The reported questions fall into five groups: technical and domain questions (languages, code quality, methodologies), system design, problem-solving scenarios, coding, and behavioral questions. Only the coding group is described as hands-on, as live exercises or take-home assignments. The other groups are conversations where you reason aloud, so each answer needs a concrete project from your own history behind it.
What sets this loop apart from a generic software loop is the hardware angle. The reported design questions ask how you would design a software system for a power management application and how you would integrate new software with existing hardware. Rehearse those answers with a device-facing mindset: devices as data sources that can go offline, send malformed readings or run different firmware versions, how readings are stored and queried, how commands reach a device and how a failed command is detected, and how you test without the physical unit. You do not need to claim power-systems experience you lack, but you should be able to reason about the software side of a hardware boundary.
The requirements candidates describe name C++, Python or Java, Agile or Scrum, Git, testing and debugging, and hardware integration, with cloud, IoT and machine learning as extras. Choose one of the three languages for coding and be ready to explain a project in each language you list. Prepare short, honest answers on cloud, IoT and machine learning in power management, because candidates report those as occasional questions rather than the core.
The final stage is behavioral, and the reported prompts are about advocating a technical decision to a non-technical audience, handling team conflict, leading a project, prioritizing across projects and what motivates you. Given the cross-functional teams candidates describe, pick stories that involve people outside software: a quality engineer, a product manager or a mechanical colleague. Build each story around a decision you made and its measurable result.
Initial Screening
reportedCandidates describe the Initial Screening as the first filter, where your background is checked against the basic requirements for the role. The requirements candidates describe list C++, Python or Java, Agile or Scrum, Git, software testing and debugging, and some understanding of connecting software to hardware. Approach it as a short, structured tour of your experience that maps directly onto that list. Have one project ready per language you claim, and be ready to say which methodology your last team used and what you did inside it. Ask the recruiter what the later technical interviews will cover and whether coding is live or a take-home.
What to demonstrate
- Whether your experience matches the listed requirements, such as languages, Git and testing
- How clearly you can summarise your projects and your own contribution
- Basic fit for the role and its cross-functional setting
How to prepare
- Write one project per language you list (C++, Python or Java), with what you built and what you owned. This also covers the proficiency question on languages.
- Prepare a two-sentence answer to which software development methodology you have used and which you prefer, with one concrete example of how it shaped a release.
- Prepare to mention any contact with hardware, embedded code, device APIs, simulators or test rigs, even from a side project.
- Write questions for the recruiter: the number of technical interviews, whether coding is live or take-home, and who you will meet.
Technical Interviews
reportedThe Technical Interviews are described as one or more interviews on technical knowledge and problem-solving. The questions candidates report cover several areas: technical and domain questions (languages, a complex algorithm you implemented, code quality, a hard problem you resolved, methodologies), system design (a power management application, architecture factors, data storage trade-offs, integrating with existing hardware, requirements gathering), problem-solving scenarios (intermittent failures, tight deadlines, performance metrics, evaluating a technology, risk assessment) and coding (reversing a linked list, optimizing a search, a basic REST API, data structure manipulation, explaining your thought process). Candidates describe the coding as live exercises or take-home assignments. Practise each area in the form it takes: write code in your chosen language, talk through designs aloud, and narrate a debugging sequence step by step.
What to demonstrate
- Working code on core data-structure and API problems, in a language you chose
- Design reasoning where software meets hardware, including storage choices and integration
- A structured debugging and performance-analysis process for intermittent or slow systems
- How clearly you explain your thought process while solving
How to prepare
- Write an iterative linked-list reversal from memory in your chosen language, then the recursive version, and test it on empty, one-node and two-node lists.
- Sketch a power management application end to end: device readings in, storage and queries, alerts, commands out, and what happens when a device is offline. Then do the same for a hardware integration.
- Write a minimal REST API with create, read, update and delete routes, input validation and consistent error responses, and practise explaining the status codes you chose.
- Rehearse a debugging walk-through for a failure that happens only sometimes: reproduce, add logging, narrow the variables, then confirm the fix.
Behavioral Interviews
reportedCandidates describe the Behavioral Interviews as the stage that checks how well you align with the company's values and culture. The reported prompts are about advocating a technical decision to a non-technical audience, handling conflict within a team, leading a project and its outcome, prioritizing across several projects, and what motivates you. The role is described as cross-functional, so stories involving quality assurance, product management or mechanical colleagues fit well. Approach it with a small set of stories you have rehearsed aloud, each showing a decision you made, the trade-off, and a measurable result.
What to demonstrate
- Communication of technical decisions to people outside engineering
- Collaboration and conflict handling inside cross-functional teams
- Ownership of a project or initiative, with a concrete outcome
- Alignment with the values and culture as candidates describe the stage
How to prepare
- Write five stories, one for each reported behavioral prompt, and name the decision, the alternative you rejected and the result for each.
- Build at least one story that includes a non-software colleague, such as a quality engineer or a product manager, and what you changed to work with them.
- Rehearse each story aloud until the situation takes two or three sentences and the reasoning gets most of the time.
