I started with requirements which felt right, but I burned maybe 15 minutes just on scoping and the interviewer had to nudge me toward the actual architecture.
Start by clarifying functional and non-functional requirements, then estimate scale (users, files, storage). Design the high-level architecture with core components (API gateway, metadata service, blob storage, search, sharing service), then dive into data models and key flows like upload/download, sharing, versioning, and trash.
Pro tip: Emphasize trade-offs, especially around consistency vs. availability for sharing and metadata, and how you'd handle large file uploads with chunking and resumability. Also, mention how you'd leverage AWS services like S3, DynamoDB, and Elasticsearch to simplify the design.
Clarify functional requirements (upload/download, folders, sharing, search, versioning, trash) and non-functional (availability, durability, consistency, latency). Estimate scale: number of users, files per user, average file size, read/write ratio.
Outline main components: API gateway, metadata service (SQL/NoSQL), blob storage (S3), search service (Elasticsearch), sharing/access control service, versioning service, trash service. Describe how they interact.
Design schemas for users, files, folders, permissions, versions, and trash. Consider hierarchical folder structure using parent pointers or materialized paths. Decide on SQL vs NoSQL for metadata based on query patterns.
Detail upload/download (chunking, resumability, deduplication), sharing (link sharing, user/group permissions, access control lists), search (indexing metadata and content), versioning (storing multiple versions, retrieval), and trash/restore (soft delete, retention policy).
Discuss trade-offs: consistency vs. availability for sharing, latency vs. durability for uploads, cost vs. performance for storage tiers. Explain how to scale each component (sharding, caching, CDN) and handle failures.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Talked through separating blob storage from metadata, which they seemed fine with.
Start by clarifying requirements (scale, access patterns, consistency, durability) and then propose a layered architecture: object store for blobs, metadata store for structured attributes, and indexing layer for efficient lookups. Discuss trade-offs between consistency, latency, and cost, and justify choices with Amazon-scale considerations like S3, DynamoDB, and Elasticsearch.
Pro tip: Emphasize decoupling and eventual consistency where possible, and mention how you would handle failures and scale each layer independently—this shows you think about real-world operations, not just ideal designs.
Ask about data size, access patterns (read/write ratio, query types), consistency needs, durability, and budget. This ensures your design targets the right trade-offs.
Propose a scalable, durable object store like Amazon S3 for storing large blobs (images, videos, documents). Discuss partitioning, replication, and lifecycle policies.
Choose a database for structured metadata (e.g., DynamoDB for key-value access or Aurora for relational). Discuss schema, indexing, and consistency models.
Implement a search/indexing service (e.g., Elasticsearch or a custom inverted index) to enable efficient queries on metadata. Discuss how to keep it in sync with the metadata store.
Explain how each layer scales independently, handles failures, and maintains consistency. Discuss cost, latency, and operational complexity trade-offs.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This was actually the part I felt best about.
Start by clarifying requirements (file size, concurrency, client types) and then outline a chunked upload protocol with resumability. Describe the client-server interaction, storage design, and failure handling, emphasizing idempotency and scalability.
Pro tip: Mention how you'd leverage S3 multipart upload with pre-signed URLs to offload chunk handling and enable resumability without burdening your servers. Also highlight the importance of tracking upload state and cleaning up incomplete uploads to avoid storage leaks.
Ask about file sizes, network conditions, client types, and concurrency needs. Determine if the solution should be server-centric or leverage cloud storage services.
Define chunk size (e.g., 5-10 MB), how to split files, and how to assign unique identifiers to each chunk and the overall upload session.
Outline the API endpoints for initiating upload, uploading chunks (with retries), checking status, and completing the upload. Ensure idempotency and track progress server-side.
Describe retry logic with exponential backoff, handling duplicate chunks, and supporting parallel chunk uploads. Discuss how to resume from the last successful chunk.
Explain how chunks are stored (e.g., S3 multipart), how to assemble them, and how to clean up incomplete uploads. Consider scalability and cost implications.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Pretty standard answer: TLS for transit, envelope encryption for at-rest where each file gets its own data key and the data key is encrypted with a master key.
Start by clarifying the requirements and constraints, then propose a layered encryption strategy covering data at rest and in transit, and discuss trade-offs like performance, key management, and compliance. Emphasize AWS services and best practices, and conclude with monitoring and rotation policies.
Pro tip: Demonstrate awareness of Amazon's leadership principles by highlighting customer trust and security as top priorities, and mention specific AWS services like KMS, ACM, and S3 encryption options to show practical knowledge.
Ask about data sensitivity, compliance needs (e.g., HIPAA, GDPR), performance requirements, and existing infrastructure to tailor the solution.
Propose using AWS KMS for key management, S3 SSE (SSE-S3, SSE-KMS, SSE-C) or EBS encryption for storage, and client-side encryption for additional control.
