Interview guides · Activision

Activision Software Engineer Interview Guide

Activision Software Engineer interview guide: the 5 reported rounds, C++ memory, graph coding, ad-platform and pipeline design, with a 7-day plan.

The 60-second brief

Activision publishes games, Call of Duty among them, through studios such as Infinity Ward, and it also runs a media and advertising division. Candidates describe Software Engineer openings across very different parts of that business: engine, rendering and gameplay code in C++; backend advertising and monetization services and data pipelines in Java or Python, per the requirements candidates describe; build and CI/CD systems that package games for PlayStation, Xbox and Windows; mobile SDKs; and cloud services on AWS or Google Cloud Platform with Docker and Kubernetes. Candidates say the questions vary by studio, team and level, so work out early whether you are interviewing for a studio role or a backend/data role.

This guide is for engineers preparing for an Activision Software Engineer loop, from mid-level to staff. It covers the five reported stages, gives question-specific answers for the twenty reported questions (domain knowledge, coding, system design and behavioral), and lists the stack candidates describe: C++ for engine and gameplay work, Java and Python for backend and data work, Kafka, Spark or Flink for pipelines, Bigtable or Cassandra for high-throughput storage, Jenkins or Gradle for builds, and HLSL for graphics roles. Use the studio-specific material (rendering, 3D math, CI/CD for consoles) if your posting is a game-team role and the distributed-systems material if it is an advertising or data role.

Candidates report five stages over roughly 4-6 weeks: a Recruiter Screen, a Technical Screen (live coding or a remote assessment), a Virtual Onsite Loop of 3 to 5 interviews, Technical Assessments covering system design and advanced coding, and Behavioral Interviews about past projects.

Focus areas

Security Governance, Risk, & Compliance (GRC)
System Design
Information Security Controls

Overview

The reported questions split into four groups. Domain questions ask how C++ manages memory, what you have done with multi-threading and performance tuning, how you would debug a rendering pipeline, how you would build user data collection and tracking, and which games you have been playing and how you read their technical implementation. Coding questions cover a tree or graph traversal, optimizing a slow algorithm, a data structure or file and HTTP handling inside a mobile SDK, an automation script in Python or C++, and a take-home on systems integration or backend service logic. Design questions cover a monetization backend at billions of requests per day, a Kafka or Spark pipeline, NoSQL schema design for Bigtable or Cassandra, caching and latency, and CI/CD for multi-platform game deployment.

That spread means preparation has two layers. The shared layer is data structures and algorithms, concurrency and memory, and being able to explain performance in terms of measurement and data layout rather than intuition. The track layer differs: for a studio role, rehearse 3D math, frame budgets, GPU debugging, asset streaming and console build pipelines; for a backend or advertising role, rehearse throughput estimates, partitioning, delivery semantics in event pipelines, query-first NoSQL schemas and containerised services that scale for a launch.

Candidates also report questions about the games they play and why they want to work in games, and the behavioral stage is described as peers, the hiring manager and cross-functional partners talking through past projects. Prepare those answers with the same rigour as the technical ones: a game you can analyse system by system, and project stories that involve art, design or QA rather than engineers only.

The Activision interview process

These are the five stages candidates describe. The Virtual Onsite Loop is reported to mix technical and behavioral interviews; ask your recruiter how your own loop is grouped and in what order.

Recruiter Screen

Candidates describe the Recruiter Screen as a conversation about their background, their interest in the company and how they match the specific opening. Because candidates describe openings across engine, gameplay, backend advertising, data, build and mobile SDK work, use this call to pin down which of those the role is, which studio or division it sits in, and whether the next step is a live technical screen or a take-home. Have a short account of your experience that points at that track, and a one-sentence answer for why this team.

What to demonstrate

How closely your background matches the specific opening
Your interest in Activision and the specific team
Basic qualifications for the opening

How to prepare

Write a short walk-through of your CV that ends on the work most similar to the posting (C++ engine work, Java/Python services, pipelines or build systems).
Ask which studio or division the role is in, which language the technical screen uses, and whether a take-home replaces the live screen.
Ask about the level, location and work arrangement of the opening so you can prepare at the right depth.

Technical Screen

Candidates report a live coding session or a remote assessment centred on algorithms and data structures for backend roles, and say some teams use a take-home coding test at this point instead. Language-specific topics are also reported at the technical stage, such as C++ memory management or Java concurrency. Practise writing complete, compiling code in the language of the posting, talking through complexity as you go, and testing on small inputs including empty and cyclic cases.

What to demonstrate

Working code for an algorithms and data structures problem
Complexity analysis and edge-case handling
Fluency in the posting's language, including memory or concurrency details

How to prepare

Solve tree and graph traversal problems both recursively and iteratively, including cycle detection and topological order on a dependency graph.
Rehearse a short explanation of stack versus heap, RAII and smart pointer ownership in C++, or of thread pools and concurrent collections in Java.
If you get a take-home, plan a README of assumptions and a test suite before writing the service logic.