- Prepare an honest answer on what motivates you, supported by a real project rather than a slogan.
PracHub editorial advice for the preparation topics above.
Reversing a linked list by overwriting the next pointer before saving it, or skipping empty and single-node lists
Keep three references (previous, current, next), save next before you re-point current, and return previous at the end. Trace the code on an empty list, one node and two nodes before declaring it done, and state the O(n) time and O(1) space.
Answering the power management design question as a generic web application, with no mention of devices that go offline, send bad readings or run different software versions
Start by naming the devices and what they send, then design for loss and delay: buffering, idempotent ingestion, timestamps from the device, a storage choice matched to the query pattern, and a plan for validating changes without the physical unit.
Answering the intermittent-failure question with a guessed fix instead of a method
Describe the sequence: capture what is known (when, how often, what changed), add logging to make the failure visible, reproduce it under controlled conditions, vary one factor at a time, and confirm the fix by running it long enough to trust. Mention shared state, timing and environment as common causes.
Listing C++, Python and Java as skills without a project behind each, then stumbling on the follow-up about how you applied them
List only languages you can discuss in depth. For each, prepare what you built, one design decision you made in it, and one bug or performance problem you solved.
Telling behavioral stories in the first person plural, with no decision, trade-off or result you can name
Rewrite each story with I for your actions, one rejected alternative and one measurable outcome. Practise each aloud so the setup is two or three sentences and the reasoning gets most of your time.
Choose a category, try a prompt, then open its approach, worked solution or follow-up when you need it.
Can you describe a recent project where you implemented a complex algorithm?
Can you describe a recent project where you implemented a complex algorithm?
Approach
- Pick an algorithm you designed or implemented yourself, not one you called from a library. State the problem, the input size and the constraint that made the simple version fail.
- Give the before and after complexity, for example an O(n^2) pairwise comparison replaced by sort plus sweep in O(n log n), or a hash map pass in O(n), and the measured effect.
- Explain how you proved it correct: the invariant it maintains, and differential tests comparing it with a brute-force version on random inputs.
- Name the trade-offs you accepted: extra memory, harder-to-read code, or numerical stability if it used floating point.
- Cover the edge cases you hit: empty input, duplicates, extreme values, worst-case ordering.
Follow-up
- What was the worst-case input, and did you test it?
- Why not use an existing library implementation?
- How did you check correctness beyond the happy path?
Write a function to reverse a linked list.
Write a function to reverse a linked list.
Approach
- Iterative, three pointers: prev = null, curr = head; while curr is not null, save next = curr.next, set curr.next = prev, advance prev = curr and curr = next; return prev. O(n) time, O(1) extra space.
- Recursive alternative: reverse the rest, then set head.next.next = head and head.next = null. It is also O(n) time but uses O(n) stack, so a long list can overflow the stack.
- Edge cases: empty list, one node, two nodes. Confirm the old head ends with next = null so no cycle is left behind.
- In C++, be explicit about ownership: raw pointers need no frees here, but with std::unique_ptr links each relink needs std::move. For a doubly linked list, swap prev and next on every node.
- Test by building lists from arrays, reversing, and comparing with the reversed array.
Follow-up
- Reverse only the nodes between positions m and n.
- Reverse the list in groups of k.
- How would you check for a cycle before reversing? (Floyd's tortoise and hare)
How would you optimize a search algorithm to improve its performance?
How would you optimize a search algorithm to improve its performance?
Approach
- Clarify what is being searched: an unsorted array, sorted data, key lookups, text, or a graph or state space. Ask for data size, how often it changes and how many queries there are.
- For lookups: a linear scan is O(n); sorting once for O(n log n) then binary searching is O(log n) per query; a hash table gives O(1) average for exact match; a balanced tree (std::map, TreeMap) gives O(log n) with ordered and range queries.
- For paths: BFS for unweighted shortest paths, Dijkstra with a binary heap in O((V + E) log V) for non-negative weights, A* with an admissible heuristic to cut explored nodes, plus pruning and memoisation.
- Constant factors matter too: contiguous cache-friendly arrays, early exit, no allocation inside the loop, precomputed indexes that trade memory for speed.
- Benchmark on realistic data. Sorting only pays off when enough queries amortise its cost.
Follow-up
- When is binary search not worth it?
- How would you answer range queries over timestamped readings?
- What changes when the data no longer fits in memory? (B-trees, on-disk indexes)
Solve a coding challenge that involves manipulating data structures.
Solve a coding challenge that involves manipulating data structures.
Approach
- Clarify the structure, the operations needed, input sizes and constraints, and work a small example by hand before coding.
- Pick the structure from the operations: an array for index access, a hash map for O(1) average lookup and counting, a stack for nesting and matching, a queue for BFS and level order, a heap for top-k in O(n log k), and a hash map plus doubly linked list for an LRU cache with O(1) get and put.
- State the brute-force version and its complexity, then name the step the better structure removes.
- Cover edge cases: empty input, a single element, duplicates, null pointers, very large input. Trace a test case through the finished code.
