task beats a meeting.
3. Turn the findings into user stories with testable acceptance criteria (Given/When/Then), and capture the non-functional requirements as well: permissions and roles, audit trail, performance, and offline or mobile use if relevant.
4. Make conflicts explicit. When two groups want incompatible things, set out the options with their cost and risk and take them to the decision owner instead of quietly building a compromise.
5. Validate early with a wireframe or clickable prototype, agree a minimal first slice, and plan to check real usage after release against the success measure everyone agreed on.

Follow-up

• Two senior stakeholders want contradictory behaviour from the feature. What do you do?
• Requirements change halfway through the sprint. How do you handle it?
• How would you know after launch that you gathered the right requirements?

If a piece of software you developed is underperforming, what steps would you take to identify the issue?

Medium · Problem-Solving / Case Studies

Approach

1. Define underperforming with a number: p50, p95 or p99 latency, throughput, memory or CPU. Compare it with a baseline, and find out when it started and whether it affects everyone or only some users or data sizes.
2. Measure before changing anything. Use APM traces to see where request time goes (database, external calls, application CPU, garbage collection), then a profiler or flame graph for CPU-bound paths, and browser dev tools for front-end slowness.
3. Check the usual causes: N+1 queries from an ORM, missing indexes, unbounded result sets, synchronous remote calls inside a loop, memory leaks that cause GC pressure, and on the front end large bundles or unnecessary re-renders.
4. Fix the largest contributor first. By Amdahl's law, speeding up a part that takes fraction p of the time by factor s gives an overall speedup of 1 / ((1 - p) + p / s), so a small part never pays off much.
5. Re-measure under the same load to confirm the gain, then add a guard: a performance test in CI or an alert on the latency percentile.

Follow-up

• The median is fine but p99 latency is bad. What would you suspect?
• It is slow only for a few large accounts. How does that change the investigation?
• When would you add a cache, and what problems does a cache bring?

Describe your process for approaching a new software project from inception to deployment.

Medium · Problem-Solving / Case Studies

Approach

1. Discovery: the problem, the users, the success metric and the constraints (deadline, budget, compliance, existing systems). Write them down and get agreement before designing.
2. Design: a short design document covering architecture, data model, API contracts, the alternatives considered and the risks, reviewed by peers. Run a spike or prototype on the riskiest unknown before committing.
3. Plan and build in vertical slices that each deliver something usable, worked in sprints if the team runs Agile or Scrum (the role requirements candidates describe mention both). Code review, automated tests and CI on every change.
4. Deploy safely: CI/CD to a staging environment, feature flags, an incremental rollout, dashboards and alerts ready before launch, and a tested rollback path. For schema changes, use expand-and-contract migrations so old and new code both work during the rollout.
5. After launch: measure against the success metric, collect feedback, record follow-up work and technical debt, and hand over runbooks so the system can be supported.

Follow-up

• How do you roll back a release that included a database migration?
• What goes into your design document, and what do you leave out?
• How do you decide between building a component and buying or adopting one?

How do you balance technical debt with new feature development?

Medium · Problem-Solving / Case Studies

Approach

1. Make debt visible and priced. Track debt items with their cost in terms the product side understands: hours lost per change, incidents caused, slower onboarding, or features blocked.
2. Prioritise by interest, not size. Debt in code that changes often or sits on a critical path costs more than ugly code nobody touches. Security and data-integrity debt jumps the queue.
3. Pay it down alongside features: refactor the area a feature already touches, and include the cleanup in that feature's estimate rather than asking for a separate rewrite.
4. Treat deliberate debt as a decision: taking a shortcut to hit a date is fine if it is documented with a trigger for paying it back. Accidental debt that nobody records is the dangerous kind.
5. Show the payoff afterwards with a before/after metric, such as build time, defect rate in that module, or time to ship the next change there, so the next request for capacity is easier to justify.

Follow-up

• Your product manager refuses to schedule any refactoring. How do you make the case?
• Tell me about debt you took on deliberately. Was it the right call?
• How do you know a refactor actually paid off?

How would you approach debugging a complex issue in a production environment?

Medium · Problem-Solving / Case Studies

Approach

1. Stabilise first. Assess the impact (who is affected and how badly), and if the issue lines up with a recent deploy or config change, roll back or turn off the feature flag before looking for the root cause.
2. Build a timeline from the signals: error rates and latency metrics, logs filtered by a correlation or request ID, and traces. Then ask what changed around the start time: deploys, config, traffic pattern, data volume or a dependency.
3. Form hypotheses and test the cheapest one that rules out the most. Narrow the search by bisecting: by commit range (git bisect), by host or region, by customer, or by feature flag.
4. Reproduce outside production with production-like data or the failing request replayed. If it cannot be reproduced, add targeted logging or metrics around the suspect path and ship that, rather than guessing at a fix.
5. Fix with a regression test, confirm in production that the metric recovers, then write a blameless postmortem covering the cause, why monitoring did or did not catch it, and the follow-up actions.

