How the interviewer opens it
“Design a parts compatibility feature for an e-commerce site. When a shopper views a car, bike, or computer part, we should tell them whether it fits their product and help them find parts that do.”
The prompt is vague on purpose. The first few minutes are yours: narrow it to the core features and agree on the numbers before you draw anything.
What to clarify first
- supported domains and shopper input
- identity and conditions
- sources and trust
- verdict semantics
- catalog scale
- traffic and read:write mix
- latency
- consistency and availability
- freshness and durability
- existing systems and exclusions
The answers stay with the interviewer. In a SysRound round, Alex answers them as an interviewer would.
Scope of a strong answer
Core requirements
- Identify the shopper's target product and relevant configuration.
- Check a part against that configuration and supplied context, returning a verdict with a reason.
- List confirmed compatible parts using the same shopper context, with category, brand, and price filters and pagination.
- Import and safely update compatibility evidence from trusted sources.
Usually out of scope
- Inferring physical compatibility from images or product descriptions.
- Solving arbitrary multi-part assemblies or complete computer builds.
- Checkout, inventory reservations, and popularity-based recommendations.
- Building the main product catalog or account system.
- Treating unreviewed seller or shopper claims as authoritative.
What interviewers probe
- Product identity and incomplete knowledge
- Two products with the same marketing name may require different parts, while a shopper may not know the distinguishing attribute. A binary answer can turn missing information into an expensive false positive.
- Serving checks and context-aware discovery
- A fast point lookup does not automatically support filtered reverse queries. Discovery must apply the same conditional rules and shopper context while avoiding catalog scans and per-result network calls.
- Conflicting evidence and the correction deadline
- Fitment is imported knowledge. A withdrawal or broad correction can change many answers, and repeated cache refreshes from stale replicas can preserve a false positive indefinitely unless serving has an explicit freshness authority.
Common mistakes
- Treating compatibility as a category match or an unqualified many-to-many table.
- Confusing unknown with incompatible, or ignoring qualifiers when labeling a part as fitting.
- Designing context-aware point checks but context-free discovery.
- Proposing a graph database or universal rules engine without concrete query and update paths.
- Ignoring source conflicts, withdrawals, and out-of-order imports.
- Treating cache TTLs or eventually consistent indexes and replicas as sufficient proof of freshness.
The bar by level
- Senior (L5/E5)
- Supports equivalent context in checks and discovery, defines source precedence and idempotent updates, and designs filtered pagination with batched evaluation. Uses the estimates to justify capacity and caching. Gives a complete 60-second correction argument that accounts for stale replicas, cache refill, broad updates, and unknown responses during failures.
- Staff (L6+/E6+)
- Defines trust, acceptance, and publication boundaries across catalog identity, source evidence, overrides, and serving authority. Quantifies expansion and validation costs; handles identity repair and scoped rollback. Specifies fenced failover and safe regional degradation, and can justify any freshness-watermark or lease optimization without weakening the correction deadline.