Follow-up
- Implement an LRU cache with O(1) get and put.
- Find the k largest values in a stream.
- What is the memory overhead of the structure you chose?
Explain your thought process as you work through the coding problem.
Explain your thought process as you work through the coding problem.
Approach
- Restate the problem, confirm inputs, outputs and constraints, and agree on a small example before writing code.
- Say the brute-force version and its complexity out loud, then name the bottleneck the better approach removes and its complexity.
- While coding, state what each variable and loop invariant means, briefly enough that the code keeps moving.
- Test aloud: trace the example through the code, then the edge cases. Fix bugs openly, and finish with the final time and space complexity and what you would improve.
Follow-up
- What would change if the input arrived as a stream?
- Which part of your solution are you least sure is correct?
- How would you test this as production code?
Hold a per-tenant active cap against concurrent creates
A tenant on the standard plan may hold at most 50 resources with status='active'. The create handler runs SELECT count(*) FROM resource WHERE tenant_id = $1 AND status = 'active', compares to 50, then inserts. Two creates arrive 3 ms apart on different instances and the tenant lands at 51. Name the anomaly, say whether PostgreSQL 16 READ COMMITTED or REPEATABLE READ prevents it and why, then give an implementation that holds the cap at READ COMMITTED with the exact statements. Finally, say what changes when the cap is 'at most one running export per tenant' on job_run.
Approach
- Name it: write skew. The two transactions read an overlapping set and write disjoint rows, so there is no row-level conflict for the engine to detect and each commit is individually legal.
- Rule out the levels precisely. READ COMMITTED takes a fresh snapshot per statement and takes no lock on the counted rows, so both see 49. PostgreSQL's REPEATABLE READ is snapshot isolation: it removes non-repeatable reads and phantoms within the snapshot but still admits write skew, because the anomaly is not a re-read of a changed row, it is a read of a set that a concurrent transaction invalidates. Only SERIALIZABLE closes it, by tracking the read dependency and aborting one transaction with SQLSTATE 40001 — a guarantee that exists only if the application re-runs the whole transaction from the read.
- Convert the set predicate into a single-row conflict: keep tenant.active_resource_count and run UPDATE tenant SET active_resource_count = active_resource_count + 1 WHERE tenant_id = $1 AND active_resource_count < 50 in the same transaction as the INSERT. Zero affected rows is the cap, returned as 409. The row lock serialises the decision at any isolation level, and contention is bounded to one tenant's row — which is also the fair-scheduling unit, unlike a global counter that would convoy every tenant behind one row.
- State the cost you just took on: a counter is a second source of truth that can drift, so every path that changes status must adjust it inside the same transaction, and a periodic reconciliation has to exist, with resource_revision as the authority for what the count should have been.
Follow-up
- A resource moves from archived back to active. Which statements change, and what breaks if the counter update and the status change land in different transactions?
- The cap becomes plan-dependent and a plan can change mid-month. Where does the number 50 live, and who reads it?
Replace offset paging on the resource feed with keyset
resource holds resource_id, tenant_id, owner_user_id, title, body_ref, version, status ('draft','active','archived','deleted'), created_at, updated_at, deleted_at, with an index on (tenant_id, status, updated_at DESC, resource_id DESC). The listing endpoint returns active resources for one tenant, newest update first, 50 per page, today with LIMIT 50 OFFSET n. Tenants reach page 400 and rows are created while they read. Write the keyset query, define what the cursor carries and how it is encoded, and say which part of the index each predicate uses. Assume PostgreSQL 16.
Approach
- Name the two failures separately. OFFSET 20000 makes the server produce and discard 20,000 rows, so page cost grows with depth rather than with page size. Independently, any write that changes how many rows sort above the offset moves the window between two fetches, and the direction decides which anomaly you get: an insert lands at the head of updated_at DESC and pushes already-returned rows down past the boundary, so they are returned a second time; a delete above the offset, or a row whose updated_at is bumped above the cursor, pulls rows up and one is never returned at all. Nothing in the response reveals either.
- Write the seek: WHERE tenant_id = $1 AND status = 'active' AND (updated_at, resource_id) < ($2, $3) ORDER BY updated_at DESC, resource_id DESC LIMIT 50. The row-value comparison is one index range rather than a disjunction, and both columns are NOT NULL, which is what makes that comparison well defined.
- Map each predicate onto the index: tenant_id and status are equality on the leading columns, (updated_at, resource_id) is the range, and the ORDER BY matches the index order so no Sort node appears and the scan stops after 50 rows. The DESC in the definition only matters for mixed directions — a plain ascending btree on the same columns is read backwards for this query.
- Put both sort columns in the cursor and nothing the client can tamper with into another tenant: base64 of (updated_at, resource_id), validated server-side, with tenant_id taken from the principal.
Follow-up
- The client asks for 'jump to page 400'. What do you offer instead, and what does the honest version cost?
- Sort order becomes user-selectable across four columns. How many indexes is that, and which would you refuse to add?
What programming languages are you proficient in, and how have you applied them?
