This was the anchor question for the whole session.
Start by clarifying functional and non-functional requirements, especially around security, compliance, and scale. Then propose a high-level architecture that separates authentication, authorization, storage, and metadata services, and dive into data modeling, API design, and security controls. Finally, discuss trade-offs and potential improvements.
Pro tip: Emphasize defense-in-depth: combine encryption at rest and in transit, fine-grained authorization (e.g., ABAC), and audit logging. Also, mention how you would handle key management and rotation, as this is often overlooked.
Ask about expected scale, file types/sizes, compliance needs (e.g., HIPAA, GDPR), and authorization granularity. Confirm non-functional requirements like latency, durability, and availability.
Outline components: API gateway, authentication service (e.g., OAuth2/OIDC), authorization service, metadata database, blob storage (e.g., S3 with SSE), and key management service. Explain how they interact.
Define entities: User, Document, Permission, AuditLog. Design RESTful APIs for upload, download, list, and share, including authorization checks. Consider using signed URLs for direct upload/download to reduce server load.
Detail encryption at rest (per-file keys, envelope encryption) and in transit (TLS). Implement fine-grained authorization (RBAC/ABAC), audit logging, and secure key management (e.g., AWS KMS). Discuss threat models and mitigations.
Discuss trade-offs: consistency vs. availability, cost vs. performance. Explain how to scale (sharding, caching, CDN) and handle failures (retries, idempotency). Mention monitoring and alerting.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Talked through horizontal scaling for the API tier, sharding metadata by user or document ID, and offloading storage to object storage.
Start by clarifying the current architecture and scale assumptions (file sizes, read/write ratio, user concurrency). Then propose a layered scaling strategy: separate metadata from file storage, introduce horizontal scaling for each component, and use caching and CDNs for hot data. Finally, discuss trade-offs and how you'd measure success.
Pro tip: Show you understand that scaling isn't just about adding servers—it's about identifying bottlenecks and making deliberate trade-offs. Mention that you'd start with profiling and metrics to find the actual constraints before optimizing.
Ask about the current system design, expected growth (files, users, traffic), and performance goals. This ensures your answer is tailored and not generic.
Break down the system into components (storage, metadata DB, API servers, network) and discuss which will hit limits first as files and traffic grow.
For each bottleneck, suggest horizontal scaling (sharding, replication), caching, CDNs, and asynchronous processing. Explain how they address the specific growth.
Compare options like SQL vs NoSQL, consistency vs availability, and cost vs performance. Mention migration paths and potential risks.
Explain how you'd measure success (latency, throughput, cost) and iterate. Emphasize starting with monitoring and gradual rollout.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the system's requirements and constraints, then propose a role-based access control (RBAC) model with fine-grained permissions, and discuss trade-offs between simplicity and flexibility. Emphasize how the design supports scalability, security, and maintainability.
Pro tip: Demonstrate awareness of real-world challenges like permission inheritance, audit logging, and the principle of least privilege, and suggest starting with a simple model that can evolve.
Ask questions to understand the system's users, resources, and access patterns, including any compliance or multi-tenancy needs.
Propose RBAC as a baseline, and mention alternatives like ABAC or ReBAC if fine-grained or relationship-based access is needed.
Outline how roles map to permissions, how permissions are assigned to resources, and how inheritance and hierarchies work.
Explain where enforcement happens (e.g., API gateway, service layer) and how it integrates with authentication (e.g., JWT, OAuth).
Compare simplicity vs. flexibility, performance implications, and how the model can evolve with changing requirements.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Chunked uploads, presigned URLs, reassembly on completion.
Start by clarifying the requirements: file size, expected concurrency, and use case (e.g., document upload for legal analysis). Then propose a chunked, resumable upload architecture using pre-signed URLs to object storage, with a backend service to coordinate and validate chunks. Discuss trade-offs between simplicity and scalability, and how to handle failures and cleanup.
Pro tip: Emphasize idempotency and resumability: clients should be able to retry chunks without duplicating data, and the system should track upload state to allow resuming after network failures. This shows you've thought about real-world reliability, not just the happy path.
Ask about file sizes, upload frequency, latency requirements, and whether files need processing after upload. This ensures your design fits the actual use case.
Decide between direct-to-storage (e.g., S3 pre-signed URLs) vs. proxying through your servers. Direct-to-storage is usually better for large files to avoid overloading your backend.
Break files into chunks, upload them in parallel or sequentially, and track progress. Use a unique upload ID and store chunk metadata to enable resuming after failures.
After all chunks are uploaded, validate integrity (e.g., checksums), assemble the file if needed, and trigger any post-processing (e.g., virus scan, indexing).
Discuss retries, timeouts, orphaned chunk cleanup, and how to handle partial uploads. Mention monitoring and alerting for failed uploads.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Genuinely the hardest part of the interview.
Start by clarifying the threat model: the main risks are unauthorized uploads, chunk tampering, and cross-file chunk injection. Then explain how to bind each presigned URL to a specific file ID, chunk index, and user session, and how to verify integrity and ownership at finalization using checksums and metadata.
Pro tip: Mention that presigned URLs should be short-lived and scoped to a single chunk with a unique upload ID, and that you should validate the final assembled file's checksum against a client-provided manifest to detect any tampering.
Identify potential attacks: unauthorized users uploading chunks, chunks being swapped between files, and malicious data injection. This sets the stage for the security controls.
Generate presigned URLs that include the file ID, chunk index, and user ID in the signature or as enforced metadata, so each URL is only valid for a specific chunk of a specific file by a specific user.
Upon upload, validate that the chunk's metadata (e.g., checksum, size) matches expectations, and store chunks in a temporary location keyed by file ID and chunk index.
When all chunks are uploaded, assemble the file and compute its checksum, comparing it to a client-provided manifest checksum to ensure no chunk was tampered with or swapped.
Log all upload activities for auditing, and implement expiration and cleanup for incomplete uploads to prevent resource leaks and abuse.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.