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Production Deployments and Operations Questions

Covers the end to end practices and trade offs involved in releasing, running, and operating software in production environments. Topics include deployment strategies such as blue green deployment, canary releases, and rolling updates, and how each approach affects reliability, rollback complexity, recovery time, and release velocity. Includes feature flagging and release gating to separate deployment from feature exposure. Addresses continuous integration and continuous deployment pipeline design, automated testing and validation in pipelines, artifact management, environment promotion, and release automation. Covers infrastructure as code and environment provisioning, containerization fundamentals including container images and runtimes, container registries, and orchestration fundamentals such as scheduling, health checks, autoscaling, service discovery, and the role of Kubernetes for scheduling and orchestration. Discusses database migration patterns for large data sets, strategies for online schema changes, and safe rollback techniques. Explores monitoring and observability including metrics, logs, and traces, distributed tracing and error tracking, performance monitoring, instrumentation strategies, and how to design systems for effective troubleshooting. Includes alerting strategy and runbook design, on call and incident response processes, postmortem practice, and how to set meaningful service level objectives and service level indicators to balance reliability and velocity. Covers scalability and high availability patterns, multi region deployment trade offs, cost versus reliability considerations, operational complexity versus operational velocity trade offs, security and compliance concerns in production, and debugging and troubleshooting practices for distributed systems with partial information. Candidates should be able to justify trade offs, explain when a simple deployment model is preferable to a more complex architecture, and give concrete examples of operational choices and their impact.

HardSystem Design
42 practiced
Design an observability platform that ingests metrics, logs, and traces from thousands of services. Explain ingestion pipeline (streaming vs batch), hot vs cold storage, retention tradeoffs, query/aggregation capabilities, sampling/downsampling strategies for traces and metrics, cost control techniques, and how to provide actionable SLO dashboards and alerting with low false positives.
MediumTechnical
42 practiced
Describe an automated canary promotion algorithm: how to initially route a small percent of traffic, the metrics to collect (error rate, p95 latency, saturations), how to statistically compare canary vs baseline, promotion rules, hold/rollback criteria, and how to handle timeouts or insufficient samples.
MediumTechnical
75 practiced
Describe a secure and efficient container image build pipeline: multi‑stage builds to minimize size, pinning base images, vulnerability scanning, image signing/provenance, layer caching, reproducible builds, and retention policies in the registry. Explain how to integrate these steps into CI pipelines without blocking developer feedback loops.
MediumTechnical
48 practiced
Design an approach to instrument distributed tracing across polyglot microservices (Java, Node, Python). Describe how to propagate trace context across HTTP/gRPC calls and message queues, sampling strategies to control costs, and how to correlate traces with logs and metrics to enable end‑to‑end root cause analysis.
MediumSystem Design
49 practiced
Design an alerting strategy and a concise runbook for a payment processing service that starts showing elevated error rates. Include SLI/SLO mapping, alert severity tiers and thresholds, telemetry required (metrics, logs, traces), immediate mitigation steps to include in the runbook, paging/escalation rules, and how to avoid alert fatigue during deployments.

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