What programming languages are you proficient in, and how have you applied them?
Approach
- Name two or three languages and give the depth for each, tied to something you shipped. The requirements candidates describe for this role list C++, Python or Java, so map your experience onto those first.
- For C++, show you understand ownership: RAII, std::unique_ptr versus std::shared_ptr, deterministic destruction, and avoiding heap allocation in tight loops or timing-sensitive code.
- For Python or Java, name what you used it for (test harnesses, tooling, data analysis, services) and one runtime property that mattered, such as the GIL in CPython or garbage-collection pauses in Java.
- Explain one language choice you made for a component and the deciding factor: memory control, latency, library support or team skill.
- Say plainly which languages you use in production and which you can only read.
Follow-up
- In C++, how do you prevent leaks and dangling pointers without a garbage collector?
- When would you choose Python over C++ for a component that talks to hardware?
- How do you approach learning a new programming language?
How do you ensure code quality and maintainability?
How do you ensure code quality and maintainability?
Approach
- Describe the layers you use: unit tests, integration tests (against a simulator or real hardware for device code), code review, and CI that blocks merges in Git when checks fail.
- Name the static tools for your languages: clang-tidy or cppcheck and clang-format for C++, mypy, a linter and black for Python, and compiler warnings treated as errors.
- Explain the design side: small modules with narrow interfaces, and hardware access behind an abstraction so logic can be tested with fakes.
- Say what you look for in a review: error handling, resource cleanup, naming, and whether the tests cover the change.
- Give a measure you track, such as coverage on changed lines or escaped defects, rather than chasing a global coverage number.
Follow-up
- How do you unit-test code that talks to a hardware device?
- What do you do when a deadline pushes you to skip tests?
- What do you check first when reviewing a teammate's pull request?
Describe a challenging technical problem you faced and how you resolved it.
Describe a challenging technical problem you faced and how you resolved it.
Approach
- Choose a problem with a concrete symptom and a root cause that was not obvious. Give the symptom, its impact and your constraints in two sentences.
- Walk through the diagnosis: each hypothesis, the evidence that ruled it out (logs, profiler, debugger, git bisect), and what pointed to the real cause.
- Explain the fix and why you chose it over the alternatives, then how you verified it: a regression test, a metric, a soak run.
- Close with what changed afterwards: a new test, alert or check that keeps the problem from coming back.
- Keep the story about your own actions; say "I" and name where others helped.
Follow-up
- How did you confirm you fixed the root cause and not just the symptom?
- What would you do differently now?
- Who else did you involve, and at what point?
What software development methodologies are you familiar with, and which do you prefer?
What software development methodologies are you familiar with, and which do you prefer?
Approach
- Contrast the main options concretely. Scrum: sprints, backlog, planning and retrospectives. Kanban: continuous flow with WIP limits. Waterfall or the V-model: phase gates, with requirements traced to tests.
- The requirements candidates describe for this role mention Agile or Scrum, so describe exactly how your team ran it: sprint length, how stories were sized, what the definition of done included.
- Make your preference depend on context: hardware lead times and fixed release dates push toward planned milestones, while software-only parts can iterate quickly. Hybrids are common.
- Give an example where the process broke, for example sprint commitments missed because hardware arrived late, and what you changed.
Follow-up
- How do you run sprints when the hardware you depend on is late?
- What signal would tell you the process is not working?
- How do you handle a requirement change in the middle of a sprint?
How would you design a software system for a power management application?
How would you design a software system for a power management application?
Approach
- Fix the scope first: what is being managed (sources, loads, a test stand), which quantities are measured (voltage, current, temperature), sample rates, device count, local versus remote control, and the safety requirements.
- Layer it: device drivers or protocol adapters behind one interface, a control layer with an explicit state machine on a local controller, telemetry ingestion into a time-series store, and an API or UI for supervision and alarms.
- Keep protection and control logic local and deterministic, never dependent on the network or a cloud service. Add a watchdog, a defined safe state on communication loss, limit checks on every command, and an audit log of who changed what.
- On the data path, timestamp at the source, buffer at the edge with store-and-forward, and set retention tiers with downsampled history.
- Name the trade-offs: polling versus event-driven reporting, sample rate versus bandwidth and storage, and versioned configuration so a bad change can be rolled back.
Follow-up
- What happens to the equipment if the link to the supervisory server drops?
- How would the design change going from ten devices to thousands?
- How would you roll out a firmware or configuration update safely?
What factors do you consider when deciding on an architecture for a new software system?
What factors do you consider when deciding on an architecture for a new software system?
Approach
- Start from requirements: functional needs, then quality attributes with numbers (latency, throughput, availability, safety, security), then constraints (hardware, existing systems, team skills, budget, regulation).
- Map those to concrete choices: monolith or services, synchronous calls or messaging, where it runs (embedded, on-premises, cloud), and which data store fits the access pattern.
- Separate decisions that are hard to reverse (data model, wire protocols, the language on the device) from cheap ones, spend the analysis on the hard ones, and record them in architecture decision records.