Virtual Onsite Loop

Candidates describe the Virtual Onsite Loop as 3 to 5 interviews mixing deep technical sessions with behavioral ones, held with engineers, hiring managers and cross-functional partners. The reported formats are live coding, system design aimed at gaming or advertising infrastructure, and behavioral conversations. Treat it as several distinct sessions: have a coding warm-up routine, a design framework you can apply to either a backend or a build/streaming problem, and a set of project stories ready.

What to demonstrate

Live coding under interview conditions
System design for gaming or advertising-scale infrastructure
How you communicate with engineers and non-engineering partners

How to prepare

Run one full mock day: a coding problem, a design problem (the monetization platform or the telemetry pipeline) and two behavioral stories back to back.
Prepare a design template: requirements, a back-of-envelope estimate, data model, read and write paths, failure modes and the trade-off you chose.
Have one game-domain example ready (a rendering bug, a frame-time regression or a build failure you traced) even for a backend role.

Technical Assessments

Candidates report that this stage includes system design and advanced coding evaluations; no specific questions are reported for it. Practise for it with the reported design and coding questions described in the overview, treating them as material to rehearse rather than a list of what this stage asks. Bring designs with numbers attached, and code you can then optimize for both time and memory.

What to demonstrate

A system design problem worked through to components, data stores and trade-offs
Advanced coding that produces working, efficient code

How to prepare

Practise the reported monetization-platform design end to end: request rate from 'billions per day', serving path, event logging through Kafka, budget pacing and billing correctness.
Practise NoSQL modelling: design a Cassandra or Bigtable schema from three stated queries and explain how the key avoids hot partitions.
Take a slow O(n^2) solution of your own and rewrite it to O(n) or O(n log n), then reduce its allocations and explain the cache behaviour.

Behavioral Interviews

Candidates describe interviews with potential peers, the hiring manager and cross-functional partners that focus on past projects, and they also report behavioral interviews inside the Virtual Onsite Loop; ask your recruiter whether this stage is separate in your loop. Prepare a small set of projects you can walk through in depth: the problem, the decision you made, the trade-off and a measurable result, and prepare the technical detail behind each project you describe. The behavioral section below lists the behavioral questions candidates report, to practise these stories against.

What to demonstrate

How you explain past projects and the decisions you made in them
How you work with peers, a hiring manager and cross-functional partners

How to prepare

Pick three or four projects and write a STAR story for each, with a number in the result, then rehearse them against the reported behavioral questions in the behavioral section.
For every story, prepare the technical deep-dive: architecture, the hardest bug and what you would change now.
Prepare two questions for each interviewer type: peers (code review, build times), hiring manager (roadmap), partners (how requests reach engineering).

Where candidates lose points

Answering the games question with enthusiasm only, without any technical reading of how the game works.

Before your first interview, pick one recent game and prepare two systems you can analyse: what you observed (rubber-banding, pop-in, hitches, long loads), the likely mechanism, how you would confirm it and the trade-off behind it. Tie it to the track you are applying for.

Reducing the C++ memory question to 'stack is fast, heap is slow' with nothing on ownership, RAII or allocation cost.

Rehearse a structured answer: storage durations, RAII, unique_ptr versus shared_ptr and its control block, vector reallocation and when it moves versus copies, cache-friendly layout, and pool or arena allocators. Be ready to write a small pool allocator.

Writing a recursive traversal for the graph question with no visited set and no thought for cycles or deep inputs.

Practise BFS, DFS and topological sort on dependency graphs until cycle detection (three-colour DFS or Kahn's count check) is automatic, use an explicit stack for deep inputs, and test empty, single-node, self-loop and disconnected cases out loud.

Designing the monetization platform or the Kafka pipeline without numbers, delivery semantics or a failure story.

Open every design with a request-rate estimate (one billion a day is about 11.6k per second on average), state at-least-once versus exactly-once and how you deduplicate, separate the latency-sensitive serving path from billing correctness, and walk through a region or dependency outage.

Telling cross-functional stories that only involve other engineers, or that blame art, design or QA.

For the behavioral conversations, prepare at least two stories where an artist, designer or QA engineer changed what you built, with the constraint you translated, the trade-off you agreed and a measured outcome.

Questions & practice

Can you explain how memory storage and management work in C++?

