Interview guides · Aras

Aras Software Engineer Interview Guide

Aras Software Engineer interview guide: the three reported rounds, linked-list and binary search coding, Java GC, REST, debugging and behavioral prep.

The 60-second brief

Aras makes product lifecycle management (PLM) software. Candidates describe a Software Engineer there as someone who designs, builds and maintains features of that PLM product. They work alongside product managers, UX designers and other engineers, review code and track down software issues when they come up. The requirements candidates describe name Java, C# or Python as the main languages, with JavaScript also listed. They also mention the software development lifecycle, version control and cloud technologies, with Agile, CI/CD, SOLID and design patterns as related knowledge. Experience with machine learning or data analytics is listed as a plus, not a requirement.

This guide is for software engineers interviewing for the Software Engineer role at Aras. The requirements candidates describe typically ask for 2-5 years of development experience. The guide covers all three reported stages: the initial screening, the technical interviews and the behavioral questions. Every question candidates report gets a worked approach: the fundamentals explanations (OOP versus functional programming, REST, Java GC, version control, query optimisation), the coding problems (linked-list reversal, binary search, an open whiteboard challenge), the debugging and production-incident scenarios, and the behavioral prompts. A seven-day plan ties them together.

Candidates report three rounds over about 3-5 weeks. There is an initial screening, then technical interviews with coding assessments and problem-solving exercises, then a stage of behavioral questions.

Focus areas

Problem Solving
Core Computer Science Fundamentals
Technical Communication

Overview

Aras builds PLM software, and the engineers candidates describe work on features of that product with product, UX and engineering colleagues. Their work includes code reviews and fixing issues as they surface. That mix shows up in the reported questions. Some test whether you can write correct code. Others test whether you can explain how your tools work. Others test how you behave when something breaks or a colleague disagrees.

The reported technical questions fall into three groups. The first is fundamentals you explain out loud: object-oriented versus functional programming, RESTful API principles, garbage collection in Java, the role of version control, and optimising a database query. The second is classic data-structure coding: reversing a linked list, implementing binary search, and an unspecified whiteboard challenge. The third is debugging scenarios: how you debug a complex application and what you do when you find a severe bug in production. None of these are obscure. What matters is precision: off-by-one-free binary search, a reversal that does not lose the tail of the list, and a GC answer that names generations and GC roots instead of saying memory is freed automatically.

The stack candidates describe is Java, C#, Python and JavaScript, plus SOLID, design patterns, Agile and CI/CD. One reported question is specific to Java, so be able to explain the JVM heap and collectors even if you plan to code in Python. Have two or three design patterns you have actually used, such as strategy, factory or observer, with the problem each one solved. Have a concrete account of how your current team branches, reviews and ships code.

The behavioral stage and the debugging questions overlap. A good technical-challenge story is often a good debugging story too. Prepare stories built from real incidents: what broke, how you narrowed it down, what you shipped and what you changed afterwards. Use the same material for the prompts about difficult teammates, prioritisation and advocating a technical decision, so each story shows a decision you personally made.

The Aras interview process

These are the three stages candidates report, in the order they report them. Each one below says what candidates describe and how to prepare.

Initial Screening

Candidates report that the process opens with an initial screening that checks qualifications and fit. Treat it as matching your background to the requirements candidates describe. Be clear about which of Java, C# or Python you work in daily, how much hands-on development experience you have, and where you have used version control, CI/CD and cloud services. Use the screening to ask about the format of the technical interviews: which language you may code in, and whether coding happens in an editor or on a whiteboard.

