iPaaS questions: name the platforms → connector vs. custom code → when to build vs. buy → monitoring integration health
0 / 5 completed
1 / 5
The interviewer asks: "When would you choose webhooks over polling for an integration?" Which answer is most complete?
Option B is strongest: it names five decision factors with nuance for each, introduces the caveat that polling can be more efficient at very high event frequency (delta cursor), explains why webhooks require idempotent consumers (at-least-once delivery), names the specific failure scenario where webhooks lose events (consumer downtime beyond retry window), and recommends combining both approaches with a reconciliation job. Integration pattern vocabulary:Webhook — an HTTP callback triggered by an event in the source system. Polling — periodically requesting the source API for new data. Delta cursor — a timestamp or offset marking the last-processed position in a polling stream. At-least-once delivery — guarantees delivery but may duplicate; requires idempotent consumers. Reconciliation job — a periodic job that compares source and destination state to detect missed events. Options C and D are accurate but lack the high-frequency polling caveat and the reconciliation job recommendation.
2 / 5
The interviewer asks: "How do you handle partial failures in a multi-system integration?" Which answer is most systematic?
Option B is strongest: it introduces a three-level framework, classifies operations along two axes (idempotency and reversibility) before naming recovery patterns, distinguishes choreography from orchestration sagas, explains idempotency key retry as an alternative for workflows tolerating partial completion, and provides the observability requirements (correlation ID, TTL alert) as a distinct layer. Integration failure handling vocabulary:Partial failure — some steps of a multi-system workflow succeed while others fail. Saga pattern — a sequence of local transactions with compensating transactions for failure recovery. Compensating transaction — the inverse operation that undoes a completed step. Idempotency key — a unique identifier sent with a request so the target can deduplicate repeated calls. Correlation ID — a unique identifier propagated through all systems involved in a workflow for tracing. TTL alert — an alert triggered when a workflow has not completed within its expected time window. Options C and D are accurate but lack the operation classification matrix and the choreography vs. orchestration distinction.
3 / 5
The interviewer asks: "What does idempotency mean in the context of integration workflows?" Which answer is most precise?
Option B is strongest: it provides the formal definition with the system-state qualifier (same state, not just same return value), explains why atomic storage is required (crash gap vulnerability), distinguishes HTTP method semantics with the PUT-for-caller-assigned-ID pattern, introduces the Stripe Idempotency-Key header as the practical production pattern, and names three specific failure modes where idempotency fails silently. Idempotency vocabulary:Idempotency key — a unique identifier assigned by the caller to deduplicate repeated calls. Deduplication store — a persistent store (DB table, Redis) that records processed idempotency keys. Atomic store — the idempotency key and the operation's effect are persisted in the same transaction. Crash gap — the window between processing and storing where a crash causes re-processing on restart. Idempotency-Key header — an HTTP header (popularised by Stripe) allowing clients to mark requests as idempotent. Options C and D are accurate but lack the atomic storage crash gap explanation and the Stripe header practical detail.
4 / 5
The interviewer asks: "How do you version an integration that multiple customers rely on?" Which answer is most mature?
Option B is strongest: it organises versioning into three layers with distinct strategies, introduces the compatibility taxonomy (backward/forward), names Postel's Law as the governing principle for consumer implementation, introduces schema registries for event-driven integrations (a layer many candidates miss), explains the compatibility wrapper as a cost-reduction technique, and names the silent failure risk (semantic changes that appear non-breaking). Integration versioning vocabulary:Backward compatibility — old consumers work with a new version of the API or schema. Forward compatibility — old consumers can process new messages by ignoring unknown fields. Postel's Law — "be conservative in what you send, liberal in what you accept." Schema registry — a central store for message schemas that enforces compatibility rules. Full compatibility — both backward and forward compatible; the safest schema evolution policy. Compatibility wrapper — a translation layer that maps v1 input to v2 internally, avoiding duplicate implementation. Options C and D are accurate but lack Postel's Law and the schema registry event layer.
5 / 5
The interviewer asks: "When should you use an iPaaS platform vs. building a custom integration?" Which answer is most strategic?
Option B is strongest: it names four decision dimensions with specific criteria for each, names both classes of iPaaS tools (low-code Zapier/Make vs. enterprise Workato/Boomi/MuleSoft), quantifies the cost dynamics at different volumes (low volume → iPaaS, high volume/multi-tenant → custom), explains the observability gap with specific custom integration features (DLQ, tracing), and provides the practical decision trigger framework (cost threshold, reliability requirement, product differentiator). iPaaS vocabulary:iPaaS (Integration Platform as a Service) — cloud platforms for building integrations via pre-built connectors (Zapier, Make, Workato, Boomi, MuleSoft). Pre-built connector — a ready-made integration component for a specific SaaS application. Dead letter queue (DLQ) — a queue where failed messages are held for manual inspection or reprocessing. Per-task pricing — iPaaS billing model charging per integration operation execution. Product differentiator — a capability that provides competitive advantage; worth building custom. Options C and D are accurate but lack the multi-tenant volume analysis and the product differentiator trigger.