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Alexander Technology Group Software Engineer Interview Guide

Alexander Technology Group Software Engineer interview guide: four reported rounds, coding, debugging and behavioral questions, and a 7-day plan.

The 60-second brief

Candidates describe the Software Engineer role at Alexander Technology Group as building, testing and maintaining software applications. Reported project types run from enterprise applications to cloud and automation work, and the work is done alongside product management, operations and QA. The stack candidates list is Java, C#, Python or JavaScript, with React, Angular or .NET on the framework side and AWS or Azure for cloud. The topic tags attached to this loop are Continuous Integration (CI), Power Platform Development, Build Automation, Systems Engineering and general Software Engineering.

This guide is for engineers with roughly two to five years of experience, which is the range candidates report for the role. It covers the four reported rounds, the twenty questions candidates report, and a seven-day plan. Technical preparation centres on language fluency, linked-list, search and sort coding, debugging, performance, and system and feature design. The behavioral half covers the prioritization, feedback, communication and teamwork stories the reported questions call for.

Candidates report four rounds over about 3-5 weeks: Initial Screening, Technical Assessment, Behavioral Interview and Final Round. The reported questions are broad software-engineering questions rather than company-specific puzzles.

Focus areas

Continuous Integration (CI)
Power Platform Development
Software Engineering

Overview

The reported question set is general. It asks you to name and defend the languages you use, walk through a complex project you worked on, debug a hard issue, compare object-oriented and functional programming, reverse a linked list, implement binary search and give the complexity of what you wrote. Nothing in it depends on inside knowledge of the company, so the winning move is depth on fundamentals plus stories you can tell with specifics.

The role itself, as candidates describe it, spans designing, building and testing applications, taking part in code reviews, refactoring existing software and working with product, operations and QA. That maps directly onto the question mix. Code review and refactoring explain why debugging, performance and feature-design questions appear. Working with adjacent teams explains the feedback, communication and prioritization questions.

The stack matters for how you phrase answers. Candidates list Java, C#, Python and JavaScript, frameworks such as React, Angular and .NET, and AWS or Azure. Pick the one language you can write cleanly under pressure, and keep examples from your own projects in the stack you actually used. The topic tags Continuous Integration, Build Automation and Power Platform Development suggest being ready to explain how your code gets built, tested and shipped. If Power Platform is new to you, say so plainly and point to the nearest automation work you have done instead of bluffing.

Several reported questions are about your own work: languages used, a complex project, a time you handled a challenge, how you handle edge cases. These reward a prepared example with a decision and a result. Build a small bank of five or six stories and map each to several questions, so one story about a regression you tracked down can serve the debugging, challenge and edge-case prompts.

The Alexander Technology Group interview process

Candidates describe four stages, from Initial Screening to a Final Round. The format of each stage is only lightly reported, so confirm it with your recruiter as you go.

Initial Screening

Candidates describe the Initial Screening as a first pass on basic qualifications and fit. The reported material gives no format, so treat it as a conversation where your background has to be easy to follow. The language-experience question and the complex-project question are the natural material for this stage, since both are about your own track record. Ask the recruiter what the next round will look like before you hang up.

What to demonstrate

Whether your background matches the reported requirements: a major language, agile experience and cloud familiarity
How clearly you describe your own projects and the part you personally built
Basic fit for the role as candidates describe it

How to prepare

Write a short walk-through of your career with the languages and frameworks (Java, C#, Python or JavaScript; React, Angular or .NET) you can genuinely defend
Prepare a two-sentence version of your most complex project that names your own contribution, the stack and one outcome
Decide your answers on location, remote preference and availability, then ask the recruiter for the format of the Technical Assessment

Technical Assessment

Candidates report the Technical Assessment as the stage where technical proficiency is demonstrated. The reported coding questions are the linked-list reversal, binary search, complexity of your own code and a sorting implementation. The debugging, performance, object-oriented versus functional and design questions also sit under the technical heading. Because the delivery format is not reported (live coding, take-home or discussion), prepare to write code unaided and to explain it out loud.