What to demonstrate

How your experience lines up with the reported requirements: languages, years of development, SDLC and version control
How clearly you summarise your own projects and your part in them
Your motivation for working on PLM software at Aras

How to prepare

Write a short spoken summary of your last two roles that names the language, the system you built and one result you can measure
Read a general introduction to product lifecycle management so you can say in plain terms what kind of software the role works on
Map each listed requirement (Java/C#/Python, version control, cloud, Agile, CI/CD) to one concrete example from your CV
Prepare questions about the technical interview format: coding language, editor versus whiteboard, and how the problem-solving exercises are run

Technical Interviews

Candidates report technical interviews that include coding assessments and problem-solving exercises. The reported technical questions are the material to practise for this stage. They mix fundamentals explanations (OOP versus FP, REST, Java GC, version control, query optimisation), data-structure coding (linked-list reversal, binary search, an open whiteboard challenge) and debugging scenarios. Approach the coding by stating the input and edge cases first, writing the code cleanly without relying on an IDE, then tracing a small example by hand and stating the complexity. For the explanation questions, give the mechanism and a concrete example rather than a list of terms.

What to demonstrate

Working, correct code for data-structure and algorithm problems, including edge cases
Problem-solving reasoning you can explain step by step
Core computer science and language fundamentals: memory management, API design, data access
Technical communication while you solve

How to prepare

Implement iterative and recursive linked-list reversal and both closed-interval and lower-bound binary search in your chosen language on paper, then test them on empty, one-element and two-element inputs
Rehearse a spoken explanation of Java garbage collection that covers GC roots, young and old generations, and G1 versus ZGC
Run EXPLAIN on a slow join in a local database, fix it with a composite index, and be ready to narrate the before and after plans
Write out a REST resource design (paths, methods, status codes, ETag-based concurrency) for a versioned record such as a part with revisions

Behavioral Questions

Candidates report interviews that include behavioral questions on interpersonal skills and cultural fit. The reported prompts cover a significant technical challenge, collaborating with a difficult team member, prioritising across several projects, and advocating for a technical decision. Teamwork and handling feedback from peers also appear as example topics. Prepare four or five stories in STAR form. Each one should centre on a decision you made, the trade-off involved and an outcome you can state. Keep the situation short and spend most of the answer on your reasoning and actions.

What to demonstrate

Interpersonal skills: working through disagreement and difficult collaboration
How you prioritise competing work and communicate the trade-offs
Ownership of technical decisions and their outcomes

How to prepare

Write STAR outlines for the four reported behavioral prompts, each with one measurable result
Prepare a story about peer feedback you received and what you changed because of it
Check every story for 'we' and rewrite it to state your own actions and decisions
Prepare an honest answer for 'what would you do differently' on each story

Where candidates lose points

Writing a binary search that loops forever or misses the last element, typically by mixing the closed-interval condition lo <= hi with the half-open update hi = mid

Learn one closed-interval template and one half-open lower-bound template, and know why each update matches its loop condition. Before declaring the code done, trace it on an empty array, a one-element array and a target beyond either end.

Reversing a linked list by setting curr.next = prev before saving the next node, which cuts off the rest of the list

Always write next = curr.next as the first line of the loop body. Draw three nodes and update the arrows step by step on paper while you code. Check that the old head ends up pointing to null.

Answering the Java GC, REST or version-control questions with a list of buzzwords ('GC frees unused memory', 'REST is stateless') and no mechanism

For each fundamentals question, prepare the mechanism plus one example: GC roots and generational collection, idempotent methods and ETag concurrency on a sample resource, rebase versus merge on a shared branch. Practise saying each answer aloud until it holds together without notes.

Answering the production-bug question by jumping straight into debugging code while users are still affected and nobody has been told

Use a fixed order: assess impact, communicate, mitigate (rollback or feature flag), then diagnose, fix with a regression test, clean up the data and write a postmortem. Mention the case where rollback is blocked by a migration.

Telling behavioral stories in 'we' so your own decision is invisible, or describing a difficult teammate only as the problem

Rewrite each STAR outline so the action part uses 'I' and names a decision you made and its trade-off. For the conflict story, include what you changed in your own approach and the effect on the work.

Questions & practice

Can you describe how garbage collection works in Java?

