Technical Interviewer’s Questions

1. How would you handle an incident where a production service is experiencing high latency?

Great Response: "I'd first confirm the issue by checking monitoring dashboards and setting up alerts if they haven't triggered. I'd quickly assess the scope and impact to determine severity. Then I'd follow a structured approach: check recent deployments or changes, examine system metrics (CPU, memory, network, disk I/O), and look for unusual traffic patterns or resource contention. If the root cause isn't immediately obvious, I'd use distributed tracing to identify bottlenecks. For immediate mitigation, I might scale resources horizontally, enable caching layers, or implement rate limiting while investigating. I'd keep stakeholders informed through our incident communication channels and document findings in real-time. After resolution, I'd conduct a blameless postmortem to prevent recurrence by implementing automated detection and additional safeguards."

Mediocre Response: "I would check our monitoring systems to confirm the latency issues and then try to figure out what changed recently. I'd look at CPU and memory usage to see if we're running out of resources and might need to scale up. If I can't solve it quickly, I'd involve the team for help. Once we fix it, I'd document what happened and what we did to fix it."

Poor Response: "I'd immediately restart the services since that often fixes latency issues. If that doesn't work, I'd roll back the most recent deployment since it's probably causing the problem. Then I'd add more servers to handle the load while looking for the root cause. I'd keep trying different fixes until the latency drops back to normal levels."

2. Explain how you would set up monitoring for a new microservice.

Great Response: "I'd implement a multi-layered monitoring strategy. Starting with the four golden signals: latency, traffic, errors, and saturation. I'd set up infrastructure monitoring using tools like Prometheus for time-series metrics with proper exporters for system-level stats. For application metrics, I'd instrument the code with appropriate libraries to track business-relevant metrics and request rates. I'd set up distributed tracing with tools like Jaeger or OpenTelemetry to understand service dependencies and bottlenecks. For logging, I'd implement structured logging with proper indexing and search capabilities. I'd create dashboards in Grafana with relevant visualizations and set up alerting with appropriate thresholds based on SLOs, including both static thresholds and anomaly detection where appropriate. Finally, I'd implement synthetic monitoring to regularly test critical user paths and API endpoints."

Mediocre Response: "I would use a monitoring tool like Prometheus to collect metrics from the service and set up Grafana dashboards to visualize them. I'd monitor CPU, memory, disk space, and response times. I'd also set up basic alerts for when the service goes down or when resource usage gets too high. We'd have some logs going to a centralized system so we can search them when there are problems."

Poor Response: "I'd set up CloudWatch or a similar tool to alert us when the service goes down. We'd monitor the basic server metrics and set up a dashboard that shows if the service is up or down. I'd make sure we're logging errors so we can check the logs when something breaks. If users report issues, we'd have the data to look into it."

3. How would you approach capacity planning for a system with fluctuating load?

Great Response: "I'd start by collecting and analyzing historical usage patterns to identify trends, seasonality, and growth rates. Using time-series analysis and statistical methods, I'd create predictive models accounting for daily/weekly patterns, seasonal variations, and special events that might cause spikes. I'd implement autoscaling with both reactive scaling based on current metrics and predictive scaling based on forecasted demand. For cost efficiency, I'd use a combination of reserved instances for baseline capacity and on-demand/spot instances for peaks. I'd also establish headroom policies (like keeping 20% extra capacity) to handle unexpected spikes. To validate the approach, I'd regularly perform load testing to verify the system can handle projected growth. Finally, I'd implement continuous monitoring of prediction accuracy and scaling efficiency, adjusting models as needed based on actual versus predicted usage."

Mediocre Response: "I'd look at our historical usage data to understand peak times and general growth trends. Based on this, I would set up autoscaling rules that add capacity when certain thresholds are reached. I'd make sure we have enough headroom to handle unexpected traffic spikes, probably around 30% extra capacity. We'd review the capacity requirements quarterly to adjust for any changes in usage patterns."

Poor Response: "I would take our current peak usage and add 50% to make sure we have enough capacity. Then I'd set up autoscaling to handle any unexpected traffic. If we start getting close to our limits, we can always add more servers quickly. The cloud makes it easy to scale up when needed, so we don't have to be too precise with predictions."

4. Describe your approach to automating routine operational tasks.

Great Response: "I approach automation strategically, starting with task assessment. I map out all operational tasks and prioritize them based on frequency, time consumption, error rate, and business impact. For selected tasks, I create detailed runbooks first to understand all edge cases. When developing automation, I use infrastructure-as-code principles with tools like Terraform, Ansible, or Puppet, and implement CI/CD pipelines for testing and deployment. I believe in progressive automation—starting with partial automation of stable components while leaving complex decision points for humans, then gradually increasing automation coverage as confidence grows. All automation includes proper error handling, logging, alerting on failures, and self-healing mechanisms where possible. I document everything thoroughly and implement version control for all automation code. Finally, I measure the impact through metrics like time saved, error reduction, and consistency improvements to demonstrate ROI and identify further optimization opportunities."

Mediocre Response: "I'd identify the most time-consuming manual tasks and write scripts to automate them. We could use tools like Ansible or Jenkins for deployment automation and set up cron jobs for recurring tasks. I'd make sure the scripts have proper error handling and that they alert us if something goes wrong. Documentation is important too, so others can understand and maintain the automation."

Poor Response: "I'd write bash scripts or Python scripts to automate the repeating tasks we do regularly. We could schedule them to run at specific times using cron. For deployment, we'd use a CI/CD tool to push code automatically. If any script fails, it would send an email alert so we can look into it."