What to demonstrate

Working code for the reported algorithm tasks: linked list, binary search and a sort
Time and space complexity stated correctly for what you wrote
Debugging and performance reasoning, such as how you isolate a problem and prove a fix
Comfort with your main language and with Git and CI workflows

How to prepare

Write iterative and recursive linked-list reversal, binary search with correct bounds and one comparison sort from memory, then state the complexity of each
Rehearse a debugging sequence aloud: reproduce, narrow, form a hypothesis, test it, add a regression test
Prepare a clear comparison of object-oriented and functional styles with one concrete example in your own language
Practise a design outline for a high-volume transaction system, covering queueing, idempotency, partitioning and failure handling

Behavioral Interview

The Behavioral Interview is reported as the stage that tests how you work with people. The reported prompts cover a significant project challenge, prioritizing several projects, contributing to team success, giving and receiving feedback and communicating within a team. Every one of these needs a specific story with your own decision in it. Keep each story short, state the result, and say what you would change.

What to demonstrate

Whether your examples show your own decisions and results
Collaboration habits with teammates and adjacent teams such as product management, operations and QA
How you handle feedback, prioritization and communication
Self-awareness about what you would do differently

How to prepare

Write five stories in situation, decision, result form: a hard project challenge, a prioritization call, a team win, a feedback exchange and a communication failure you fixed
Quantify at least two results with something checkable, such as builds that stopped failing, a release that shipped on schedule or a defect class that disappeared
Rehearse each story aloud until the situation takes only a few sentences and most of the answer is your reasoning

Final Round

The Final Round is described as the stage that settles overall fit and readiness for the role. The format is not reported, so prepare for both technical follow-through on earlier topics and further story-based questions. The reported prioritization and team-success prompts are good rehearsal material. Bring your own questions about the team, the stack and how work is reviewed and released.

What to demonstrate

Overall fit and readiness for the role as candidates describe the stage
Consistency between what you said in earlier rounds and what you say now
Whether your questions show you understand the work: code review, refactoring and cross-team delivery

How to prepare

Re-read your notes from each earlier round and prepare to extend any technical answer you gave, especially the debugging and design ones
Keep your prioritization and team-success stories ready with updated detail
Write four questions about the team's release process, code review habits, CI setup and how engineers work with QA and operations

Where candidates lose points

Naming languages and frameworks you cannot code in on the spot

List only the languages you could write a linked-list reversal in right now. The language question opens the loop, and every later technical answer will be judged in that language, so choose one primary and be honest about the rest.

Writing binary search or list reversal that fails on an empty, one-element or off-by-one case

Practise writing both from scratch until the loop invariants are automatic. Before you say you are done, run the empty input, one element, target at each end and target missing, and fix the midpoint overflow case.

Giving a complexity answer for code you have not traced, or confusing the two sort bounds

Count the loops and recursion depth in your own code and say the time and the extra space separately. Know that merge sort is O(n log n) with O(n) extra space, quicksort has an O(n^2) worst case, and insertion sort is fast on nearly sorted data.

Debugging answers that jump straight to a guess instead of a sequence

State the order: reproduce the issue, find what changed, narrow with logs or git bisect, form a testable hypothesis, fix it and add a regression test. For performance questions, add measuring with a profiler before changing code.

Behavioral stories with no decision of your own, no result or no lesson

Build each story around one choice you made, the alternative you rejected and an outcome you can check. Prepare one story that shows a mistake you owned, because several reported questions invite one.

Questions & practice

Write a function to reverse a linked list.

Medium · Coding / Algorithms

Approach

Iterative three-pointer solution: prev = null, curr = head; while curr is not null, save next = curr.next, set curr.next = prev, move prev = curr and curr = next; return prev as the new head.
Complexity: O(n) time and O(1) extra space, since each node's pointer is rewritten exactly once in place.
Recursive alternative: reverse the rest of the list, then set head.next.next = head and head.next = null; still O(n) time but O(n) call-stack space, which can overflow on long lists (CPython's default recursion limit is 1000).
Edge cases to test aloud: empty list, single node, two nodes, and confirm the old head's next is null so no cycle is left behind.
Mention the doubly linked variant: swap each node's prev and next pointers, and the new head is the last node visited.