Medium · Technical and Domain Knowledge

Approach

Lay out storage durations: automatic (stack, freed at scope exit), dynamic (heap via new/delete or operator new, lifetime you manage), static/thread-local; note the stack is fast and bounded, the heap is flexible but costs an allocator call and can fragment.
Make ownership explicit with RAII: resources released in destructors, std::unique_ptr for sole ownership (zero overhead), std::shared_ptr for shared ownership (control block, atomic reference counts, cycles need std::weak_ptr), rule of zero/five, and move semantics to transfer instead of copy.
Cover layout and performance: alignment, padding, cache lines, contiguous containers beating node-based ones; std::vector growth reallocates and invalidates iterators and references, and it moves elements if the move constructor is noexcept or the type cannot be copied, otherwise it copies them to keep the strong exception guarantee (std::move_if_noexcept).
Mention custom allocation used in performance-sensitive code: arena/linear and per-frame allocators, fixed-size pools, and why they cut fragmentation and allocator cost.
Name the failure classes and tools: leaks, dangling pointers, double free, use-after-free, buffer overruns; AddressSanitizer, Valgrind and leak-tracking allocators.

Follow-up

Write a fixed-size pool allocator: what is its allocate and free complexity?
Why is copying a shared_ptr more expensive than moving one?
What happens to references into a vector after push_back exceeds capacity?

What experience do you have with multi-threading, asynchronous processing, and performance tuning?

Medium · Technical and Domain Knowledge

Approach

Answer with one concrete project: the workload, why it was slow or blocking, and the concurrency model you chose (a thread pool or job system for CPU-bound work, async I/O or futures for I/O-bound work).
Name the primitives and why: mutexes and condition variables, atomics, a lock-free or bounded queue between producer and consumer; in Java, ExecutorService, CompletableFuture and ConcurrentHashMap; in C++, std::thread, std::atomic and memory ordering.
Show the hazards you handled: data races, deadlock avoided by consistent lock ordering, false sharing fixed by padding to a cache line, priority inversion, and contention found under a profiler.
Quantify the tuning: what you measured (throughput, p99 latency, frame time, CPU utilisation), the before and after, and why the speedup stopped where it did (Amdahl's law, a serial section, memory bandwidth).

Follow-up

How did you find the bottleneck before changing anything?
How would you detect and diagnose a deadlock that only happens in production?
When does making code asynchronous make it slower?

Write code to solve a tree or graph traversal problem under time constraints.

Medium · Coding and Algorithms

Approach

Clarify the graph: directed or undirected, weighted or not, size, possible cycles and disconnected parts; build an adjacency list.
Pick the traversal for the question: BFS with a queue for shortest paths in unweighted graphs or level order, DFS for reachability, components, cycle detection and topological order, Dijkstra with a heap when edges have weights.
Track visited nodes (a set or array) and state complexity: O(V + E) time and O(V) space for BFS/DFS, O((V + E) log V) for Dijkstra with a binary heap.
For a dependency or asset build graph, detect cycles with three-colour DFS (white, grey, black; a grey neighbour means a cycle) or Kahn's algorithm (if fewer than V nodes are output, a cycle exists).
Prefer an explicit stack over recursion for deep trees to avoid stack overflow, and test empty input, a single node, self-loops and duplicate edges.

Follow-up

Return the actual cycle, not just whether one exists.
Group the build graph into levels that can compile in parallel.
The graph does not fit in memory: how does the traversal change?

How would you optimize an inefficient algorithm for runtime performance and memory usage?

Medium · Coding and Algorithms

Approach

Measure before changing anything: profile to find the hotspot and build a benchmark with realistic input sizes.
Fix the algorithm first: a nested-loop O(n^2) search becomes O(n) with a hash map or O(n log n) with sorting plus two pointers; cache repeated subproblems; precompute what is reused.
Then constant factors and memory: contiguous arrays over linked nodes, struct-of-arrays for hot loops, no allocation inside the loop (reserve, object pools), pass by reference or move instead of copy, fewer unpredictable branches.
For memory usage: stream input instead of loading it all, work in place, use smaller types or bitsets; state the time versus memory trade-off you are making.
Verify the optimised version gives identical output on the same inputs and report the before-and-after numbers.

Follow-up

The memory budget rules out the hash map: what do you do instead?
How do you know the speedup is real and not noise in the benchmark?
When would you leave the slow version alone?

Can you implement a specific data structure or handle file I/O and HTTP requests in a mobile SDK context?

Medium · Coding and Algorithms

Approach

If asked for a data structure, a common SDK one is an LRU cache: hash map plus doubly linked list for O(1) get and put; or a bounded ring buffer for queued events. Make it thread-safe if callbacks arrive on several threads.
File I/O: never on the main thread; write to a temporary file and rename so a crash cannot leave a half-written file; cap the on-disk size and drop oldest data; detect and skip corrupt records.
HTTP: batch requests, set connect and read timeouts, retry only timeouts and 5xx with exponential backoff and jitter, never blind-retry 4xx, honour 429 and Retry-After, use idempotency keys so retries cannot double-count.
Respect the host app: small binary and no heavy dependencies, react to network state and battery, deliver callbacks on the expected thread, and never crash the game on an SDK error.