- Include operability: logging and metrics, testability, deployment and the upgrade path for units already installed.
Follow-up
- Give an example where you chose a simpler architecture than the obvious one. Why?
- How do you document an architecture decision so the next engineer understands it?
- Which of your past architecture decisions would you reverse?
Can you explain the trade-offs between different data storage solutions?
Can you explain the trade-offs between different data storage solutions?
Approach
- Relational databases (PostgreSQL, MySQL): ACID transactions, joins and enforced schema; scaling writes beyond one node takes sharding or other extra work.
- Key-value and document stores: flexible schema and horizontal scaling, but weaker joins and ad hoc queries. Many offer tunable or eventual consistency, so say what a stale read costs you.
- Time-series databases such as TimescaleDB or InfluxDB suit append-heavy timestamped sensor data, with compression, retention policies and downsampling built in.
- Object storage is cheap for bulk files such as logs or captured waveforms but has high latency per request. On a device, SQLite or flat files on flash raise write-endurance and power-loss concerns.
- Decide from access pattern, consistency needs, volume, retention and the operational effort your team can carry.
Follow-up
- Where would you store one-second readings from many devices, and how long would you keep full resolution?
- When does a single relational database stop being enough?
- How would you store data safely on a device that can lose power at any moment?
Describe how you would approach integrating a new software solution with existing hardware.
Describe how you would approach integrating a new software solution with existing hardware.
Approach
- Collect the interface facts first: datasheets or interface control documents, the bus or protocol (serial, CAN, Modbus, Ethernet), timing limits, register maps, byte order, error codes and firmware versions. Ask the hardware engineers what the documentation leaves out.
- Put the device behind an adapter or hardware abstraction layer, and write a simulator that implements the same interface so logic runs in unit tests and CI without the device.
- Bring it up step by step: connectivity, read-only polling, then writes with bounded values. Use a bus analyser or logic analyser when behaviour is unclear, and handle timeouts, retries and checksum errors explicitly.
- Define safe behaviour when the device disconnects or sends invalid data, and run hardware-in-the-loop tests before anything reaches the field.
- Handle version differences with a firmware version check at connect time and feature switches per version.
Follow-up
- What do you do when the device behaves differently from its documentation?
- How do you test the integration without access to the physical hardware?
- How do you handle a device that stops responding in the middle of a command?
What is your process for gathering requirements for a system design?
What is your process for gathering requirements for a system design?
Approach
- List the stakeholders (operators, product managers, mechanical and hardware engineers, QA, support), interview them, and watch the current workflow where possible.
- Split functional from non-functional requirements and put numbers on the second group: response time, uptime, data rates, operating environment. Write down constraints and what is out of scope.
- Make every requirement testable with an acceptance criterion, and keep traceability from requirement to test.
- Validate early with a prototype or mock-up, prioritise (for example MoSCoW), keep a list of open questions with owners, and review requirements again as the hardware changes.
Follow-up
- What do you do when two stakeholders' requirements conflict?
- How do you handle a requirement nobody can quantify?
- How do you catch a missing requirement before testing exposes it?
Describe a situation where you had to develop a solution under a tight deadline.
Describe a situation where you had to develop a solution under a tight deadline.
Approach
- State the deadline and the cost of missing it in one sentence, then spend the answer on the scoping decision: what you cut, what you kept, and who agreed to it.
- Name the technical shortcuts you took: reusing existing components, feature flags, building only the path that mattered.
- Show how you protected quality where it counted: tests on the risky path, a review, a rollback plan.
- Close with the outcome in numbers, and the debt you took on and when you paid it back.
Follow-up
- What did you refuse to cut, and why?
- How did you tell stakeholders about the risk?
- What happened to the shortcuts afterwards?
If given a set of performance metrics, how would you analyze them to improve system performance?
If given a set of performance metrics, how would you analyze them to improve system performance?
Approach
- Fix the goal and the right metrics: latency percentiles (p50, p95, p99, not just the mean), throughput, error rate, and utilisation and saturation per resource (the USE method: utilisation, saturation, errors).
- Set a baseline and look at distributions over time. Correlate with load and with events such as deploys, and check the measurement itself, since averages and coarse sampling hide tail problems.
- Find the bottleneck before changing anything: profile with perf, py-spy or flame graphs, and use Little's law (L = λW) to reason about queue length. Amdahl's law bounds the gain from speeding up one part.
- Change one thing at a time and measure under the same workload against the baseline, with enough runs to see past noise.
Follow-up
- Average latency looks fine but users complain. What would you check?
- CPU sits at 40% but throughput has stopped rising. Where would you look?
- How do you stop an optimisation from quietly regressing later?
What steps would you take to evaluate a new technology for your team?
What steps would you take to evaluate a new technology for your team?
Approach
- Start from a problem with measurable pain, then set criteria: fit for the problem, performance, maturity, licence, support lifetime, security, how it integrates with the existing stack, team skills, and total cost including migration.
- Shortlist candidates and run a time-boxed proof of concept on a representative workload, compared with the current solution as a baseline.