Follow-up

Reverse the nodes in groups of k.
Reverse only the sublist between positions m and n in one pass.
How would you check the list has no cycle before reversing it?

How would you implement a binary search algorithm?

Medium · Coding / Algorithms

Approach

State the precondition: the array is sorted (or, more generally, a predicate is monotonic over the search range).
Iterative version: lo = 0, hi = n - 1; while lo <= hi, compute mid = lo + (hi - lo) / 2 to avoid integer overflow in Java or C#, return mid on a match, otherwise set lo = mid + 1 or hi = mid - 1; return -1 when the loop ends.
Complexity: O(log n) time and O(1) space iteratively; a recursive version uses O(log n) stack.
For duplicates, write the lower-bound variant on a half-open range [lo, hi): while lo < hi, if a[mid] < target then lo = mid + 1 else hi = mid; lo is the first index not less than target. Know the library versions: Python bisect_left, Java Arrays.binarySearch and C# Array.BinarySearch, the last two returning a negative value encoding the insertion point when the key is absent.
Edge cases: empty array, one element, target below the minimum or above the maximum, and all elements equal; an off-by-one in the loop condition or the hi update causes infinite loops.

Follow-up

Find the first and last position of a target in a sorted array with duplicates.
Search for a target in a sorted array that has been rotated.
Use binary search on the answer: find the minimum ship capacity that delivers all packages within D days.

What are the time and space complexities of your code?

Medium · Coding / Algorithms

Approach

Time: count how many times the innermost work runs as a function of input size, and say what each iteration costs; nested loops over the same input give O(n^2), a halving loop gives O(log n).
Call out hidden costs: list.pop(0) and membership tests on a Python list are O(n) while set lookups are O(1) on average; repeated string concatenation in a Java loop copies the string each time, so use StringBuilder.
For recursion, write the recurrence: T(n) = 2T(n/2) + O(n) solves to O(n log n) by the master theorem, and naive Fibonacci is exponential until memoised to O(n).
Space: count auxiliary structures and the recursion stack, say whether you exclude the input and output, and note amortised costs, such as dynamic-array append being amortised O(1).
Distinguish average from worst case: hash map operations are O(1) expected but degrade to O(n) (or O(log n) with Java 8 treeified buckets) under heavy collisions.

Follow-up

What is the space complexity of recursive DFS on a completely skewed tree?
Why is hash table lookup only O(1) on average?
Can you trade extra memory for faster time in the solution you just wrote?

Provide an example of a sorting algorithm you have implemented.

Medium · Coding / Algorithms

Approach

Pick one you can write from memory and state its properties. Merge sort: O(n log n) in every case, stable, O(n) extra space. Quicksort: average O(n log n), worst O(n^2) with bad pivots, in place with expected O(log n) stack, not stable.
Explain how you avoided quicksort's worst case: random or median-of-three pivot, three-way partitioning for many duplicates, and recursing into the smaller side first to bound the stack.
Know when simpler sorts win: insertion sort is O(n^2) in general but O(n) on nearly sorted input and is used for small subarrays; counting or radix sort run in O(n + k) for integers in a small range.
Know the library behaviour in your language: Python's sort is Timsort (stable); Java sorts primitives with dual-pivot quicksort and objects with stable TimSort; C# Array.Sort is an unstable introsort while LINQ OrderBy is stable; JavaScript Array.prototype.sort has been required to be stable since ES2019.
Test on empty input, one element, duplicates, already sorted and reverse sorted arrays.

Follow-up

Why does stability matter? Give a case where an unstable sort produces the wrong result.
How would you sort a file too large to fit in memory?
How does introsort guarantee O(n log n) in the worst case?

Hold a per-tenant active cap against concurrent creates

Hard · write skew · isolation levels · constraints

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?