Follow-up

The app is killed in the middle of a write: what is on disk when it restarts?
The server returns 429 for every request for ten minutes: what does the SDK do?
How do you test the networking code without a live network?

Write a clean, maintainable script in Python or C++ to automate a build or data processing task.

Medium · Coding and Algorithms

Approach

Structure it as small pure functions plus a thin main: argument parsing with argparse, pathlib for paths, logging instead of print, and meaningful exit codes so a CI job can fail on it.
For a build task, call tools with subprocess.run(check=True) and capture output; make steps idempotent, skip work whose input hashes have not changed, and parallelise independent steps with concurrent.futures.
For a data task, stream rows (generators, the csv module) instead of loading everything, validate each record, and report or quarantine malformed rows rather than crash.
Add a dry-run flag and configuration from arguments or a file instead of hard-coded paths, and unit-test the pure functions with pytest.

Follow-up

How would you run this in Jenkins and surface failures?
Make the script resumable after it fails halfway through.
What breaks when it runs on Windows instead of Linux?

Write the update path that detects a concurrent edit

Medium · optimistic concurrency · transactional outbox · audit history

resource carries version INT NOT NULL DEFAULT 1. resource_revision holds revision_id, resource_id, version, actor_user_id, change_kind, patch JSONB, request_id, created_at with UNIQUE (resource_id, version). outbox_event holds aggregate_type, aggregate_id, aggregate_version, event_type, payload, status. A PUT carries the version the client read. Write the exact statements for the single transaction that applies the edit, records the revision and enqueues 'resource.updated', and give the handler's branch on zero affected rows. Then say what PostgreSQL 16 does under READ COMMITTED when two of these updates hit one row at once.

Approach

One transaction, three writes, no network call inside it: UPDATE resource SET title = $3, version = version + 1, updated_at = now() WHERE resource_id = $1 AND tenant_id = $4 AND version = $2; then INSERT the resource_revision row at version $2 + 1; then INSERT the outbox_event row at the same aggregate_version. The event goes to a table rather than a broker because no transaction spans both.
Branch on the affected-row count before doing anything else. Zero has three causes — stale version, wrong tenant, row gone — so re-read once and map to 409 carrying the current version, or 404 for an id outside the caller's tenant, which also stops the endpoint confirming that another tenant's id exists.
State the engine behaviour instead of assuming it. Under READ COMMITTED the second UPDATE blocks on the row lock, and when the first commits PostgreSQL re-evaluates the WHERE clause against the newly committed row, so the version predicate now fails and the statement reports zero rows. Under REPEATABLE READ the identical collision raises SQLSTATE 40001 instead, so the handler must fold both shapes into one conflict response.
Keep UNIQUE (resource_id, version) even though the predicate already serialises writers. It is what makes a lost update unwritable if any other path ever reaches the revision table, and it converts a logic bug into 23505 rather than into a silently missing history row.

Follow-up

A client sends the version it read ten minutes ago and the resource has moved three versions. What is in your 409 so it can resolve the conflict without a full re-fetch?
Two editors, two disjoint fields, no overlap. Does your answer still refuse the second write, and should it?

Denormalise tenant onto revisions and backfill it live

Hard · denormalisation · online migration · backfill

resource_revision (revision_id, resource_id, version, actor_user_id, change_kind, patch, request_id, created_at) has 400M rows and no tenant column; tenant_id lives only on resource. Two reads need it: a tenant-scoped audit feed ordered by created_at DESC, and an offboarding purge. Both join back to resource today. Justify adding tenant_id to resource_revision against those two reads, name the anomaly the copy introduces and the constraint that prevents it, then give the ordered migration for a live table taking 1.2k writes/second — the lock each step takes, how the backfill is batched, and where each step stops being reversible. PostgreSQL 16.

Approach

Justify from the access path rather than from taste. Without the column, the audit feed either scans resource_revision by created_at and discards other tenants' rows, or resolves the tenant's resource_ids first and probes with them — both proportional to the tenant's whole history rather than to one page. With (tenant_id, created_at DESC, revision_id DESC) it is a seek that stops at 50 rows, and the purge becomes a ranged delete instead of a join.
Name the cost exactly: a second copy of a fact can disagree with the first. Make the disagreement unwritable rather than documented — add UNIQUE (resource_id, tenant_id) on resource so it can serve as a foreign-key target, then FOREIGN KEY (resource_id, tenant_id) REFERENCES resource (resource_id, tenant_id) on the revision table. A revision can then only ever carry its parent's tenant.
Step one, expand: ALTER TABLE resource_revision ADD COLUMN tenant_id BIGINT NULL, with no default, so it is a catalogue change and no rewrite. It still needs ACCESS EXCLUSIVE for an instant, and that instant queues behind the longest open transaction on the table while every later query queues behind it — set lock_timeout to 2s and retry rather than wait.
Step two, dual-write: deploy the writer that populates tenant_id on every new revision while reads still use the join. Reversible by redeploying the previous build, because nothing reads the column yet.