Medium · Technical / Domain Questions

Approach

Start with reachability. The JVM traces live objects from GC roots: local variables on thread stacks, static fields, JNI references and active monitors. Anything unreachable can be reclaimed. Because this is tracing, not reference counting, cycles of objects that reference each other are still collected.
Explain the generational heap. Most objects die young, so new objects go into Eden in the young generation. A minor GC copies the survivors into a survivor space and ages them. Objects that pass the tenuring threshold are promoted to the old generation, which is collected less often by mark-sweep-compact style collection.
Name the collectors and their trade-offs. Serial suits small heaps. Parallel maximises throughput with stop-the-world pauses. G1, the default since JDK 9, is region-based and aims at a pause target set with -XX:MaxGCPauseMillis. ZGC and Shenandoah do most of the work concurrently for very short pauses, at some cost in CPU and throughput.
Say what GC does not fix. Leaks still happen when references are kept by mistake: static collections, unbounded caches, listeners never removed, ThreadLocal values in pooled threads. To diagnose, turn on GC logs (-Xlog:gc*), take a heap dump with jcmd GC.heap_dump and find the dominating retained objects in a tool such as Eclipse MAT.
Mention reference types and cleanup. Soft references are cleared under memory pressure, which suits caches. Weak references are cleared at the next GC, as in WeakHashMap. finalize() is deprecated, so release non-memory resources with try-with-resources or Cleaner. If C# is your main language, note that .NET also uses a generational collector, with generations 0, 1 and 2.

Follow-up

A service throws OutOfMemoryError after running for days. Walk through how you find the cause.
What causes long full-GC pauses, and what would you change to reduce them?
What is the difference between a soft and a weak reference, and when would you use each?

Write a function to reverse a linked list.

Medium · Coding / Algorithms

Approach

Clarify the node shape (a singly linked node with val and next) and that you will reverse in place and return the new head. Name the edge cases up front: an empty list, a single node and two nodes.
Write the iterative version with three pointers: prev = null, curr = head. In the loop: next = curr.next; curr.next = prev; prev = curr; curr = next. Return prev. That is O(n) time and O(1) extra space.
Explain the bug to avoid. Save curr.next before overwriting it, or you lose the rest of the list. Also check that the old head ends up pointing to null, or you create a cycle.
Give the recursive alternative. Reverse the rest of the list, then set head.next.next = head and head.next = null. It is O(n) time but uses O(n) stack, so a very long list can overflow the stack in Java or Python.
Trace 1 -> 2 -> 3 by hand to show the pointer states after each step, then state the complexity.

Follow-up

Reverse only the nodes between positions m and n.
Reverse the list in groups of k nodes.
Use reversal to check whether a linked list is a palindrome in O(1) extra space.

How would you implement a binary search algorithm?

Medium · Coding / Algorithms

Approach

State the preconditions: the input is sorted and supports random access. Choose one invariant and keep it. For the closed interval [lo, hi], start with lo = 0 and hi = n - 1, loop while lo <= hi, and move to lo = mid + 1 or hi = mid - 1.
Compute mid = lo + (hi - lo) / 2 rather than (lo + hi) / 2, to avoid integer overflow in Java and C# on large arrays. Return the index when found and -1 when not. That is O(log n) time and O(1) space iteratively.
Know the lower-bound variant with a half-open interval [lo, hi): loop while lo < hi, setting hi = mid when a[mid] >= target and lo = mid + 1 otherwise. It returns the first index whose value is >= target. This handles duplicates and insertion points.
Test the edge cases explicitly: empty array, one element, target below the minimum or above the maximum, and runs of duplicates. Mixing the closed-interval loop condition with the half-open updates causes infinite loops.
Mention that binary search also works over an answer space when a predicate is monotonic, for example the minimum capacity that ships all packages in D days. Know the library forms: Java's Arrays.binarySearch returns -(insertion point) - 1 when the target is missing.

Follow-up

Find the first and last positions of a target in a sorted array with duplicates.
Search for a target in a sorted array that has been rotated.
Compute the integer square root of n using binary search.

Can you solve this coding challenge on a whiteboard?