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

Start from what is actually unique: not (tenant_id, email), but (tenant_id, email) among live rows. PostgreSQL says that directly — CREATE UNIQUE INDEX app_user_live_email ON app_user (tenant_id, email) WHERE deleted_at IS NULL. A full constraint over the same two columns burns the address permanently the first time someone deletes an account.
Keep case-insensitivity in the type or the index, never in the application: CITEXT as given, or UNIQUE (tenant_id, lower(email)) as an expression index where the extension is unavailable. A case-sensitive unique column is exactly how two accounts for one human appear.
For MySQL 8 the predicate has to move inside the key: add a discriminator column that is a constant 0 while the row is live and is set to user_id on delete, with UNIQUE (tenant_id, email, deleted_marker). Live rows share the constant and still collide; deleted rows differ from each other and stop colliding.
State the NULL variant and its dependency: leaving the marker NULL for deleted rows also works, because a unique index treats NULLs as distinct — true in MySQL, and true in PostgreSQL only under the default NULLS DISTINCT, which PostgreSQL 15 lets you reverse. Check the polarity against the three existing deleted rows: constant-on-live is what preserves the collision you want, and reversing it silently admits duplicate live accounts.

Follow-up

A deleted account re-registers with the same address the next day. Do the old resource rows follow the new user_id, and how does the API keep the two principals apart?
How do you honour an erasure request while resource_revision.actor_user_id still references this table?

What programming languages are you proficient in, and how have you applied them in past projects?

Medium · Technical / Domain Questions

Approach

Lead with one primary language and rank the others honestly (daily use, comfortable, read-only), so every later claim can be checked against that ranking.
For the primary language, tie one project to a concrete language feature you relied on: C# async/await for I/O-bound calls, Java streams and generics, Python type hints with a checker such as mypy, or JavaScript promises on the event loop.
Show runtime knowledge, not just syntax: in standard CPython the GIL keeps CPU-bound threads from running Python bytecode in parallel, so I used multiprocessing; Java generics are erased at runtime while C# generics are reified.
Name one weakness of your main language and how you worked around it in that project, for example null handling in Java solved with Optional at API boundaries.
Close with how you pick up a new language fast: port a small known program, read the standard library collections and concurrency docs, then run its linter and test framework.

Follow-up

What is one thing your primary language makes harder than it should be?
How does memory management work in the language you use most?
If the team works in a language you have not used, how would you be productive in your first two weeks?

Describe a complex software project you worked on and your specific contributions.

Medium · Technical / Domain Questions

Approach

Open with the system in two sentences: what it did, its scale (users, requests, data volume) and the main components, ideally with a quick box-and-arrow sketch.
Separate team work from yours explicitly: say "I designed" or "I implemented" only for the parts you owned, and name them (a service, a migration, a CI pipeline, a module).
Walk through one hard technical decision in detail: the options you weighed, the constraint that decided it, and the trade-off you accepted.
Give a measured outcome (latency, error rate, build time, release frequency) and how you verified it, not just that it shipped.
End with what you would change now and why; it shows you understood the system's weak points.

Follow-up

Which alternative did you reject, and what would have happened if you had chosen it?
What broke after launch, and how did you find out?
If load grew tenfold, which component would fail first?

Explain the differences between object-oriented and functional programming.

Medium · Technical / Domain Questions

Approach

Define both cleanly: OOP models a program as objects that bundle state with behaviour, using encapsulation, inheritance and polymorphic method dispatch; FP builds it from pure functions over immutable data, with functions as first-class values combined by composition and higher-order functions such as map, filter and reduce.
Explain the practical consequence: pure functions return the same output for the same input with no side effects, so they are easy to test and safe to run concurrently; mutable shared object state needs locking or careful ownership.
State the extensibility trade-off (the expression problem): in classic OOP adding a new type is easy but adding a new operation touches every class; with algebraic data types and pattern matching adding an operation is easy but adding a new variant touches every function.
Note that mainstream languages mix both: Java lambdas and streams, C# LINQ and records, Python comprehensions and functools, JavaScript array methods and closures. Show a loop with an accumulator rewritten as filter plus reduce.
Say when you choose each: OOP for long-lived stateful entities with clear boundaries, FP style for data transformations, pipelines and concurrency-heavy code.

