The failure mode I kept seeing in prep (and honestly did myself the first few mocks) is jumping straight to 'token bucket on Redis' without asking who the tenants are or what the latency SLO is.
Start by clarifying requirements and scale (100k+ QPS, multi-tenant, multi-region), then propose a distributed architecture using a fast in-memory store like Redis with sharding and replication. Discuss trade-offs between accuracy and performance, and detail how to handle multi-region consistency and tenant isolation.
Pro tip: Emphasize that rate limiting is a trade-off between precision and latency; using approximate algorithms like sliding window with local caching can achieve high throughput while maintaining acceptable accuracy. Also, highlight the importance of monitoring and dynamic configuration to adapt to changing traffic patterns.
Ask about the expected traffic patterns, latency requirements, consistency needs, and tenant isolation level. Confirm the scale: 100k+ QPS globally, multiple tenants, and regions.
Select an algorithm like token bucket, leaky bucket, fixed window, or sliding window, considering accuracy, memory footprint, and performance. Discuss trade-offs and justify your choice.
Propose a distributed system using a fast data store (e.g., Redis) with sharding and replication. Consider using local caches and asynchronous synchronization to reduce latency and load on the central store.
Explain how to handle multiple regions (e.g., regional clusters with global synchronization or eventual consistency) and tenant isolation (e.g., separate keys, quotas, and resource allocation).
Cover trade-offs between consistency and availability, handling failures (e.g., fallback to local limits), and monitoring/alerting. Mention dynamic configuration and scalability.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Fan-out at that scale is where things get interesting.
Start by clarifying requirements and scale, then propose a high-level architecture with decoupled services for ingestion, fan-out, and delivery. Dive into critical components like fan-out strategies, retry with exponential backoff, and deduplication using idempotency keys. Discuss trade-offs and how to handle failures at scale.
Pro tip: Emphasize idempotency and exactly-once semantics; show you understand that at 100M+ users, even rare edge cases become common, so design for failure and monitor everything.
Ask about user activity, notification types, latency requirements, and delivery guarantees. Confirm scale: 100M+ users, potentially billions of notifications per day.
Propose a pipeline: ingestion API -> message queue -> fan-out service -> delivery services (push/email/SMS) -> providers. Use microservices and async processing.
Discuss push vs pull models. For real-time, use push-based fan-out with partitioning by user ID. Consider hybrid for large fan-out events (e.g., celebrity users).
Implement retry with exponential backoff and jitter, dead-letter queues for failures. Use idempotency keys and a deduplication store (e.g., Redis) to prevent duplicate sends.
Discuss consistency vs availability, latency vs throughput, and cost. Mention sharding, rate limiting, and monitoring/alerting for system health.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
The clarification phase here is literally graded separately.
Start by explicitly acknowledging the ambiguity and asking clarifying questions to pin down the domain (metric streams, user behavior, or infrastructure alerts), as the detection techniques and system design differ significantly. Then, choose one domain (e.g., metric streams) and walk through a scalable, end-to-end anomaly detection system, covering data ingestion, detection algorithms, alerting, and feedback loops.
Pro tip: Demonstrate adaptability by briefly contrasting how your design would change for each domain, showing you can handle ambiguity while still delivering a concrete solution. Also, emphasize the importance of reducing false positives through techniques like ensemble methods and human-in-the-loop feedback.
Ask questions to determine the data type (metric streams, user behavior, infrastructure alerts), scale, latency requirements, and definition of an anomaly. This ensures you design the right system for the context.
Select appropriate algorithms based on the domain: statistical methods (e.g., Z-score, moving average) for metric streams, unsupervised learning (e.g., clustering, autoencoders) for user behavior, or rule-based and time-series analysis for infrastructure alerts. Consider trade-offs between simplicity and accuracy.
Outline components: data ingestion (e.g., Kafka), preprocessing (e.g., windowing, normalization), detection engine (e.g., streaming or batch), alerting (e.g., thresholds, notifications), and storage for historical data and model training. Ensure scalability and fault tolerance.
Discuss handling concept drift, false positives/negatives, and feedback loops for continuous improvement. Include monitoring of the detector itself and mechanisms for model retraining.
Recap the design and briefly explain how it would change for the other domains, showing flexibility and depth of understanding.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Privacy and security requirements here are not decorative.
Start by clarifying functional and non-functional requirements, especially around privacy, security, and efficiency. Then design the high-level architecture, focusing on how devices report location securely and how users query it with minimal data exposure. Finally, dive into trade-offs and optimizations for scale, battery, and privacy.
Pro tip: Emphasize end-to-end encryption and privacy-preserving techniques like differential privacy or secure enclaves, as Google values user trust. Also, discuss battery efficiency and offline scenarios, showing you consider real-world constraints.
Ask questions to understand scale, latency, privacy regulations, and device types. Define core features: locate device, remote lock/wipe, and last known location.
Outline components: device clients, backend services (location ingestion, query, notification), and databases. Describe data flow from device to user.
Explain encryption (in transit and at rest), authentication (device and user), and access control. Discuss how to prevent unauthorized tracking.
Address battery impact on devices, data storage and indexing for fast queries, and handling millions of devices. Consider geo-sharding and caching.