Medium · Coding / Algorithms

Approach

Since the problem is not known in advance, rehearse a fixed opening. Restate the problem, confirm input types, sizes and constraints, and work one small example and one edge case before writing any code.
State a brute-force solution and its complexity, then improve it using a named pattern. Hash maps give O(1) lookups. Two pointers work on sorted input. A sliding window handles contiguous ranges. BFS or DFS handles graphs and grids. A heap handles top-k.
On a whiteboard, write in the language you know best (Java, C# and Python are all in the requirements candidates describe), use clear names, extract helper functions and leave space for corrections.
Trace your code on the example and on an edge case (empty input, duplicates, negatives) before calling it done, then state the time and space complexity.
Keep talking through each decision, because without a compiler your reasoning is the only evidence that the code works.

Follow-up

Can you reduce the space complexity?
What changes if the input arrives as a stream too large to hold in memory?
Which test cases would you write for this function?

How would you optimize a database query for performance?

Medium · Technical / Domain Questions

Approach

Measure before changing anything. Get the actual execution plan for the slow query (EXPLAIN ANALYZE in PostgreSQL, the actual execution plan plus SET STATISTICS IO in SQL Server). Note where the time and rows go: full scans on large tables, nested loops over many outer rows, sorts that spill, and estimated row counts far from actual ones.
Fix access paths with the right index. Use a composite index with the equality columns first and the range column last, and a covering index (INCLUDE columns) to avoid lookups back to the table. Keep predicates sargable: a function on an indexed column, a LIKE pattern that starts with a wildcard, an OR across columns or an implicit type conversion can each stop the index from being used.
Rewrite the query shape. Select only the columns you need. Replace correlated subqueries with joins or EXISTS. Aggregate before joining when a one-to-many join would multiply rows. Use keyset pagination (WHERE id > last_id ORDER BY id LIMIT n) instead of large OFFSETs. Check the application for N+1 queries generated by an ORM.
Check the statistics and the environment. Stale statistics give bad plans, so refresh them with ANALYZE or UPDATE STATISTICS. Parameter sniffing can lock in a plan that suits one value but not another. Lock contention can make a fast query look slow.
State the trade-offs. Every index slows inserts and updates and uses storage. Beyond indexing, the options are caching, materialised views or summary tables, and partitioning very large tables, each adding staleness or operational cost.

Follow-up

The query is fast in development and slow in production. What are the likely reasons?
You added an index but the plan still shows a full scan. Why might that be?
How would you find and fix N+1 queries coming from an ORM?

Stop tag and share joins from fanning out a page

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

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

Approach

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

Follow-up

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

Explain the difference between object-oriented and functional programming.

Medium · Technical / Domain Questions

Approach

Define each by how it organises code. OOP bundles state with the behaviour that changes it (encapsulation) and varies behaviour through inheritance or interfaces with dynamic dispatch. FP builds programs from pure functions over immutable data and treats functions as values (higher-order functions, map/filter/reduce).
Make side effects the central contrast. A pure function returns the same output for the same input and touches no shared state, so it is easy to test, cache and run in parallel. OOP controls mutation by hiding it behind methods instead of removing it.
Name the expression problem as the design trade-off. In OOP, adding a new type is a new subclass, but adding a new operation touches every class. With algebraic data types and pattern matching, adding an operation is one function, but adding a variant touches every match.
Show that mainstream languages mix the two. Examples: Java streams and lambdas, records as immutable value objects, C# LINQ, and Python comprehensions with functools. A typical service keeps an object model for entities with identity and lifecycle and uses functional pipelines to transform collections.
Close with when you would choose each. Objects suit long-lived domain entities whose state changes through defined operations. A functional style suits stateless transformations, concurrency-heavy code and logic you want trivially unit-testable.

Follow-up

How does immutability make code thread-safe, and what does it cost in allocation and copying?
Rewrite this loop that filters and sums orders as a Java stream. When would the loop be the better choice?
Which SOLID principles still matter in functional code?

What are the main principles of RESTful API design?