Follow-up

Is a function that writes a log line pure? Why or why not?
When does immutability cost performance, and how do persistent data structures help?
How would you model an order's status transitions in each style?

How would you approach designing a system to handle millions of transactions per day?

Medium · Problem-Solving / Case Studies

Approach

Size it first: a day has 86,400 seconds, so 1 million transactions a day is about 12 per second on average and 10 million about 116; assume a peak multiple (say 10x) and design for that, which a single well-indexed relational database can usually handle.
Core write path: stateless API instances behind a load balancer, a relational database with ACID transactions as the source of truth, and an append-only ledger (double-entry rows) rather than updating balances in place.
Make writes safe to retry: require an idempotency key per request and store it with a unique constraint, so a client retry after a timeout cannot create a second transaction.
Move non-critical work (notifications, reporting, downstream sync) to a message queue such as SQS, Azure Service Bus or Kafka; use the transactional outbox pattern so the database commit and the event cannot diverge, and make consumers idempotent because delivery is at-least-once.
Scale reads with replicas or a cache, partition by account or tenant when one database is no longer enough, and add monitoring on latency, error rate, queue depth and a daily reconciliation job.

Follow-up

A client times out and retries a payment. How does your design prevent a double charge?
One account receives a large share of all traffic. How do you handle the hot partition?
How do you reconcile your ledger with an external payment provider's records?

Given a specific problem in software performance, how would you identify and rectify the issue?

Medium · Problem-Solving / Case Studies

Approach

Pin the symptom down with numbers: which operation, p50 versus p95/p99 latency, throughput, CPU, memory, and when it started; compare against a known-good baseline.
Measure before changing anything: use distributed traces or APM to see where the time goes (database, external calls, CPU, garbage collection, lock waits), then a profiler on the hot path such as py-spy, Java Flight Recorder, dotnet-trace or browser DevTools.
Check the usual suspects in order of likelihood: N+1 queries, a missing index (confirm with the query plan, e.g. EXPLAIN ANALYZE in PostgreSQL), quadratic loops over growing data, chatty network calls that could be batched, and memory growth causing long GC pauses.
Fix the single biggest contributor, re-run the same measurement under the same load, and keep the change only if the numbers moved.
Prevent regressions: add a benchmark or load test to CI and an alert on the latency percentile that degraded.

Follow-up

Average latency is fine but p99 is bad. What do you suspect first?
Memory climbs slowly over several days. How do you find the leak?
How do you confirm the fix in production without a risky big-bang release?

Describe a situation where you had to think outside the box to solve a technical problem.

Medium · Problem-Solving / Case Studies

Approach

Pick a technical story where the obvious fix was ruled out by a real constraint (no budget for new infrastructure, a legacy system you could not change, a deadline, a vendor limit) and state that constraint first.
Lay out the options you considered and why the conventional one failed, so the unusual choice reads as reasoning rather than novelty.
Describe the approach concretely in the first person: what you built or changed, and how you de-risked it (a prototype, a benchmark, a feature flag, a small pilot).
Give the measured result and the trade-off you accepted, such as added complexity or a dependency to maintain.
Avoid the two common failures: a story where the creative idea was someone else's, and a clever solution with no evidence it worked.

Follow-up

Why did the standard approach not work in that situation?
What risk did your solution introduce, and how did you contain it?
Would you make the same choice today, knowing what you know now?

How would you design a new feature for an existing software product?