Follow-up

The backfill is half finished and a rollback is required. What state is the table in, and what does the previous build do with a half-populated column?
How do you verify the backfill actually finished, given rows are still being inserted while it runs?

What games have you been playing recently, and how do you analyze their technical implementation?

Medium · Technical and Domain Knowledge

Approach

Pick one or two games you have genuinely played in the last few months and choose a specific system to analyse rather than reviewing the whole game: netcode, streaming and load times, frame pacing, matchmaking, the store or the tools behind live content.
Reason from observation to mechanism: rubber-banding suggests client-side prediction being corrected by server reconciliation; texture pop-in suggests a streaming budget or LOD distances; hitches in dense scenes suggest CPU-bound draw submission or shader compilation.
Say how you would confirm it: a frame-time graph, a GPU frame capture, network conditioning to reproduce lag, or watching memory over a long session.
Finish with one trade-off the developers likely made (tick rate versus server cost, streaming quality versus memory budget) and what you would try, tied to the track you are applying for.

Follow-up

How would you implement the hit registration or lag compensation you described?
What metric would you put on a dashboard to catch that problem in production?
If you had one sprint on that game, what would you change and what would it cost?

How would you approach designing a scalable system for user data collection and tracking?

Medium · Technical and Domain Knowledge

Approach

Clarify what is tracked (gameplay telemetry, ad impressions, purchases), the event volume and peak, who consumes it (real-time dashboards versus batch analytics) and the privacy constraints: consent, retention and deletion.
Client side: the SDK batches events with a unique event id, buffers them on disk, compresses, and retries with exponential backoff so a crash or offline period does not lose data.
Ingestion: stateless collectors behind a load balancer validate against a versioned schema and write to Kafka partitioned by user or device id, which keeps per-user order; downstream a stream job (Flink or Spark) computes real-time aggregates and a batch path lands raw data in partitioned files for analytics.
Serving: per-user lookups go to Bigtable or Cassandra keyed by user id plus time; state the delivery choice (at-least-once with deduplication on event id is the usual answer) and why exactly-once end to end is more expensive.
Privacy and operations: pseudonymise identifiers early, enforce retention with TTLs, support user deletion across the stores, and monitor consumer lag and drop rates.

Follow-up

How do you handle events that arrive hours late or out of order?
A user asks for their data to be deleted: which stores does that touch and how do you prove it is done?
One title produces ten times more events at launch: what saturates first?

Complete a take-home coding assessment focused on systems integration or backend service logic.

Medium · Coding and Algorithms

Approach

Read the spec twice and write the assumptions you made into a README before writing code; scope the core path so it is complete and tested before any extras.
Separate layers: request handling, service logic, and a client for each external system behind an interface so it can be replaced by a fake in tests.
Handle integration failure explicitly: timeouts, bounded retries with backoff, idempotency for operations that may be retried, and a clear mapping from downstream errors to your own responses.
Test the logic with unit tests and the integration points with fakes or a local container; give one-command run instructions (Docker if your stack uses it).
Close the README with what you left out and what you would add for production: metrics, structured logs, configuration, load behaviour.

Follow-up

What changes if this service has to handle a hundred times the load?
What would you add before running it in production?
Which part of your solution are you least happy with, and why?

How would you design a high-volume backend monetization platform capable of handling billions of requests dail

Medium · System Design and Architecture

How would you design a high-volume backend monetization platform capable of handling billions of requests daily?

Approach

Clarify what 'monetization' means here: ad serving, in-game purchases, or both. They need different guarantees: ad decisions need low latency and tolerate approximation, purchases need strong consistency.
Estimate: one billion requests per day is about 11.6k per second on average; assume a peak several times higher and say the number you design for.
Serving path: load balancer and a stateless decision service scaled horizontally; campaign and budget data keyed by campaign id is small enough to cache in full in-process and refresh periodically, while user segments live in Bigtable or Cassandra keyed by user id; a strict latency budget with a default response when a dependency times out.
Event path: impressions and clicks go to Kafka with unique event ids; a stream job updates budget pacing near real time, a batch Spark job reconciles billing; deduplicate on event id so billing does not double count.
Purchases go through a separate path with idempotency keys and a transactional ledger in a relational database; cover multi-region deployment and what happens when a region fails.