Medium · Technical / Domain Questions

Approach

Start with resources and the uniform interface. Use nouns in paths, such as /parts/{id}/revisions, not verbs. Use HTTP methods for their meaning: GET is safe and idempotent, PUT and DELETE are idempotent, POST creates or triggers and is not idempotent, and PATCH is a partial update.
Explain statelessness and cacheability. Each request carries its own authentication (for example a bearer token), so any server instance can handle it and the service scales horizontally. Responses state their cacheability with Cache-Control and ETag, and clients revalidate with If-None-Match to get 304 Not Modified.
Use status codes precisely. 201 Created with a Location header, 204 No Content, 400 for malformed input, 401 for unauthenticated versus 403 for forbidden, 404, 409 for a state conflict, 422 for semantically invalid input, 429 for rate limiting, and 5xx only for server faults. Return a consistent error body with a machine-readable code.
Cover the practical concerns. Use optimistic concurrency with If-Match and 412 Precondition Failed when two clients edit the same record. Use an idempotency key on POST so a retried request does not create a duplicate. Prefer cursor pagination over large offsets, and filter through query parameters.
Address versioning and evolution. Additive changes are safe. Breaking changes go under a new version, either /v2 or a media-type header. Mention HATEOAS as the REST constraint most real APIs only partly follow.

Follow-up

What is the difference between PUT and PATCH, and which one is idempotent?
Two users save changes to the same record at once. How does your API stop one from silently overwriting the other?
How would you remove a field from a response without breaking existing clients?

What is the role of version control in software development?

Medium · Technical / Domain Questions

Approach

State what version control provides. It gives a complete history of who changed what and why, the ability to revert or compare any two states, parallel work on branches, code review through pull requests, and tagged releases that can be rebuilt exactly. It is also what CI/CD pipelines trigger from.
Show working Git knowledge. Commits are snapshots linked to their parents, and branches are movable pointers. Merge keeps history as it happened, while rebase rewrites commits onto a new base, so never rebase a branch others have pulled. Undo shared history with git revert, not reset plus force-push.
Compare branching strategies. Trunk-based development uses short-lived branches, frequent integration and feature flags for unfinished work. GitFlow-style release branches suit products with versioned releases that need patching. Say which one your team used and what it cost.
Give habits that make history useful. Keep commits small and atomic with messages that explain why. Protect the main branch with required reviews and green CI. Use git bisect to find the commit that introduced a regression in O(log n) test runs.
Optionally link it to the domain. PLM software also tracks revisions and changes to product data, so the ideas of history, branching and controlled release carry over to the product itself.

Follow-up

When would you merge instead of rebase, and what goes wrong if you rebase a shared branch?
A bad commit is already on main and others have pulled it. How do you undo it?
How would you find which of the last 200 commits introduced a failing test?

If you discovered a critical bug in production, what steps would you take to address it?

Medium · Problem-Solving / Case Studies

Approach

Assess impact first. Who is affected? Is data being corrupted or only displayed wrongly? Is it spreading? Then raise it at once through the team's incident channel or on-call process. A severe bug is a communication problem before it is a coding problem.
Mitigate before finding the root cause. Roll back the last deploy, turn off the feature flag, or stop the job that is writing bad data. Choose a forward hotfix only when rollback is impossible or riskier, for example after a schema migration that cannot be undone.
Then diagnose and fix properly. Reproduce in a staging environment with production-like data, write a regression test, and ship through review and CI, even on an expedited path.
Clean up and verify. Repair or backfill any corrupted records, confirm recovery on dashboards and error rates, and keep stakeholders updated until it is closed.
Finish with a blameless postmortem covering the timeline, root cause, why tests and monitoring missed it, and owned action items such as new alerts, tests or deploy safeguards.

Follow-up

Rollback is impossible because a database migration already ran. What now?
How would you communicate with affected customers or internal users during the incident?
What makes a postmortem action item useful rather than a formality?

How would you approach debugging a complex application?