- Weigh exit cost and lock-in, which matters most when a product has to be supported for many years.
- Write a decision record with the recommendation, risks and rollout plan (pilot, then wider adoption), and agree in advance what result would stop the trial.
Follow-up
- How would you evaluate a cloud platform for device telemetry?
- What would make you abandon the trial early?
- How do you get the rest of the team to adopt the choice?
Explain how you approach risk assessment in software development.
Explain how you approach risk assessment in software development.
Approach
- Identify risks by type: technical unknowns (new hardware behaviour, new technology), integration, schedule, security and safety.
- Score each one by likelihood and impact in a risk register. For failure modes in hardware-facing software, an FMEA rates severity, occurrence and detection and multiplies them into a risk priority number (RPN = S x O x D).
- Mitigate in priority order: early spikes or prototypes for the unknowns, targeted tests and reviews for risky changes, staged rollouts, feature flags and a tested rollback.
- Give each risk an owner and a trigger that forces a review, and revisit the register at each milestone.
Follow-up
- Give an example of a risk you caught early. What did it save?
- How do you weigh a failure that is unlikely but severe?
- How do you decide a change is safe to release?
Can you demonstrate how to implement a basic REST API?
Can you demonstrate how to implement a basic REST API?
Approach
- Model resources and routes, for example /devices, /devices/{id} and /devices/{id}/readings: GET to list and read, POST to create (201 with a Location header), PUT to replace, PATCH for partial updates, DELETE returning 204.
- Use status codes precisely: 400 for validation, 401 or 403 for auth, 404, 409 for conflicts, 5xx only for server faults. GET, PUT and DELETE are idempotent and POST is not, so support an Idempotency-Key header for retried creates.
- Implement it with a framework you know (Flask or FastAPI in Python, Spring Boot in Java): validate request bodies against a schema, keep handlers thin over a service and storage layer, and return a consistent JSON error body.
- Add list pagination (limit plus cursor) and filtering, authentication with tokens, a versioning scheme (/v1 or a header), request logging, and tests for each endpoint including the error paths.
Follow-up
- How do you handle two clients updating the same resource at once? (ETag with If-Match, 412 Precondition Failed)
- How would you paginate a large, changing list?
- How would you secure and rate-limit the API?
How would you approach debugging a system that intermittently fails?
How would you approach debugging a system that intermittently fails?
Approach
- Characterise it first: how often it fails, under which conditions (load, temperature, specific units, firmware or hardware revision), when it started, and what changed around then.
- Add observability without changing timing too much: timestamped structured logs, counters, a ring buffer dumped when the failure triggers, core dumps. Check the failure detection itself is reliable.
- Reproduce it on purpose with stress tests, fault injection, long soak runs or replayed inputs, then cut variables one at a time; once it reproduces, use git bisect.
- Check the usual intermittent causes: data races on shared state, uninitialised memory, leaked memory or handles, timer or counter overflow, retry and timeout logic, noise on a comms link (watch checksum error counts). ThreadSanitizer, AddressSanitizer and Valgrind find several of these.
- Verify the fix with a regression test and a run long enough to matter: by the rule of three, zero failures in n trials bounds the failure rate below about 3/n at 95% confidence.
Follow-up
- The failure disappears when you add logging. What does that suggest?
- How long must the soak test run before you trust the fix?
- How would you debug it if it only happens on units in the field?
The plan starts from the three reported rounds and the five reported question groups. Early days build the coding and design fundamentals the technical interviews cover, and later days turn them into spoken answers and behavioral stories.
Prepare, practise & reflect
One practical outcome each day. Spend longer where you need it.
0 / 7 done01Map the loop and inventory your experience
- Write the three reported rounds and the five question groups on one page, and mark the group you are weakest in.
- Pick one language from C++, Python or Java for coding, and write a short project summary for every language you will list on your CV.
- Write your answers to the methodology question: what you have used (Agile, Scrum or other) and what you actually did inside it.
Deliverable: A one-page loop map, a chosen coding language, and a written answer for each of the language and methodology questions.
Practice prompt ↗Practice prompt ↗02Core coding: linked lists, data structures and a REST API
- Write iterative and recursive linked-list reversal in your chosen language and test on empty, one-node and two-node lists.
- Solve two data-structure manipulation problems of your choice (for example, merging two sorted lists or detecting a cycle) while speaking your reasoning aloud.
- Build a small REST API with create, read, update and delete routes, input validation and consistent error responses.
Deliverable: A tested reversal function, two solved data-structure problems with spoken-through notes, and a working small REST API.
Practice prompt ↗Practice prompt ↗Practice prompt ↗Practice prompt ↗03Algorithms and search optimization
- Take a linear search over a list of device readings and improve it step by step: sorting plus binary search, a hash index, then a bounded structure for recent values. Write the time and space cost of each.
- Write the story of a complex algorithm you implemented: the problem, the options you considered, why you chose yours and how you tested it.
Deliverable: A table comparing search approaches with complexity and a written project story for the complex-algorithm question.
