Basic Fault Tolerance Patterns Questions

Understanding common patterns that make systems fault-tolerant: replication (data redundancy across multiple servers), failover (switching to backup when primary fails), circuit breakers (stopping requests to failing services to prevent cascades), retry with exponential backoff (intelligent retrying with delays), timeouts (preventing hanging requests), and graceful degradation (providing partial functionality when components fail). Know when each pattern is appropriate and its trade-offs. Understand that fault tolerance usually involves trade-offs: more replicas cost more but tolerate more failures.

EasyTechnical

94 practiced

Define graceful degradation. Give two concrete examples of graceful degradation strategies for an e-commerce service (for example, during a partial outage) that preserve core user journeys while disabling lower-priority features.

HardTechnical

58 practiced

Design an automated DR test plan that validates failover and replication across regions during maintenance windows. Include the types of tests (smoke, failover, data consistency), automation scripts, rollback procedures, stakeholder notifications, and safety gates to avoid impacting customers.

HardTechnical

62 practiced

Design a pattern for multi-step operations that involve multiple downstream services (e.g., place order: inventory, payment, fulfillment) so that partial failures can be reconciled. Outline idempotency, compensating transactions (sagas), ordering guarantees, and how you would surface reconciliation failures to operators.

HardTechnical

69 practiced

You must achieve 99.99% availability for a service. Model the replication and capacity trade-offs to meet this target given a failure distribution (e.g., single server MTBF, rack failures, and region outages). Explain assumptions, a capacity plan, and how to include error budgets in cost decisions.

MediumTechnical

54 practiced

Compare timeouts and deadlines in distributed request chains. How do you propagate a global deadline through multiple synchronous downstream calls so the overall latency budget isn't exceeded? Mention tracing or context propagation techniques you would use in a Go or Java stack.

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