Follow-up

• The bug only happens under production load and you cannot reproduce it locally. What next?
• How would you tell a race condition from a data-specific bug?
• How do you debug an issue that involves customer data you are not allowed to copy out of production?

Suggested practice checklist

The plan follows the five reported stages and the four question families: tooling and languages, coding and database questions, quality and debugging scenarios, and a behavioral story bank, ending in a full run-through.

Day 1 — Map the loop and your stack

• Write out the five reported stages in order, and beside each one note what candidates say it covers and which of your answers belongs there.
• List the languages, frameworks and databases on your CV against the role's list (Java, Python, JavaScript, React or Angular, SQL, NoSQL, Agile or Scrum) and mark gaps honestly.
• Write your answer to the question about which languages you know and which you pick for which task.

Deliverable: A one-page loop map, a stack-gap list, and a written answer on languages and preferences.

Day 2 — Linked lists, search and algorithms

• Implement linked list reversal iteratively and recursively and run it on empty, one-node and two-node lists.
• For the search question, write which approach you would use for a sorted array, for an unsorted list queried many times, and for records looked up by key (binary search, hash index, scan), with the cost of each.
• Choose three algorithms you know well and explain each one's application and complexity aloud.

Deliverable: Working reversal code in both forms, a search-approach table with complexities, and a spoken three-algorithm answer.

Day 3 — Slow queries and performance

• Take a slow query on a local database, read its execution plan, change one thing and compare the plans before and after.
• Write a performance triage checklist for underperforming software: measure, profile, find the bottleneck, change one thing, re-measure.

Deliverable: Before-and-after plan notes for the query, a solved practice SQL exercise, and a one-page performance triage checklist.

Day 4 — Code quality, testing and technical debt

• Write unit tests for your reversal function: normal, empty, single-node and repeated-value cases, and say what each guards against.
• Write your answer on code quality and maintainability using one real project: reviews, linting, naming, small changes, CI.
• Write your answer on balancing technical debt against new features, with a concrete example of a cleanup you justified.

Deliverable: A unit test file for the reversal function and two written answers, one on quality and one on technical debt.

Day 5 — Debugging, requirements and project lifecycle

• Write a production debugging runbook: what changed, logs and metrics, reproduction, bisect or rollback, mitigation, root cause, regression test.
• Outline how you would gather requirements for a feature with several stakeholders: who to interview, what to ask, how to resolve conflicts.
• Outline a project from inception to deployment, including your Agile or Scrum routines and the CI/CD steps you have used.

Deliverable: A debugging runbook, a stakeholder interview checklist, and a project lifecycle outline.

Day 6 — Behavioral story bank

• Write five stories for the difficult teammate, leading a project, competing deadlines, feedback and learning a technology prompts, in situation, action, result form.
• Write your Git experience answer, a challenging technical problem story and a walk-through of a recent coding challenge.
• Record yourself answering two of them and mark every "we" that should be "I".

Deliverable: Five written stories, three further written answers, and a recording with corrections noted.

Day 7 — Full-loop rehearsal

• Run a back-to-back mock in reported order: background summary, one coding problem, one debugging scenario, then two behavioral answers.
• Write questions for team members about how they review code, test and release.
• Re-read your weakest answers from the week and rehearse each again aloud.

Deliverable: Mock notes marking weak spots, a list of questions for the team, and one re-recorded weak answer.

Stories to prepare for the Acculynx behavioral stages

Candidates report behavioral interviews built on situational questions about teamwork, adaptability and soft skills, followed by discussions with team members. For a Software Engineer, that means stories about review disagreements, competing deadlines and learning new tools, told with your own decisions visible. Prepare five specific episodes and reuse them across prompts.

Describe your experience with version control systems such as Git.

Medium · Technical / Domain Questions

Approach

1. Answer with a workflow you actually used: feature branches and pull requests, or short-lived branches merged often, who reviews, and how conflicts get resolved.
2. Show command-level fluency: rebase against merge, interactive rebase to tidy local commits, cherry-pick for a hotfix, stash, and bisect to find the commit that introduced a bug.
3. Include one recovery story: undoing a bad commit that was already pushed with a revert instead of rewriting shared history, or recovering a lost commit from the reflog.
4. State your rule on shared branches: no force-push to them, and use force-with-lease only on your own branch after a rebase.
5. Close with how Git ties into review and CI: small commits, clear messages, and branches that trigger tests before merge.