Practice prompt ↗Practice prompt ↗04System design with a hardware boundary
- Design a software system for a power management application on paper: device readings in, storage and queries, alerting, commands out and offline behavior.
- Compare two or three data storage options for time-stamped readings and for configuration data, and write which you would pick for each and why.
- List the questions you would ask when gathering requirements for this system, and the factors that decide the architecture (load, latency, failure tolerance, maintainability).
- Write an integration plan for a new software component joining existing hardware, covering interfaces, a simulator for testing and a rollback path.
Deliverable: A design sketch with a data-flow diagram, a storage trade-off table, a requirements question list and an integration plan.
Practice prompt ↗Practice prompt ↗Practice prompt ↗Practice prompt ↗Practice prompt ↗05Debugging, performance and risk
- Write a debugging sequence for a system that fails intermittently, with a specific example from your own experience and the evidence that confirmed the cause.
- Take a set of performance metrics (latency percentiles, error rate, CPU) from a past project and write how you would find the bottleneck and verify an improvement.
- Outline how you would evaluate a new technology for a team and how you would assess project risk, with a decision rule and an exit plan.
- Rehearse your tight-deadline story and your code quality and maintainability answer, covering reviews, tests and documentation.
Deliverable: A debugging runbook, a metrics-analysis outline, a technology-evaluation checklist, and written answers for the deadline and code quality questions.
Practice prompt ↗Practice prompt ↗Practice prompt ↗Practice prompt ↗Practice prompt ↗Practice prompt ↗Practice prompt ↗06Behavioral stories
- Write five stories, one each for advocating a technical decision to a non-technical audience, team conflict, leading a project, prioritization, and motivation.
- Mark in each story the decision you made, the alternative you rejected, and the measurable result.
- Say each story aloud and trim the setup until it takes two or three sentences.
Deliverable: Five written stories, each with a named decision, a rejected alternative and a result, rehearsed aloud once.
Practice prompt ↗Practice prompt ↗Practice prompt ↗Practice prompt ↗Practice prompt ↗07Full rehearsal
- Answer aloud in sequence: the language-proficiency question, one design question, one debugging question and two behavioral prompts.
- Code the linked-list reversal and the REST API skeleton again from a blank file, without notes.
- Write your questions for the interviewers and the questions to confirm with the recruiter about format and the number of technical interviews.
Deliverable: A recorded or written run of five spoken answers, two from-scratch code files and a list of questions for the recruiter.
Practice prompt ↗Practice prompt ↗Practice prompt ↗Practice prompt ↗Practice prompt ↗Practice prompt ↗Practice prompt ↗Expand any day for tasks and deliverables. Your progress is saved on this device.
Candidates describe the Behavioral Interviews as the stage that checks alignment with the company's values and culture. The reported prompts concentrate on explaining a technical decision to non-engineers, team conflict, leadership, prioritization and motivation, and the role is described as cross-functional. Prepare stories that show your own decision and a measurable result, ideally including a colleague outside software.
Describe a time when you had to advocate for a technical decision to a non-technical audience.
Describe a time when you had to advocate for a technical decision to a non-technical audience.
Approach
- Choose a decision you owned that the audience had to approve or fund, such as delaying a release to add test coverage, picking one library or data store over another, or retiring a fragile module. Say what the decision was and who the non-technical audience was (a product manager, a customer contact, a production lead).
- Show the translation: replace technical terms with cost, risk, schedule and customer impact. For example, say that a retry mechanism cuts lost readings rather than describing its backoff algorithm. Name the one analogy, number or demo you used.
- State the alternative you rejected and what it would have cost, so the story shows a trade-off rather than a lecture. Include the objection you heard and how you answered it with evidence.
- Close with the result and one measurable signal, such as defects after release, support tickets or schedule impact. Say what you would adjust next time.
- The mistake that sinks this story is a recap of the technical design with a line about explaining it simply. The story has to show the persuasion: what the other person cared about and what changed in their mind.
Follow-up
- What did the audience still disagree with, and how did you handle it?
- How would you explain the same decision to a quality engineer or a mechanical engineer differently?
- Tell me about a time your recommendation was rejected. What did you do next?
How do you handle conflicts within a team?
How do you handle conflicts within a team?
Approach
- Pick a real disagreement about substance (an interface design, a test strategy, who owns a module), not a personality clash. Open with the situation in two sentences and what each side wanted.
- Show how you separated positions from goals: you asked what each person was protecting (release date, safety margin, maintainability) and restated their view until they agreed you had it right. Then say how you turned the argument into something testable, such as a short prototype, a benchmark or a review of past incidents.
- Say how the decision was made and by whom. If you disagreed and committed, say so, and what you did to make the chosen path work.
- End with what happened to the working relationship and the project, and the process change you kept, such as writing a short design note before building.
- The mistake is a story where you were simply right and the other person gave in, or the claim that you have never had a conflict. Both sound like you lack the experience.
Follow-up
- What if the other person had been your manager or a more senior engineer?
- When did you escalate, and when is escalating the wrong move?