Follow-up

How do you stop a campaign overspending its budget when several regions serve it?
How do you detect and discard duplicate or fraudulent clicks?
A region goes down during a major launch: what happens to in-flight requests and budgets?

Can you walk through the architecture of a distributed data pipeline using tools like Kafka or Spark?

Medium · System Design and Architecture

Approach

Ingest: producers write to Kafka topics partitioned by a key that needs ordering (user, match, device); replication factor 3, acks=all and min.insync.replicas=2, since without that setting acks=all can be met by a single in-sync replica; schemas in Avro or Protobuf with a registry so producers and consumers evolve independently.
Process: Spark Structured Streaming or Flink store the Kafka offsets in their own checkpoints alongside operator state instead of relying on consumer-group commits, so recovery and replay resume from offsets consistent with that state; event-time windows with watermarks handle late data; failed records go to a dead-letter topic.
Store: raw data to a data lake in Parquet partitioned by date, aggregates to a warehouse for analytics and to a NoSQL store for low-latency serving.
Semantics: at-least-once plus idempotent sinks is the default; Kafka transactions with transactional sinks give exactly-once at higher cost; reprocessing by replaying offsets or a batch Spark job over the lake.
Operate it: alert on consumer lag, throughput and error rate, and compact small files in the lake.

Follow-up

Consumer lag keeps growing: how do you diagnose and fix it?
A producer ships a breaking schema change: what stops it from taking down consumers?
How do you backfill a week of data after fixing a bug in the transform?

How do you structure schemas and data modeling for high-throughput NoSQL databases like Bigtable or Cassandra?

Medium · System Design and Architecture

Approach

Design from the queries: list the access patterns first, then build one table (Cassandra) or row-key layout (Bigtable) per pattern, denormalising instead of joining.
Cassandra: the partition key spreads data across nodes and clustering columns sort rows inside a partition; bound partition size by bucketing, for example (player_id, day); choose consistency levels such as QUORUM for reads and writes when you need read-your-writes.
Bigtable: one sorted row key, so a key that starts with a timestamp sends every new write to one tablet; fix that with field promotion (lead with player_id), a small salt prefix or a hashed leading field. A reversed timestamp after that prefix (player_id#reverse_ts) only gives newest-first scans; on its own it is still monotonic and still hotspots. Group columns into families by access pattern.
Plan lifecycle and pitfalls: TTL for retention, tombstones from heavy deletes in Cassandra, secondary indexes being costly at scale, tall narrow tables for time series, and how a new query will need a new table plus a backfill.

Follow-up

Product wants a new query the schema does not support: what do you do?
One key (a popular player or event) gets most of the traffic: how do you spread it?
How do you store and expire per-match telemetry for 90 days?

What strategies do you use for caching and reducing latency in cloud-based environments?

Medium · System Design and Architecture

Approach

Start from where latency comes from: network round trips, slow dependencies, cold data, serial fan-out. Measure p50 and p99 per hop before adding a cache.
Layer caches: CDN or edge for static and semi-static content, an in-process LRU for hot small data, a distributed cache for shared data; use cache-aside by default, write-through when reads must see recent writes.
Invalidation and expiry: TTLs with jitter so keys do not expire together, explicit invalidation on writes for data that must be fresh, and never cache data such as entitlements or balances where staleness is unacceptable without a check.
Protect the origin: request coalescing or a lock per key against cache stampedes, early probabilistic refresh, replicating hot keys, and consistent hashing so losing a cache node moves only a fraction of keys.
Other latency levers: keep services and data in the same region, connection pooling, parallel fan-out with timeouts, and precomputing expensive results.

Follow-up

The cache is cold at launch and every request hits the database: what do you do?
How do you keep cached purchase or entitlement data correct?
A cache node fails: what happens to hit rate and to the database?

How do you architect an end-to-end CI/CD pipeline to support multi-platform game deployment?

Medium · System Design and Architecture

Approach

Triggers and gating: pre-submit builds and fast tests on every change, fuller post-submit builds, and nightly builds with long test suites; a broken main build blocks further merges until fixed.
Build matrix: one pipeline producing PlayStation, Xbox and Windows targets on dedicated agents (for example Jenkins), with pinned toolchain and SDK versions so builds are reproducible.
Speed: incremental and distributed compilation, a shared cache of compiled outputs, and a separate asset cooking stage keyed by content hash so unchanged assets are reused.
Stages after compile: unit tests, packaging per platform, automated smoke tests on target hardware, then promotion to QA with versioned, retained artifacts (game builds are large, so set retention rules).
Health: build-time and failure dashboards, quarantine for flaky tests, notifications to the author of a breaking change, and a quick way to roll QA back to the last good build.

Follow-up

Full builds take far too long: where do you look first?
Only one platform's build fails: how do you isolate it?
How do you keep flaky automated tests from blocking everyone?