Recommend TLS/SSL for all data in transit, using ACM for certificate management, and enforcing HTTPS with security groups and VPC endpoints.
Discuss key rotation policies, separation of duties, and using AWS KMS customer managed keys for granular control and auditability.
Mention logging with CloudTrail, monitoring with CloudWatch, and regular audits to ensure encryption policies are enforced and compliant.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Framed it as an async pipeline triggered after upload.
Start by clarifying requirements (file types, sizes, latency, security) and then design an asynchronous, event-driven pipeline using AWS services like S3, SQS, and Lambda. Explain how virus scanning and thumbnail generation can run in parallel or sequentially, with trade-offs around cost, latency, and reliability.
Pro tip: Emphasize idempotency and dead-letter queues to handle failures gracefully, and mention how you'd monitor and alert on scanning failures or thumbnail generation errors—this shows operational maturity that Amazon values.
Ask about file types, size limits, expected throughput, latency requirements, and security/compliance needs. This ensures your design addresses the right constraints.
Propose an event-driven pipeline: file upload triggers an event, which enqueues a message. Use separate queues or topics for virus scanning and thumbnail generation to decouple processing.
Use a scanning service (e.g., ClamAV on EC2/ECS, or a managed service) triggered by the queue. Scan the file, and on success, proceed; on failure, quarantine the file and notify.
For image/video files, use a library like ImageMagick or FFmpeg in a Lambda or container. Generate thumbnails and store them in a separate S3 bucket or prefix.
Use dead-letter queues for failed scans or thumbnail jobs, implement retries with backoff, and auto-scale based on queue depth. Monitor with CloudWatch and set alarms.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Talked through sharding metadata by user ID, replicating blobs across availability zones.
Start by clarifying requirements like read/write ratio, consistency needs, and scale. Then propose a partitioning strategy (e.g., hash-based) and a replication strategy (e.g., leader-follower with quorum) that together meet scalability and high availability. Discuss trade-offs and how you'd handle rebalancing, failover, and consistency.
Pro tip: Tie your choices to Amazon's leadership principles and services (e.g., DynamoDB, S3) to show customer obsession and ownership. Mention real-world failure scenarios and how your design mitigates them.
Ask about data volume, read/write patterns, latency, consistency, and availability targets to scope the problem.
Select a partitioning key and method (e.g., hash, range) to distribute data evenly and avoid hotspots.
Decide on replication topology (e.g., leader-follower, multi-leader) and consistency model (e.g., quorum) to ensure durability and availability.
Explain how the system detects failures, promotes replicas, and rebalances partitions when nodes are added or removed.
Compare consistency vs. availability, latency vs. durability, and cost implications of your design choices.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Said permission checks should happen at the API gateway layer before any storage call.
Start by clarifying the scale and requirements, then propose a layered architecture with permission checks at multiple points: coarse-grained at the edge, fine-grained at the service, and data-level filtering. Emphasize caching, policy evaluation optimizations, and trade-offs between latency, consistency, and complexity.
Pro tip: At Amazon, always tie your answer back to customer impact and operational excellence—mention how you'd measure permission check latency and set up alarms for authorization failures.
Ask about the number of users, resources, request rate, latency SLAs, and consistency requirements. This shapes the entire design.
Choose a model like RBAC, ABAC, or ReBAC, and decide how policies are stored and versioned. Consider using a centralized policy decision point (PDP).
Enforce coarse-grained checks at the API gateway (e.g., authentication, rate limiting) and fine-grained checks at the service layer (e.g., resource-level permissions). Optionally, push filters to the data layer for row-level security.
Cache policy decisions with short TTLs, use efficient policy evaluation (e.g., compiled policies), and consider asynchronous checks for non-critical paths. Shard policy data if needed.
Instrument permission checks with metrics and logs, and handle cache invalidation and policy updates gracefully. Discuss trade-offs between consistency and latency.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Did some rough math on storage, bandwidth, and metadata ops.
Start by clarifying the scale (e.g., requests per second, data volume) and then walk through a structured capacity estimation: break down traffic, compute resource needs (compute, storage, network), and identify primary cost drivers. Emphasize trade-offs and how design choices impact both capacity and cost.
Pro tip: Tie your estimates to concrete AWS services and pricing models (e.g., EC2, S3, DynamoDB) and mention cost optimization strategies like auto-scaling, reserved instances, or spot instances to show business awareness.
Ask clarifying questions about user base, request rate, data size, and growth projections to establish a baseline for calculations.
Calculate peak QPS, storage requirements, and network bandwidth, then derive the number of servers, database capacity, and caching layers needed.
Break down costs into compute, storage, network, and managed services, highlighting which components dominate the bill.
Explain how design decisions (e.g., instance types, data retention, CDN usage) affect capacity and cost, and propose optimizations.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.