Team Specific Technical Stack and Backend Systems Questions

Discuss the team's specific technologies mentioned in the job description (Node.js, Python, Java, PostgreSQL, MongoDB, AWS, Azure, etc.). Ask about their backend architecture, how they handle scalability and reliability, deployment practices, and monitoring/alerting. Inquire about recent technical decisions or challenges they've faced. Show interest in learning their specific tech stack and systems. Ask realistic questions about the ramp-up period and learning curve.

MediumTechnical

37 practiced

Provide an Azure DevOps YAML pipeline snippet (concise) that builds a Docker image for a Node.js service, runs unit tests, scans the image for vulnerabilities (example scanner), pushes to Azure Container Registry, and deploys to AKS. Include a gated approval before production deployment and references to secure storage of service connection credentials.

HardTechnical

62 practiced

You observe increased tail latency for a distributed call chain Node.js -> Java -> Python. Describe a systematic triage to find the root cause using distributed tracing, logs, and metrics. After identifying likely causes, list infra-level and code-level optimizations you might apply to reduce tail latency.

EasyTechnical

52 practiced

Describe the trade-offs between in-app database connection pooling (for example HikariCP in Java or pg-pool in Node.js) and using an external pooler such as pgbouncer. For a fleet of Node.js API pods that create frequent short-lived connections, which strategy would you recommend and why?

HardTechnical

66 practiced

Design a backup and restore strategy for a sharded MongoDB cluster that minimizes RPO and RTO. Discuss coordinated snapshot approaches across shards and config servers, oplog-based incremental backup and replay, point-in-time recovery considerations, encryption and access control of backups, and regular verification of restore procedures.

EasyTechnical

46 practiced

Given an AWS EKS cluster running Node.js and Python services, list exact tools, commands, and API calls you would use to gather CPU, memory, and network metrics and logs to triage a pod causing high latency. Include Kubernetes-native commands, Prometheus queries, and relevant CloudWatch steps.

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