Medium · Problem-Solving / Case Studies

Approach

Start from the user problem and a success metric (adoption, task completion time, error rate), and agree on the smallest version that tests it.
Map the feature onto the existing system: which services, data model and UI components change, and which existing flows must keep working unchanged.
Make changes backward compatible: additive API fields or a new version, and expand-and-contract schema migrations (add a nullable column, backfill, switch reads, then enforce and remove old paths) so deploys need no downtime.
Ship behind a feature flag with a gradual rollout and a kill switch; cover it with unit and integration tests plus regression tests on the flows it touches, run in the CI pipeline.
Define what you monitor after launch (the success metric, errors, latency) and the condition under which you roll back or iterate.

Follow-up

How do you roll out a database schema change without downtime?
The feature flag has been on for six months. What is the cleanup plan?
The metric you chose drops after launch. What do you do next?

What factors do you consider when optimizing code for performance?

Medium · Problem-Solving / Case Studies

Approach

Measure first and optimise the proven bottleneck; set a target (for example a p95 latency) so you know when to stop.
Algorithmic complexity usually dominates: replacing a nested-loop lookup with a hash map turns O(n^2) into O(n) expected time, and sorting once then scanning beats repeated searches.
I/O and network round trips: batch queries, avoid N+1 patterns, add the right index, pool connections, and cache results that are read often and change rarely, with a clear invalidation rule.
Memory and allocation: reduce object churn to cut garbage collection pressure, prefer contiguous data for cache locality, and stream large inputs instead of loading them whole.
Concurrency and limits: use async for I/O-bound work and parallelism for CPU-bound work, but Amdahl's law caps speedup at 1 / ((1 - p) + p / n) for parallel fraction p on n cores; weigh every gain against readability and correctness.

Follow-up

When is an optimisation not worth making?
How would you choose a cache eviction policy and a TTL?
Work through Amdahl's law: 90% of the work parallelises and you have 8 cores. What speedup can you expect?

How do you approach debugging a challenging issue in your code?

Medium · Technical / Domain Questions

Approach

Reproduce first: turn the report into a minimal, reliable repro, ideally a failing automated test, so you can tell when the bug is fixed.
Read the evidence before theorising: full stack trace, logs around the timestamp, and what changed (recent commits, dependency versions, config, input data, environment).
Narrow the search space by halving it: git bisect (mark a good and a bad commit, optionally git bisect run with a test script) finds the breaking commit in about log2(n) steps; do the same over input size or code path.
Form one hypothesis at a time and test it with a breakpoint, a targeted log line or an assertion; for intermittent bugs suspect races, timeouts and shared state, and add thread IDs and timestamps to logs.
Fix the root cause rather than the symptom, keep the failing test as a regression test, and check whether the same pattern exists elsewhere in the codebase.

Follow-up

The bug only happens in production and you cannot reproduce it locally. What do you do?
How would you debug a test that fails intermittently in CI?
How do you know your fix addressed the cause and not a coincidence?

Suggested practice checklist

The plan moves from your language and project story, through the reported coding, debugging and design questions, to the behavioral stories and a full rehearsal. Each day is tied to reported questions from this loop's own question set.

Day 1 — Map the four rounds and pick your language

Write the four reported rounds in order and note the format you still need to confirm with the recruiter for each.
Choose one primary language from Java, C#, Python and JavaScript, and list the frameworks and cloud platform you can discuss with a real project behind each.
Write a short account of your most complex project: your own contribution, the stack and the outcome.

Deliverable: A one-page loop map, a chosen primary language, and a written project summary for the language and complex-project questions.

Day 2 — Linked lists and binary search

Write the linked-list reversal iteratively and recursively, and trace both on an empty list and a one-node list.
Write binary search with a safe midpoint, then the lower-bound variant that returns the first matching index.
State time and space complexity aloud for each version, including recursion stack depth.

Deliverable: Four working functions (two reversals, two searches) with their complexities written beside them.

Day 3 — Sorting and edge cases

Implement merge sort and insertion sort in your primary language, then explain when quicksort's worst case matters and what stability means.
Make a table of your edge-case checklist and run it against every function from days 2 and 3.
Write the edge-case story: one bug or near-miss, what it broke and what regression test you added.

Deliverable: Two sort implementations with a complexity and stability table, an edge-case test list, and one written edge-case story.