How do you approach debugging complex graphical features or rendering pipelines?

Medium · Technical and Domain Knowledge

Approach

Get a deterministic repro first: fixed camera, seed and content, a save or replay, and note which platforms, GPUs or drivers show it.
Take a GPU frame capture and walk the draw calls: pipeline state, bound textures and buffers, shader inputs and outputs, render target formats; visualise intermediates such as depth, normals and overdraw.
Bisect: toggle render passes and features, swap content versus code, and bisect builds to the change that introduced it; turn on graphics API validation or debug layers.
Check common causes: uninitialised or wrongly transitioned resources, missing synchronisation between CPU writes and GPU reads, depth precision and z-fighting, sRGB/linear mismatches, HLSL compiling differently per platform.
For performance bugs, first decide CPU-bound versus GPU-bound with timing, then look at draw call count, shader cost or bandwidth.

Follow-up

The bug only reproduces on one console: what is different and how do you narrow it?
It only appears after a long play session: what do you suspect?
The glitch is intermittent and you cannot capture it on demand: what do you add?

Suggested practice checklist

Each day targets one cluster of the reported questions, from track choice and language fundamentals through coding and design to behavioral stories and a full mock.

Day 1 — Pick your track and prepare your game and motivation answers

Read the posting and decide whether it is a studio (C++ engine, gameplay, rendering, build) or backend/data (Java, Python, pipelines) role; list the stack items it names.
Write a short CV walk-through that ends on your most relevant project.
Choose one recent game and write a technical analysis of two of its systems, with how you would verify each guess.
Write your answer to why tools or gameplay development, with one piece of evidence.

Deliverable: A track decision with its stack list, a CV walk-through script, a two-system game analysis and a written answer on why tools or gameplay.

Day 2 — C++ or Java memory and concurrency fundamentals

Write out the C++ memory answer: storage durations, RAII, smart pointer ownership, vector growth and move-versus-copy, layout and cache lines.
Implement a fixed-size pool allocator and a thread-safe bounded queue in C++, and test both.
Backend track: implement the bounded queue with BlockingQueue and an ExecutorService, and write how the Java memory model's happens-before rules (volatile, synchronized) apply to it and how GC pauses affect latency.
Write your multi-threading story with the primitives used, the hazard you handled and before-and-after numbers.

Deliverable: A one-page memory outline, a tested pool allocator and bounded queue (or the Java BlockingQueue version with a memory-model and GC note), and a quantified concurrency story.

Day 3 — Traversal, optimization and studio 3D math

Solve four graph problems in your interview language: BFS shortest path, connected components, cycle detection in a build dependency graph, and topological order grouped by level.
Take one O(n^2) solution and rewrite it to O(n) or O(n log n), then remove allocations from its hot loop and benchmark both.
Studio track: implement quaternion slerp and a view-frustum test against bounding spheres, and outline a memory budget for streaming assets in an open world.
Say the complexity and edge cases aloud for each solution.

Deliverable: Four tested traversal solutions, a before-and-after benchmark of one optimized algorithm, and (studio track) tested slerp and frustum-culling code with a streaming budget outline.

Day 4 — SDK code, automation scripts and the take-home

Implement an LRU cache and an event uploader that writes to disk atomically and retries HTTP with backoff and jitter.
Write a Python build or data script with argparse, logging, exit codes, a dry-run flag and pytest tests.
Outline a take-home README template: assumptions, design, how to run, tests, what you left out.

Deliverable: A tested LRU cache and uploader, a tested automation script, and a reusable take-home README template.

Day 5 — Backend design: monetization, tracking, caching and containers

Design the monetization platform end to end with a throughput estimate, serving path, event path, budget pacing and failure handling.
Design the user data collection and tracking system, including the client SDK, Kafka partitioning, deduplication and user deletion.
Write a caching plan for one of those systems covering layers, invalidation, stampede protection and node loss.
Package a small service (the tracking event collector works) in a Docker container, then write how Kubernetes would absorb a launch-day surge: autoscaling targets, readiness probes that keep cold pods out of rotation, and pre-scaling before the launch.

Deliverable: Two design diagrams with written trade-offs, a caching plan for one of them, and a containerised service with a written autoscaling and readiness-probe plan.

Day 6 — Pipelines, NoSQL schemas, CI/CD and rendering debugging

Draw a Kafka plus Spark or Flink pipeline with schemas, replication settings, checkpointed offsets, windows, sinks, delivery semantics and a backfill plan.
Design a Cassandra or Bigtable schema from three stated queries and show how the key avoids hot partitions.
Outline a multi-platform CI/CD pipeline for PlayStation, Xbox and Windows, including asset caching and QA promotion.
Write a step-by-step debugging sequence for a rendering bug that only appears on one platform.

