This is the kind of question where you can spend 45 minutes and still feel like you only scratched the surface.
Start by clarifying requirements and scale, then design a high-level architecture that separates live and on-demand pipelines but shares ad decisioning and delivery components. Focus on the ad insertion workflow, including targeting, real-time bidding, and seamless playback, while addressing trade-offs like latency, scalability, and fault tolerance.
Pro tip: Emphasize how you would handle ad insertion without disrupting the user experience, such as using client-side ad insertion (CSAI) for on-demand and server-side ad insertion (SSAI) for live, and discuss how to measure and optimize ad performance.
Ask questions to understand expected traffic (concurrent viewers, peak load), content types (live vs. VOD), ad formats (pre-roll, mid-roll, post-roll), targeting capabilities, and latency requirements. Establish non-functional requirements like availability, scalability, and cost.
Outline the main components: content ingestion and transcoding, ad decisioning service, ad server, manifest manipulation, and client players. Explain how live and VOD pipelines differ and where they converge (e.g., ad decisioning, analytics).
Detail the process from ad request to playback: when an ad break is triggered, the player or server requests ads from the ad decisioning service, which selects ads based on targeting and bidding. Then, the ad is stitched into the stream (SSAI) or played by the client (CSAI).
Discuss how to scale each component (e.g., using CDNs for ad delivery, sharding for ad decisioning, caching for targeting data) and ensure fault tolerance (e.g., fallback to direct ad serving, graceful degradation).
Compare SSAI vs. CSAI, discuss latency implications for live, ad personalization vs. privacy, and how to handle ad blockers. Mention monitoring and A/B testing for continuous improvement.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
I knew latency would come up but the 'at the edge' constraint threw me a bit.
Start by clarifying the requirements and constraints, then propose a high-level architecture that pushes decision-making to the edge. Focus on latency budget allocation, caching, and lightweight models, and discuss trade-offs between accuracy and speed.
Pro tip: Emphasize that sub-100ms includes network time, so the decision must happen as close to the user as possible—ideally on-device or at the edge PoP. Mention that you'd measure and optimize the p99 latency, not just the average.
Ask about scale, user distribution, ad types, and what 'edge' means (CDN, PoP, on-device). Confirm the latency budget and whether it's end-to-end or just decision time.
Propose a multi-tiered system: on-device caching for frequent ads, edge servers for real-time bidding and lightweight ranking, and a central system for model training and updates.
Allocate the 100ms across network, computation, and data fetching. Aim for <20ms for decision logic, leaving room for network and rendering.
Discuss techniques like pre-fetching, caching, model quantization, feature pre-computation, and using in-memory databases at the edge.
Acknowledge trade-offs between accuracy and latency, and describe how you'd monitor performance and iterate (e.g., A/B testing, canary deployments).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Went with active-active across regions with fallback to a default ad slate if the decision engine is unreachable.
Start by clarifying the requirements: low-latency ad serving, high availability, and consistency trade-offs. Then outline a multi-region active-active architecture with fault isolation, data replication, and failover mechanisms, emphasizing trade-offs like latency vs. consistency and cost. Conclude with monitoring and testing strategies to ensure resilience.
Pro tip: Tie your answer to Snapchat's scale and real-time bidding needs—mention specific techniques like cell-based architecture and asynchronous replication to show you understand their unique challenges.
Ask about SLAs (e.g., 99.99% availability), latency targets, data consistency needs, and budget. This shows you prioritize business goals over generic solutions.
Propose a cell-based or sharded architecture where failures are contained within regions or cells, preventing cascading failures. Use health checks and circuit breakers to isolate unhealthy components.
Describe active-active or active-passive setups with asynchronous/synchronous replication for ad data and user profiles. Detail DNS-based or anycast routing for failover, and how to handle data consistency during region failover.
Discuss trade-offs: latency vs. consistency (e.g., eventual consistency for ad metrics), cost of cross-region traffic, and complexity of conflict resolution. Mention strategies like CRDTs or last-write-wins for ad counters.
Outline monitoring (e.g., per-region latency, error rates), automated failover triggers, and chaos engineering practices to validate fault tolerance. Include post-mortems and continuous improvement.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
RTB I was comfortable with, talked through the bid request/response cycle and timeout handling.
Start by clarifying requirements and scale, then outline a high-level architecture that separates the real-time bidding (RTB) exchange integration from ad podding logic. Walk through the key components: bid request handling, ad pod construction, auction mechanics, and response formatting, while addressing latency, reliability, and compliance. Conclude with trade-offs and monitoring considerations.
Pro tip: Emphasize the need for low-latency, high-throughput systems and how you'd handle failures gracefully—Snapchat values engineers who can balance performance with reliability at scale. Mention specific protocols like OpenRTB and ad podding standards (e.g., VAST) to show domain expertise.
Ask about expected QPS, latency SLAs, ad pod duration, and exchange protocols (e.g., OpenRTB). Confirm whether the system is for buying or selling ads and the types of ads (video, display).
Outline components: an RTB gateway to handle bid requests/responses, an ad pod builder to assemble multiple ads into a pod, an auction engine to select winning bids, and a response formatter to return ads in the required format (e.g., VAST).
Explain how to construct ad pods: define pod duration, number of ads, and constraints (e.g., competitive separation). Describe how to fill pods with bids, handle unfilled slots, and ensure smooth playback.
Discuss bid request parsing, sending bid requests to multiple exchanges, collecting bids within a timeout, running auction (first or second price), and returning winning bids. Mention OpenRTB protocol and how to handle different exchange formats.