Day 4 — Debugging and performance

Write your debugging sequence as numbered steps and apply it to a real bug from your own history, naming how you narrowed it.
For a slow endpoint or loop you have worked on, write how you measured it, what the bottleneck was and what you changed.
List the factors you weigh when optimizing: algorithmic complexity, I/O and queries, caching, memory and the cost of added complexity.

Deliverable: A numbered debugging procedure, one performance case study with before and after evidence, and an optimization-factors list.

Day 5 — Design and programming paradigms

Outline a design for a system handling millions of transactions a day: ingestion queue, idempotent writes, partitioning, retries and failure handling.
Outline how you would add a new feature to an existing product: requirements with product management, an interface that does not break callers, a rollout plan and tests in CI.
Write a comparison of object-oriented and functional programming with one example of each in your main language, and a story of an unconventional technical solution.

Deliverable: Two design outlines, a one-page paradigm comparison with code examples, and one written story about an unconventional solution.

Day 6 — Core behavioral stories

Write the Git experience answer: your branching model, the CI checks on merge, and one recovery story.
Write the significant-challenge story in situation, decision, result form, and rehearse it aloud.
Write the prioritization story with two competing demands, what you deferred and how you communicated it, and the team-success story with a before-and-after result.

Deliverable: Four written and rehearsed answers: Git, project challenge, prioritization and team success.

Day 7 — Feedback, communication and a full run-through

Write your feedback story covering both receiving and giving, and your communication story with the mechanism you adopted.
Run a full mock: language and project introduction, one coding question, one debugging question and two behavioral answers, all aloud.
Write four questions for the Final Round about the team's release process, code review and how engineers work with QA and operations.

Deliverable: Two written stories for feedback and communication, a recorded or self-reviewed mock run with notes, and a list of four closing questions.

Behavioral preparation for the Alexander Technology Group loop

The reported behavioral prompts cover project challenges, prioritization, teamwork, feedback and communication, which matches a role that works with product management, operations and QA. Prepare specific stories with your own decision and a result, and reuse one story across several prompts.

What is your experience with version control systems, particularly Git?

Medium · Technical / Domain Questions

Approach

Answer with what you have done, not a command list. Name the branching model you used (short-lived feature branches off main, or trunk-based), how pull requests were reviewed and which checks had to pass in CI before a merge.
Show fluency through two or three real situations: rebase versus merge for keeping history readable, squash for a messy branch, cherry-pick for a hotfix and git bisect to find the commit that introduced a regression.
Tell one recovery story. For example, a bad merge reached main, you used git revert (with -m 1 for a merge commit) instead of rewriting shared history, and you found a lost commit through git reflog.
Say what you changed afterwards: branch protection, smaller pull requests, required status checks or a clearer conflict-resolution habit. Avoid casual claims about force-pushing, and say that you only rewrite your own unshared branch, preferring --force-with-lease.

Follow-up

When do you rebase and when do you merge, and what does each do to history?
A commit with a bug is already on the shared main branch. How do you undo it safely?
How would you find which commit broke a test that passed last week?

Describe a time when you faced a significant challenge in a project. How did you handle it?

Medium · Behavioral / Leadership

Approach

Choose a challenge where you made the key decision, and open with the situation in two or three sentences: the project, what went wrong or was unclear, and what was at stake.
Spend most of the answer on the options you weighed and why you chose one. Name the trade-off, such as rewriting a module versus patching it, or cutting scope versus moving the date.
Give the action as steps you personally took and the result with something checkable: a defect rate, a release that shipped or a service that stopped failing.
End with the lesson and one thing you would do differently. The answer fails when the challenge is just a tight deadline with no decision, or when it turns into blaming a teammate.

Follow-up

What alternative did you consider and reject, and why?
How did you know the fix worked, and what evidence did you have?
What would you do differently if the same situation came up again?

How do you prioritize tasks when working on multiple projects?