Follow-up

• When would you rebase and when would you merge, and what does each do to history?
• You reset a branch with --hard and lost commits. How do you get them back?
• A bad change is already on the main branch. How do you remove its effect without rewriting history?

Tell me about a time when you had to collaborate with a difficult team member. How did you handle it?

Medium · Behavioral / Leadership

Approach

1. Pick a conflict with a real working cost, such as repeated review standoffs or missed handoffs, and describe the behavior and its effect on the work, not the person's character.
2. Show the first step you took: a private conversation to understand their constraints, before escalating or complaining to others.
3. Describe the working agreement you reached, for example review turnaround, an interface contract, or how disagreements get settled, and what you changed on your side.
4. End with the outcome and what stayed true afterwards: the delivery that shipped, the relationship that continued, and what you would repeat.
5. The mistake that sinks this answer is a story where you were right and they were the problem. Name your own contribution to the friction.

Follow-up

• What would you have done if the conversation had not changed anything?
• At what point do you bring in a manager, and what do you say?
• What did you learn about your own working style from this?

Describe a project where you took the lead. What were the outcomes?

Medium · Behavioral / Leadership

Approach

1. Choose a project where you owned decisions, even without a title: scope, breakdown, a technical choice, a deadline call.
2. Cover how you organised the work: splitting tasks, who took what, how you tracked progress, and how you kept reviews from becoming a bottleneck.
3. Describe one hard moment, such as a risk, a blocked teammate or a scope change, and the decision you made with the information you had.
4. State outcomes with evidence from the project: what shipped, what defects or incidents followed, a performance or time figure you can back up.
5. Use "I" for your decisions and name teammates for theirs. A story told only as "we" gives the listener nothing to assess.

Follow-up

• What would you change if you ran that project again?
• How did you handle a teammate who fell behind?
• Which decision were you least sure about, and how did it turn out?

How do you prioritize tasks when faced with multiple deadlines?

Medium · Behavioral / Leadership

Approach

1. Give a method, then an example: list everything, estimate effort, rank by impact and by the cost of missing each date, and note dependencies between items.
2. Say that you talk to the people affected. Name who you told (product manager, designer, teammate), what you offered (reduce scope, move a date, pair on it) and how you confirmed agreement.
3. Use one concrete collision, such as a feature due the same week as a production defect or a review backlog, and show the order you chose and why.
4. Cover what you protect when time is short: tests on risky paths, defect fixes that affect users, and communication about what slipped.
5. Close by saying how the outcome was checked afterwards, for example whether both dates held and what you adjusted next time.

Follow-up

• Two stakeholders both say their request is the top priority. What do you do?
• A production bug lands in the middle of a feature. How does the order change?
• Tell me about a time you got the priority wrong.

Can you provide an example of receiving constructive feedback and how you acted on it?

Medium · Behavioral / Leadership

Approach

1. Pick feedback that was specific and a bit uncomfortable, such as review comments about unclear naming, thin tests or oversized pull requests, rather than a disguised strength.
2. Describe your first reaction honestly, then the step you took to understand it: asking for an example, restating it, or checking it against the code.
3. Explain what you changed in your habits: a test checklist before opening a pull request, smaller changes, a design note for larger work.
4. Show evidence that the change held, such as fewer review round trips or a later compliment on the same area.
5. If you disagreed with part of the feedback, say how you handled it respectfully and what you took from it anyway.

Follow-up

• Tell me about feedback you disagreed with and what you did.
• How do you give hard feedback to a teammate in a code review?
• What is the most recent feedback you received?

How do you approach learning new technologies or methodologies?

Medium · Behavioral / Leadership

Approach

1. Describe a repeatable method with a real case: scope a small goal, read the official documentation, build a throwaway spike, then apply it to real work with a reviewer.
2. Use a technology from this role's world, such as a web framework, a database type or a cloud service, and say what you built to test your understanding.
3. Explain how you decide a technology is worth adopting: fit with the problem, maintenance cost, team familiarity, and what you would give up.
4. Include how you share it: a short write-up, a demo or pairing, so the knowledge does not stay with one person.
5. Avoid answering with only "I read blogs." Show learning that ended in shipped, reviewed work.

Follow-up

• Tell me about a time a new technology turned out to be the wrong choice.
• How do you stay productive while you are still learning something unfamiliar?
• How did you bring teammates up to speed on what you learned?