Talk about horizontal scaling, caching, circuit breakers, and fallbacks. Highlight monitoring metrics like bid rate, win rate, latency, and error rates. Mention A/B testing and gradual rollouts.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Talked about a probabilistic identity graph and a central frequency store with TTL-based counters.
Start by clarifying the scale and requirements (e.g., QPS, latency, accuracy). Then propose a high-level architecture that maps devices to a unified user identity, stores frequency counts in a low-latency store, and deduplicates across devices using that identity. Finally, discuss trade-offs and potential optimizations.
Pro tip: Emphasize the importance of a robust identity graph and consider using probabilistic data structures like Bloom filters or HyperLogLog for memory efficiency, but be ready to discuss their trade-offs in accuracy.
Ask about scale (e.g., daily active users, ad impressions per second), latency requirements (e.g., <10ms), and accuracy needs (e.g., exact vs approximate counts).
Propose a system to map multiple devices to a single user ID, using deterministic (e.g., login) and probabilistic (e.g., device graph) methods.
Select a low-latency, scalable store (e.g., Redis, Cassandra) for frequency counts, and decide between exact counters or approximate sketches (e.g., Count-Min Sketch).
On ad request, retrieve the user's frequency count across devices, increment atomically, and enforce the cap. Handle race conditions and eventual consistency.
Address consistency vs availability, cost, and potential optimizations like caching, sharding, and asynchronous updates.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Standard event pipeline stuff: client beacons, a message queue, stream processing, and a write to a time-series store.
Start by clarifying the requirements: what events (impressions, quartile events, clicks) need to be tracked, expected scale (e.g., billions of events per day), and latency requirements for analytics. Then propose a high-level architecture that decouples ingestion, processing, and storage, ensuring scalability, fault tolerance, and exactly-once semantics where needed. Finally, discuss trade-offs and optimizations for cost and performance.
Pro tip: Emphasize the importance of idempotency and deduplication in the pipeline to handle retries and ensure accurate metrics, especially for billing or ad performance. Also, mention how you would handle late-arriving data and backfill for corrected metrics.
Ask questions to understand the volume (e.g., daily active users, events per user), latency needs (real-time vs batch), and data retention. Confirm the need for exactly-once processing and the importance of data accuracy for metrics like quartile events.
Propose a scalable ingestion layer using a distributed message queue like Kafka or Pub/Sub to handle high throughput. Ensure events are partitioned by key (e.g., ad ID or user ID) for ordered processing and include metadata like timestamps and unique event IDs.
Use a stream processing framework (e.g., Flink, Spark Streaming) to validate, enrich, and aggregate events in real-time. Implement windowing for quartile events (e.g., 25%, 50%, 75%, 100% watched) and handle late data with watermarks.
Store raw events in a data lake (e.g., S3) for batch processing and aggregated metrics in a time-series database or OLAP store (e.g., Druid, ClickHouse) for fast queries. Ensure the serving layer supports low-latency dashboards and ad-hoc analysis.
Implement end-to-end monitoring, alerting, and data quality checks. Use idempotent writes and deduplication to handle failures. Discuss how to recover from outages and backfill data without affecting downstream metrics.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
I framed it as traffic splitting at the user segment level with holdout groups, and talked about guardrail metrics to catch revenue drops early.
Start by framing the problem as a safe experimentation challenge: you need to measure policy impact while protecting user experience and revenue. Describe a controlled A/B testing framework with guardrail metrics, gradual rollout, and statistical rigor to detect both intended and unintended effects.
Pro tip: Always define guardrail metrics (e.g., user satisfaction, revenue per user) and set pre-registered stopping rules to avoid p-hacking and ensure you don't harm key business metrics. Also, consider using interleaving or switchback experiments when user-level randomization is impractical.
Articulate the policy change and what you expect to improve (e.g., ad relevance) along with primary metrics (e.g., CTR) and guardrail metrics (e.g., user retention, revenue).
Choose randomization unit (user, session, geo), calculate sample size for desired power, and ensure control and treatment groups are comparable.
Start with a small percentage of traffic, monitor guardrail metrics in real-time, and have automated alerts for significant degradations.
Use appropriate statistical tests (e.g., t-test, sequential testing) to measure effects, check for novelty effects, and segment by user cohorts.
If safe and effective, ramp up; otherwise, roll back or refine the policy. Document findings and share insights for future experiments.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Consent management, data minimization, purpose limitation, the usual.
Start by acknowledging that privacy compliance is a cross-cutting concern that must be designed into the system from day one, not bolted on later. Then walk through key areas such as data minimization, user consent, data retention, and regulatory requirements (e.g., GDPR, CCPA), explaining how you would implement them in the architecture. Finally, tie your approach to Snapchat's specific context, such as ephemeral messaging and handling of user-generated content.
Pro tip: Emphasize that privacy is not just a legal checkbox but a competitive advantage and a core user trust factor, especially for a platform like Snapchat where ephemeral data is a key feature. Show that you understand the trade-offs between data utility and privacy, and propose concrete technical mechanisms like encryption, access controls, and automated data deletion.
Determine which privacy laws apply (e.g., GDPR, CCPA, COPPA) based on user locations and data types (PII, sensitive data). Classify data to understand what protections are needed.
Incorporate data minimization, purpose limitation, and storage limitation into the system architecture. Collect only what is necessary and retain it only as long as needed.
Design clear consent flows, allow users to access, correct, and delete their data, and provide granular privacy settings. Ensure consent is freely given, specific, informed, and unambiguous.
Use encryption in transit and at rest, role-based access controls, and audit logs. Implement data anonymization or pseudonymization where possible.
Set up automated compliance checks, regular audits, and data protection impact assessments (DPIAs). Ensure the system can adapt to evolving regulations.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.