- How do you handle a conflict with someone from another discipline, such as quality assurance or product management?
Give an example of a project where you took the lead. What was the outcome?
Give an example of a project where you took the lead. What was the outcome?
Approach
- Pick a project where you set direction, not just carried out tasks. State its goal, its size (people, scope, deadline) and your role in two sentences, and say whether you led with formal authority or without it.
- Walk through how you led: how you broke the work into pieces, how you assigned them and why to those people, how you tracked progress and handled a slipping item, and which decisions only you could make.
- Give the outcome in measurable terms (shipped when, defects, usage, time saved) and include something that went wrong and what you did about it. A story with no problems sounds unreal.
- Say what you learned about leading, and what you would delegate or scope differently.
- The mistake is saying we throughout, leaving the listener unable to tell what you decided. Use I for your choices and credit others by name for theirs.
Follow-up
- How did you handle a team member who was falling behind?
- What did you decide not to build, and who disagreed?
- How did you keep other teams informed as the project changed?
How do you prioritize your tasks when working on multiple projects?
How do you prioritize your tasks when working on multiple projects?
Approach
- Open with the principle in a sentence: you rank work by impact on users or the release, the deadline, and what blocks other people. Then attach it to a real week in which two or three projects collided, for example a production bug against a planned feature.
- Show the mechanics: a written list, an estimate for each item, and a visible commitment. Say what you did with the lowest-value work, whether you deferred it, shrank it or declined it, and who you told.
- Describe a specific moment you renegotiated: you took the trade-off to the stakeholders (a product manager, a team lead) with options and costs instead of silently dropping something. Tell what they chose.
- Close with the result and how you now guard against overload, such as keeping slack for interrupts or batching reviews.
- The mistake is an abstract framework with no example, or an answer that amounts to working longer hours. Neither shows judgment.
Follow-up
- Two stakeholders each say their task is urgent. What do you do?
- How do you handle an urgent bug while mid-way through a feature?
- Tell me about a time you got the prioritization wrong.
What motivates you to perform your best work?
What motivates you to perform your best work?
Approach
- Make the answer specific and true: name two or three things that have driven your best work, such as hard debugging, building something people rely on, or making a system easier for the next engineer. Back each with a project where it showed.
- Connect it to this role without claiming knowledge of the employer. Candidates describe software that works with physical hardware, so if seeing your code control or measure a real device motivates you, give the project where that happened. Otherwise speak honestly about what draws you to software that has to be correct.
- Show that the motivation produces behavior: you took on the flaky test suite, you wrote the runbook, you stayed to finish the integration. Give one outcome.
- Cover how you keep up your effort when the work is routine, such as setting a small improvement goal or learning the surrounding system.
- The mistake is a generic line about passion, or an answer that is only pay or title. Pick motivations that hold up when the follow-up asks for a time they mattered.
Follow-up
- Describe a time you were not motivated by your work. What did you do?
- What kind of work drains your energy, and how do you manage it?
- What would make you leave a role you otherwise liked?
- 01
Describe a time when you had to advocate for a technical decision to a non-technical audience.
- 02
How do you handle conflicts within a team?
- 03
Give an example of a project where you took the lead. What was the outcome?
- 04
How do you prioritize your tasks when working on multiple projects?
- 05
What motivates you to perform your best work?
- 06
Describe a situation where you had to develop a solution under a tight deadline.
How many rounds are there and how long does the process take?
Candidates report three stages over roughly 3-5 weeks: Initial Screening, Technical Interviews (one or more) and Behavioral Interviews. The exact number of technical interviews and the format vary by team and role, so ask your recruiter for the schedule in writing.
PracHub Software Engineer practice ↗Which programming language should I use?
The requirements candidates describe list C++, Python or Java. Use the one in which you can write a clean linked-list reversal and a small REST API without looking anything up, and be ready to discuss a project in every language you list.
PracHub Software Engineer practice ↗Is the coding live or a take-home?
Candidates describe coding as live exercises or take-home assignments. Ask the recruiter which applies to your loop. Prepare for both: practise writing and explaining code at once for the live format, and add tests, input validation and a short README for a take-home.
PracHub Software Engineer practice ↗Do I need power systems or embedded experience?
The requirements candidates describe call experience with power systems or embedded software highly desirable, and list understanding of hardware integration among the must-have skills. If you lack direct experience, prepare examples with device APIs, simulators or sensors, and practise design answers that handle offline devices, bad readings and version differences.
PracHub Software Engineer practice ↗What should I practise for the system design questions?
The reported questions include designing a software system for a power management application, choosing an architecture, comparing data storage options, integrating with existing hardware and gathering requirements. Practise a repeatable outline: requirements, data flow, storage, failure modes and testing, applied to a device-facing system.
PracHub Software Engineer practice ↗Will they ask about cloud, IoT or machine learning?
Candidates describe these as less common and as nice-to-have skills. Prepare a short, honest answer on your cloud experience and one on how machine learning could help power management, such as spotting abnormal readings, without overstating experience.
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.
- 01Avtron Power Solutions 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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