Discuss trade-offs like accuracy vs. privacy, real-time vs. batched updates, and handling offline devices. Mention failure modes and mitigations.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying functional and non-functional requirements, such as the number of regions, update frequency, and consistency needs. Then propose a high-level architecture with a central management service, regional caches, and edge devices, emphasizing eventual consistency and offline operation. Finally, dive into data modeling for time-of-day variants and trade-offs between consistency, latency, and availability.
Pro tip: Explicitly discuss how you would handle conflicts and versioning, and consider using a CRDT or last-write-wins with vector clocks to ensure deterministic merges across regions.
Ask questions to understand scale (number of restaurants, regions), update frequency, consistency requirements, and offline duration tolerance. Define what 'time-of-day menu variants' means (e.g., breakfast, lunch, dinner).
Propose a central menu management service that publishes updates to regional distribution services (e.g., via pub/sub). Each region caches menus and syncs to edge devices (restaurant servers) using a push/pull mechanism.
Design a menu schema that includes time-of-day variants, region-specific overrides, and versioning. Use a hierarchical or tagged structure to efficiently query applicable menus.
Ensure edge devices can operate offline by storing menus locally and syncing when connectivity resumes. Use a sync protocol that handles conflicts (e.g., version vectors) and allows partial updates.
Discuss trade-offs between consistency models (strong vs. eventual), latency, and cost. Address scalability concerns like handling many regions and devices, and monitoring/alerting for sync failures.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the requirements: scale, read/write ratio, latency, and whether the word set is static or dynamic. Then compare trie-based and sorted-index approaches for in-memory and disk-based scenarios, discussing time/space trade-offs. Finally, explain how sharding can distribute the load and maintain query efficiency at scale.
Pro tip: Emphasize that the choice depends on the workload: tries excel for prefix-heavy queries and dynamic sets, while sorted indexes (e.g., B-trees or SSTables) are better for range scans and disk-based storage. Mention that sharding by lexicographic ranges can preserve query locality but may cause hotspots.
Ask about data size, read/write patterns, latency requirements, and whether the set is static or dynamic. This determines the appropriate data structures and sharding strategy.
Discuss tries (standard, compressed, ternary search trees) for storing words. Explain how to perform range queries by traversing the trie and collecting words within the lexicographic bounds, noting time complexity O(k + output size) where k is the prefix length.
Describe using a sorted array, balanced BST, or B-tree to store words. Range queries become binary search for the start and end, then sequential scan. Compare with tries in terms of memory, update cost, and query performance.
Explain sharding by lexicographic ranges (e.g., A-F, G-M) to distribute data. Discuss trade-offs: range sharding enables efficient range queries but can lead to hotspots; hash sharding balances load but scatters range queries across shards.
Based on requirements, recommend a hybrid or specific approach. For example, use a distributed sorted index with range sharding and caching for hot ranges, or a trie for in-memory prefix-heavy workloads.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This is the part of every round where I felt most exposed.
Start by framing failure handling as a core design principle, not an afterthought. For each failure scenario (region disconnect, storage failure, tenant abuse), describe detection, impact, and the fail-open vs fail-close decision with clear reasoning. Emphasize trade-offs between availability, consistency, and security, and how you'd validate decisions through testing and monitoring.
Pro tip: Explicitly state that fail-open vs fail-close decisions should be driven by business impact and user expectations—e.g., fail-open for read paths to maintain availability, fail-close for write paths to prevent data corruption. This shows you think in terms of risk management, not just technical mechanisms.
Ask clarifying questions about SLAs, consistency requirements, and tenant isolation expectations. State your assumptions about the system's scale and criticality.
Explain how you detect each failure (e.g., health checks, circuit breakers, rate limiting) and how you isolate the blast radius (e.g., bulkheads, cell-based architecture).
For each scenario, decide whether to fail-open (continue serving, possibly degraded) or fail-close (reject requests) based on user impact, data integrity, and security. Justify each choice.
Outline steps for graceful degradation, automatic recovery, and manual intervention. Include how you'd communicate status and handle retries/backoff.
Explain how you'd test failure scenarios (chaos engineering, game days) and use monitoring/alerting to refine decisions over time.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the product requirements and expected load characteristics, then walk through a phased evolution from MVP to 10x to 100x, highlighting where bottlenecks emerge and how you would re-architect. Emphasize trade-offs, incremental rollout strategies, and cross-functional alignment to ensure a smooth transition.
Pro tip: Demonstrate maturity by acknowledging that re-architecture is risky and should be driven by data—propose a strangler pattern or feature-flagged migration to de-risk the rollout, and always have a rollback plan.
Ask questions to understand the product's core functionality, expected user base, read/write patterns, latency and consistency requirements, and budget constraints. State your assumptions explicitly.
Propose a simple, monolithic architecture that prioritizes speed of iteration and low operational overhead. Use managed services where possible and avoid premature optimization.
Identify bottlenecks (e.g., database, compute) and introduce targeted improvements like caching, read replicas, horizontal scaling, and asynchronous processing. Keep the architecture as simple as possible.
Recognize when a fundamental re-architecture is needed (e.g., moving to microservices, sharding, event-driven design). Explain the new design, how it addresses current limits, and the trade-offs involved.
Describe a phased migration strategy (e.g., strangler pattern, blue-green deployment, feature flags) with monitoring, canary releases, and rollback plans. Highlight cross-functional collaboration with product, SRE, and other teams.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.