Deliverable: A pipeline diagram, a NoSQL schema with key rationale, a CI/CD outline and a rendering debugging checklist.

Day 7 — Behavioral stories and a full mock

Write STAR stories for cross-functional shipping, disagreement with senior stakeholders, shifting priorities and mentoring, each with a number in the result.
Prepare the technical deep-dive behind each story: architecture, hardest bug, what you would change.
Run a mock sequence: one coding problem, one design problem and two behavioral stories back to back, then note the weakest answer.

Deliverable: Four STAR stories with deep-dive notes and a list of weak spots from the mock to revisit.

Behavioral and past-project preparation

Candidates describe behavioral interviews with potential peers, the hiring manager and cross-functional partners centred on past projects, report behavioral sessions inside the Virtual Onsite Loop as well, and say they are asked about the games they play. Prepare stories where non-engineers (artists, designers, QA) shaped the work, where you made and defended a technical decision, and where a deadline forced a scoping call, each with a measurable result and the technical detail to go deeper.

What games have you been playing recently, and how do you analyze their technical implementation?

Why do you want to work specifically in tools or gameplay development within the gaming industry?

Medium · Behavioral and Team Collaboration

Approach

Choose honestly between tools and gameplay and name it; a vague 'anything in games' answer is weaker than a clear preference.
Back it with evidence: a mod, a game jam, an engine or tools project, or a tool that saved designers or artists time, with what you built and what it changed.
Show you understand the work: tools engineering means your users are artists and designers and iteration speed is the metric; gameplay engineering means feel, responsiveness and staying within the frame budget.
Connect it to the team you are interviewing with. The mistake that sinks this answer is fandom alone with no engineering content.

Follow-up

Tell me about a tool you built: who used it and how did you get their feedback?
What makes a gameplay system hard to tune, and how would you make it easier?
What would make you prefer the other track?

Tell me about a time you had to collaborate across multi-functional teams like art, design, and QA to ship a f

Medium · Behavioral and Team Collaboration

Tell me about a time you had to collaborate across multi-functional teams like art, design, and QA to ship a feature.

Approach

Use STAR, keep the situation to two sentences, and pick a feature where at least two non-engineering disciplines shaped the outcome.
Show translation work: turning an art or design request into technical constraints (memory budget, frame time, data format), building something that let designers tune values without code changes, or giving QA reproducible steps and test builds.
Name one real conflict, such as scope versus quality or an asset over budget, and the trade-off you negotiated, with your part in it.
End with a measurable result (shipped on the milestone, crash or bug count, iteration time) and what you would do differently. Blaming another discipline sinks the story.

Follow-up

What did the designers or artists need that you did not expect?
How did you handle a request you could not deliver within budget?
How did QA's feedback change your implementation?

How do you handle disagreements on technical direction or architecture with senior stakeholders?

Medium · Behavioral and Team Collaboration

Approach

Pick a real architecture disagreement with someone more senior, where the stakes were clear (performance, maintenance, deadline).
Show how you made it about evidence: a prototype, a benchmark, or a short written comparison of options with costs and risks.
Describe the outcome honestly: you persuaded them, they persuaded you, or you disagreed and committed; say what you learned either way.
Avoid two failure shapes: a story where you simply won by being right, and one where you gave in without making your case.

Follow-up

What would have changed your mind?
How did you keep the working relationship intact afterwards?
Looking back, who was right?

Describe a situation where you had to manage shifting priorities and deliver under a tight deadline.

Medium · Behavioral and Team Collaboration

Approach

Set the context in one or two sentences: what changed, how late, and what the fixed date was (a milestone, a launch, a release).
Show the re-plan: listed the work, separated must-ship from nice-to-have, cut or deferred scope explicitly and agreed it with the owner, and told stakeholders early.
Show how you protected quality on what did ship: tests or extra QA on the risky parts, a feature flag, a rollback path.
Give the result and a lesson. Relying on overtime as the whole answer is the common mistake.

Follow-up

What did you cut, and who agreed to it?
What went wrong despite the re-plan?
How would you spot the slip earlier next time?

How do you approach mentoring junior engineers and promoting code health within your team?

Medium · Behavioral and Team Collaboration

Approach

Tell one specific mentoring story: who the engineer was, what they struggled with, what you did (pairing, scoped tasks, review feedback that explained the why), and how their work changed.
Then name team-level practices you drove: review standards, tests on new code, static analysis or linters (for example clang-tidy in a C++ codebase), shared conventions, and paying down debt in planned increments.
Quantify where you can: fewer regressions, faster reviews, a junior engineer owning a feature on their own.
Avoid an answer made only of principles with no person or result in it.

Follow-up

Tell me about a mentee who did not improve: what did you do?
How do you give review feedback on code from someone more senior?
How do you justify time spent on code health against feature work?