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.

MediumTechnical

0 practiced

Explain how to implement distributed tracing across services written in multiple languages. Cover propagation of trace context (headers), sampling strategies to manage volume, how to handle high-cardinality tags, and how traces are used during incident triage to pinpoint latency hotspots and error paths.

MediumTechnical

0 practiced

Describe the elements of a high-quality postmortem for a production outage. Explain how to keep the postmortem blameless, identify systemic contributing factors beyond the immediate trigger, prioritize and assign action items, and how to track remediation to closure and measure impact of fixes.

HardTechnical

0 practiced

Implement in Python a canary analysis function that takes two numeric lists: baseline and canary latencies (milliseconds). Return True if the canary is statistically indistinguishable (i.e., acceptable) from baseline using a two-sample t-test with significance level 0.05. Handle small sample sizes and unequal variances (Welch's t-test). Provide example usage and expected outputs.

Example:baseline = [100, 110, 95, 105]canary = [120, 130, 115, 125]# expected return: False (canary significantly slower)

MediumTechnical

0 practiced

Define an artifact management policy for Docker images and other build artifacts: include versioning and tagging scheme, immutability guarantees, artifact signing and provenance, retention and cleanup policies, promotion between environments, and how to retrieve the exact image deployed for incident investigation.

MediumSystem Design

0 practiced

Design an observability solution for a microservices platform: which metrics (SLIs and system metrics) to collect, log structure and aggregation strategy, tracing coverage and sampling, retention policies, dashboards for SRE vs product teams, and automated detection approaches for regressions and anomalies.

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