Medium · Problem-Solving / Case Studies

Approach

Reproduce first. Pin down the exact inputs, version, configuration and data that trigger the problem. If you cannot reproduce it, collect evidence: logs with correlation IDs across services, metrics around the time of failure, and the deploy history showing what changed.
Narrow the search space by bisection. Split the system at boundaries (client, API, service, database) and check which side the bad value first appears on. Use git bisect across commits, and use feature flags or config toggles to isolate components.
Use the tool that fits the symptom. Use a debugger with conditional breakpoints for logic errors and a profiler for slowness. Use heap dumps for memory growth, thread dumps (jstack, or dotnet-dump for .NET) for hangs and deadlocks, and the database's query log or plan for data-access issues.
Change one variable at a time and write a failing test that captures the bug before you fix it. The test proves the fix and prevents a regression.
Separate trigger from root cause. A deploy or a traffic spike may expose a latent bug. After fixing it, look for the same pattern elsewhere in the codebase. Expect timing-dependent bugs to change when you add logging.

Follow-up

The bug happens only in production and you cannot attach a debugger. What do you do?
How do you approach a failure that happens on roughly one request in a thousand?
At what point would you ask a colleague for help, and what would you bring them?

Suggested practice checklist

The plan follows the three reported rounds. It starts with the screening, then works through the reported coding, fundamentals and debugging questions, and ends with the behavioral stories and a full mock run.

Day 1 — Screening readiness and the role's requirements

List the requirements candidates describe (Java/C#/Python, SDLC, version control, cloud, Agile, CI/CD) and write one concrete example from your CV next to each
Write and rehearse a short summary of your last two roles that names the language, the system and one measurable result
Read a general introduction to product lifecycle management and write three sentences on what PLM software manages
Write the questions you will ask about the technical interview format: language, editor or whiteboard, problem-solving format

Deliverable: A requirements-to-evidence table, a rehearsed background summary, a short PLM note and a list of format questions for the screening.

Day 2 — Linked-list reversal and binary search

On paper, implement iterative and recursive linked-list reversal, then the variant that reverses positions m to n
Implement closed-interval binary search and half-open lower bound, then first and last occurrence with duplicates
Trace every solution on empty, one-element and two-element inputs and note any bug you find
Solve rotated-sorted-array search and integer square root as binary-search variants

Deliverable: Hand-written, hand-traced solutions for both reversal methods, the sublist reversal, three binary-search forms and two variants, with a list of the bugs you caught.

Day 3 — Whiteboard problem solving

Solve three unseen problems from different patterns (hash map, two pointers or sliding window, BFS/DFS) on a whiteboard or paper, talking aloud
For each, state the brute force and its complexity before the improved solution
After each, trace one edge case and write the time and space complexity

Deliverable: Three whiteboard solutions, each with brute force, optimised version, traced edge case and stated complexity.

Day 4 — Databases and REST APIs

In a local database, write a slow join, capture its plan with EXPLAIN ANALYZE, add a composite or covering index and capture the plan again
Solve the SQL practice question on stopping joins from fanning out, and explain why aggregating before the join fixes it
Design REST endpoints for a record with revisions: paths, methods, status codes, If-Match concurrency, pagination and versioning

Deliverable: Before and after query plans with a written explanation, a solved fan-out SQL question, and a one-page REST resource design.

Day 5 — Language and engineering fundamentals

Draw the JVM heap (Eden, survivor spaces, old generation), then explain minor versus full GC and G1 versus ZGC aloud
Solve one small problem twice in Java (or C#): once with classes and mutation, once with streams or LINQ and immutable data, and list the trade-offs
In a toy Git repository, practise merge versus rebase, git revert on a pushed commit and git bisect to find an introduced bug

Deliverable: A GC diagram with a spoken explanation, two versions of the same solution with trade-off notes, and a Git exercise log.

Day 6 — Debugging and production incidents

Take a real bug you fixed and write it up as a debugging sequence: reproduction, bisection, tool used, root cause, regression test
Write an incident-response checklist for a severe production bug, including the case where rollback is blocked by a migration
Write the STAR outline for your significant technical challenge, reusing the debugging write-up where it fits

Deliverable: A written debugging walkthrough, a production-incident checklist and a STAR outline for the technical-challenge prompt.