Medium · Behavioral / Leadership

Approach

State a method first, then prove it with a real case. A workable method is to list everything, rank by impact, deadline and cost of delay, mark dependencies and blockers, and re-rank when something changes.
Use a concrete example with two competing demands, for instance a production defect against a planned feature. Say what you deferred, who you told and what you offered in return, such as a new date or a smaller scope.
Describe how you involve others: ask product management which item matters more, tell QA or operations what will slip and write the decision down so it does not get re-litigated.
Include how you protect focus, such as batching interruptions or reserving time for the high-impact item. Avoid answering with a framework name and no example, or claiming you handle everything at once.

Follow-up

Two managers each say their task is the top priority. What do you do?
How do you handle a production issue that interrupts planned work?
Tell me about something you decided not to do, and how you said so.

Can you give an example of how you have contributed to team success?

Medium · Behavioral / Leadership

Approach

Pick a contribution that changed the team's outcome, not just your own tickets. Good examples are fixing a flaky build that was slowing everyone down, introducing a review checklist, writing onboarding notes or unblocking a teammate on a hard bug.
State the before and after with a number or an observable change, such as fewer broken builds, shorter review turnaround or a release that no longer needed manual steps.
Be exact about your part and the team's part. Say 'I wrote the pipeline change, and two teammates adopted it and extended it', which sounds more credible than claiming sole credit.
Show that you noticed the problem yourself rather than being assigned it. The answer fails when it is a generic claim of being a team player with no event behind it.

Follow-up

What would have happened if you had not stepped in?
What did your teammates contribute, and how did you work with them?
How did you measure the improvement?

What is your approach to receiving and providing feedback?

Medium · Behavioral / Leadership

Approach

Cover both halves. For receiving, tell one real case, such as a code review comment you disagreed with at first. Explain how you checked whether the reviewer was right and what you changed afterwards.
For giving, describe your habits in code review: comment on the code and not the person, give the reason behind the suggestion, mark what is a blocker and what is a preference, and offer an alternative.
Add one example of feedback you gave that was hard to hear, such as a pull request that was too large or a missed test, and say how it landed and what followed.
Say how you ask for feedback early, for instance by sharing a design outline before writing code. The answer fails when it says you welcome all feedback and gives no instance.

Follow-up

What do you do when you disagree with a reviewer's comment?
How would you give feedback to a colleague who gets defensive?
What is a habit you changed because of feedback you received?

How do you ensure effective communication within your team?

Medium · Behavioral / Leadership

Approach

Name concrete mechanisms rather than values: pull request descriptions that say what changed and why, short written design notes before big changes, regular status updates and a clear place where decisions are recorded.
Show how you adapt to the audience. Engineers get implementation detail, while product management, operations and QA get impact, risk and dates, which fits a role that works with those adjacent teams.
Tell one story where communication failed, such as a requirement misunderstood or a handoff that dropped a detail. Explain what it cost and what process or habit you changed afterwards.
Cover how you deliver bad news: early, with the facts, with options and with your recommendation. Avoid answers that are only about being friendly or approachable.

Follow-up

How do you keep teammates informed when some work in other time zones or work asynchronously?
How do you handle a colleague who does not respond to messages you need answered?
How have you told a stakeholder that a deadline will slip?

How do you handle edge cases in your code?

Medium · Coding / Algorithms

Approach

Describe a repeatable checklist you run on every function: empty or null input, a single element, duplicates, already sorted or reverse sorted data, boundary values, overflow, invalid input and, where relevant, concurrent access.
Make it concrete with the tasks in this loop. Reversing a linked list needs the empty list and the one-node list. Binary search needs an empty array, a target absent from either end and an overflow-safe midpoint such as lo + (hi - lo) / 2.
Explain how you turn edge cases into tests before or alongside the code, and how you validate input at system boundaries so the core logic can assume clean data.
Tell a short story of an edge case that reached production or was caught in review, what it broke and what you changed afterwards, such as a regression test or a stricter parser.

Follow-up

How do you decide which edge cases are worth handling and which to reject as invalid input?
Tell me about an edge case that slipped through to production.
How do you test for edge cases you have not thought of?