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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.

HardTechnical
0 practiced
Describe strategies to perform online schema changes for petabyte‑scale datasets with minimal impact to live traffic. Cover copy‑and‑rename patterns, dual‑write approaches, background backfills with chunking and throttling, coordination with sharding/partitions and replicas, and how to validate correctness and performance during the rollout.
MediumTechnical
0 practiced
Case study: after adopting CI/CD pipelines the company increased deployments from weekly to daily, but incident and rollback rates rose. Analyze likely root causes across people, process, and technology. Propose a prioritized remediation plan with measurable KPIs (e.g., MTTR, rollback rate, change failure rate) to restore reliability while keeping improved velocity.
HardTechnical
0 practiced
Design an efficient streaming anomaly detection algorithm for real‑time canary metric streams (error rates, latencies). Provide the approach and pseudo‑code covering sliding windows, baselining, statistical tests (e.g., binomial proportion, bootstrap), burst smoothing/hysteresis to reduce false positives, and computational optimizations for high throughput. Explain how you would tune sensitivity and alert thresholds.
EasyTechnical
0 practiced
What is Infrastructure as Code (IaC)? Compare Terraform, CloudFormation, and Pulumi in terms of declarative vs imperative models, state management, idempotency, and multi‑cloud capability. Provide a short example scenario where IaC improves reproducibility of environment provisioning.
HardTechnical
0 practiced
For a customer‑facing API propose concrete SLO targets for availability and latency (for example: availability 99.95% per month, p99 latency < 500ms). Calculate the resulting monthly error budget (in minutes/requests), and propose release gating rules tied to error budget consumption (e.g., throttle or pause automated rollouts when burn rate exceeds threshold).

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