Day 7 — Behavioral stories and full mock

Write STAR outlines for the difficult-teammate, prioritisation and technical-advocacy prompts, plus one on peer feedback
Read each outline and replace every 'we' with what you personally did
Run a mock interview with a friend: one coding problem, one fundamentals explanation and two behavioral prompts, then list what to fix

Deliverable: Four STAR outlines in first person and a mock-interview notes sheet listing the fixes to make before the real loop.

Behavioural preparation

Candidates report a stage of behavioral questions on interpersonal skills and cultural fit. The reported prompts cover a hard technical problem, a difficult collaborator, competing priorities and arguing for a technical choice. Each needs a specific story in which your own decision and its trade-off are clear.

Tell me about a time when you worked as part of a team to achieve a goal.
How do you handle feedback from peers?

Describe a time when you faced a significant technical challenge. How did you resolve it?

Medium · Problem-Solving / Case Studies

Approach

Pick a challenge with real technical depth that you drove yourself. Good examples are a performance regression, data corruption, a concurrency bug or a hard migration. Avoid a story that was just a lot of work.
Structure it as STAR. Give the situation and stakes briefly: who was affected and what would break. Then spend most of the answer on how you diagnosed it, the options you weighed and why you chose one.
Name the trade-off you accepted explicitly, for example a quick mitigation now and a proper fix later, or a simpler design at some performance cost.
End with a measurable result (latency, error rate, hours saved, incidents avoided) and one change you made afterwards, such as a test, an alert or a design guideline.
The mistake that sinks this answer is a team story told with 'we', where the listener cannot tell what you personally did or decided.

Follow-up

What alternatives did you reject, and why?
How did you confirm your fix was what actually solved the problem?
What would you do differently if you faced it again?

Describe a situation where you had to collaborate with a difficult team member.

Medium · Behavioral / Leadership

Approach

Choose a real disagreement about work behaviour, such as missed reviews, blocking decisions or dismissive feedback, and describe the behaviour, not the person's character.
Show that you first tried to understand their constraints and goals, then had a direct, private conversation focused on specific situations and their impact on delivery.
Describe the mechanism that resolved it: agreed review turnaround, a written design doc both sides commented on, clearer ownership boundaries, or shared criteria for the decision. Mention escalation only as a later step, if it happened.
End with the outcome for the project and the working relationship, and what you changed in your own approach.
The mistake that sinks this answer is a story where the other person is simply the villain and you learned nothing.

Follow-up

What would you have done if the conversation had not changed anything?
What did this teach you about your own working style?
When do you decide to involve your manager?

How do you prioritize tasks when working on multiple projects?

Medium · Behavioral / Leadership

Approach

State the criteria you use: impact on users or the business, hard deadlines, dependencies (who is blocked on you) and cost of delay. Contrast that with simply working through whatever arrived first.
Explain how you make trade-offs visible. List the competing work, give your recommended order and its reasoning, and confirm it with your manager or stakeholders instead of deciding silently.
Give a concrete example where you had to delay or drop something, how you negotiated it, and what happened as a result.
Cover interruptions. Say how you handle a production bug or urgent request mid-sprint, for example by timeboxing the work, re-planning openly and telling the people whose work slips.
The mistake that sinks this answer is a generic description of to-do lists with no real conflict and no decision.

Follow-up

Two stakeholders each insist their project comes first. What do you do?
Tell me about a time your prioritisation turned out to be wrong.
How do you keep an urgent bug from derailing a planned release?

Tell me about a time when you had to advocate for a technical decision.

Medium · Behavioral / Leadership

Approach

Set up the decision briefly: what was at stake (for example a library choice, a refactor or a migration, a testing approach) and what the default path would have been.
Show how you built the case with evidence rather than opinion: a prototype, a benchmark, incident history or cost estimates, plus the options you compared and their downsides.
Describe how you handled objections. Name the strongest counterargument, how you addressed or accepted it, and who you had to convince.
Give the outcome either way. If adopted, give the measurable result. If overruled, show that you committed to the chosen path and what you learned.
The mistake that sinks this answer is presenting it as you being right and others being wrong, with no real trade-off acknowledged.

Follow-up

What was the best argument against your position?
What would you have done if the decision had gone the other way?
How did the decision hold up six months later?