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API and Service Testing Questions

Testing strategies for APIs and services operating at scale. Covers the test pyramid and the balance between unit, API, integration, and end to end tests; consumer driven contract testing and contract verification; mocking and stubbing dependent services in CI and local testing; strategies to keep tests fast and reliable in microservices environments; test data management, versioning-aware tests, performance and load testing for APIs, and integrating tests into continuous integration and deployment pipelines.

MediumTechnical
72 practiced
Propose a strategy to seed deterministic test data for ephemeral environments spun up by CI or testcontainers. Cover methods to speed up setup (snapshots or cloning), ensure referential integrity, make seeding idempotent for retries, and clean up resources across parallel runs.
MediumTechnical
71 practiced
Design a load test for a search API that must handle 500 RPS for 1 million users. Specify the tool you would choose (k6, Gatling, JMeter), test data generation approach, ramp-up pattern, think time, key metrics to capture (including p50/p95/p99 latency and error rate), and how you would detect and isolate bottlenecks.
MediumTechnical
100 practiced
Explain how you would use distributed tracing, structured logs, and metrics to triage an intermittent API test failure in CI that only occurs under high concurrency. Describe what correlation identifiers to add to tests, how to collect traces, and how to surface relevant spans and metrics when a test run fails.
MediumTechnical
150 practiced
Implement a Python decorator or pytest fixture that retries a flaky API test up to 3 attempts using exponential backoff plus jitter. Requirements: configurable max attempts and base delay, log each retry attempt, and raise the final exception if all attempts fail. Provide a short usage example applied to a pytest test function.
HardTechnical
89 practiced
You run load tests against services behind a service mesh (for example Istio) and observe increased tail latency at high load. Propose a methodology to isolate whether the mesh (sidecar), application code, or database is responsible. Include instrumentation to collect, experiments (control vs experiment), and low-level techniques such as packet capture, eBPF, and flame graphs.

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