5. How would you design a high-availability database setup?

Great Response: "I'd implement a multi-region, multi-AZ architecture with a primary-replica setup. The primary database would handle writes while multiple read replicas distribute read traffic. I'd configure automatic failover with minimal data loss using synchronous replication for critical data and asynchronous for less critical data, with proper monitoring of replication lag. For data durability, I'd implement point-in-time recovery with transaction logs and regular backups stored in multiple regions. To prevent data corruption, I'd use checksums and consistency checks. For load management, I'd implement connection pooling, query caching, and intelligent load balancing. The architecture would include automated health checks that trigger failovers when needed. I'd regularly test the failover mechanisms through chaos engineering practices to ensure they work as expected under various failure scenarios. Finally, I'd implement comprehensive monitoring of database performance metrics, replication status, and failover events with appropriate alerting."

Mediocre Response: "I would set up a primary database with multiple read replicas across different availability zones. We'd configure automatic failover so if the primary goes down, one of the replicas would be promoted. We'd also have regular backups and a way to restore to a point in time if needed. We'd need to monitor replication lag and make sure the replicas stay in sync with the primary."

Poor Response: "I'd create a master-slave replication setup with the master handling writes and slaves handling reads. We'd have backups running every day, and if the master fails, we'd promote one of the slaves to be the new master. We could use a load balancer to direct traffic to the appropriate database server. This gives us redundancy and spreads out the load."

6. What strategies would you use to optimize CI/CD pipelines for faster deployments?

Great Response: "I'd begin by analyzing the current pipeline performance using metrics and flamegraphs to identify bottlenecks. For optimization, I'd implement parallelization of independent build and test stages, caching of dependencies, artifacts, and test results, and incremental builds that only process changed files. I'd optimize test execution by categorizing tests (unit, integration, end-to-end) and implementing test pyramids with more unit tests than slower integration tests. I'd set up intelligent test selection that only runs tests affected by code changes and use distributed testing across multiple runners. For infrastructure improvements, I'd use ephemeral environments that spin up only when needed, implement infrastructure-as-code for consistency, and leverage spot instances for cost-effective scaling. I'd also improve developer workflow with trunk-based development, feature flags for safer deployments, and standardized environments using containers. Finally, I'd establish pipeline metrics to continuously measure and improve delivery performance."

Mediocre Response: "I would look at the current pipeline and find the slowest parts. Common optimizations include parallel test execution, caching dependencies, and breaking up the pipeline into smaller steps that can run independently. We could use faster CI/CD runners and optimize our test suite to remove redundant tests. Setting up proper caching of Docker images and build artifacts would help speed things up too."

Poor Response: "I'd focus on running fewer tests in the CI pipeline, saving the more comprehensive tests for a nightly run. We could also increase the resources allocated to our build servers to make them faster. If that's not enough, we could skip certain checks during regular builds and only run them for releases. The goal is to get feedback to developers as quickly as possible."

7. How do you approach troubleshooting a system outage when there's no clear indication of the cause?

Great Response: "I follow a systematic approach starting with rapid assessment of the scope and impact to prioritize efforts. I first check for recent changes—deployments, config changes, or infrastructure updates—that might correlate with the outage. I gather data from multiple monitoring systems, looking at system metrics, logs, and user-reported symptoms to establish a timeline of events. I use the process of elimination by testing hypotheses based on the symptoms, prioritizing the most likely causes first. For complex issues, I implement bisection techniques to narrow down the problem space. I leverage distributed tracing and request sampling to identify patterns in failed requests. Throughout the process, I document findings and coordinate with a cross-functional team if needed, assigning specific investigation areas. If the issue persists, I consider implementing controlled experiments like canary deployments or A/B testing to isolate variables. After resolution, I ensure we update our monitoring to detect similar issues earlier in the future."

Mediocre Response: "I'd start by checking our monitoring dashboards and recent alerts to understand what's happening. I would look at system metrics like CPU, memory, and disk usage to see if anything stands out. Next, I'd check recent deployments or changes that might have caused the issue. I'd look through logs for error messages that could provide clues. If I'm still stuck, I'd involve team members who might have different insights and collaborate on finding the root cause."

Poor Response: "First, I'd restart the affected services to see if that resolves the issue quickly. If not, I'd check if there were any recent code deployments and consider rolling back to the previous version. I'd look at the server logs for obvious errors and check if we're running out of resources like memory or disk space. If I can't figure it out quickly, I'd escalate to more senior team members or the developers who might know the system better."

8. Explain how you would implement an automated backup and recovery system.

Great Response: "I'd design a comprehensive system starting with a clear backup policy defining RPO (Recovery Point Objective) and RTO (Recovery Time Objective) based on business requirements. For implementation, I'd use a combination of full, incremental, and differential backups optimized for each data type. All backups would be automatically encrypted at rest and in transit. I'd implement multi-region storage with at least three copies of critical data across physically separate locations. The system would include automated verification through integrity checks and periodic recovery testing. For databases, I'd capture transaction logs between backups to enable point-in-time recovery. The entire process would be infrastructure-as-code with proper versioning. For monitoring, I'd implement comprehensive metrics tracking successful/failed backups, backup sizes, and recovery time tests. Finally, I'd establish a well-documented recovery playbook with regularly practiced drills to ensure the team can execute recovery procedures under pressure."

Mediocre Response: "I would set up daily automated backups with retention policies based on importance—keeping daily backups for a week, weekly backups for a month, and monthly backups for a year. The backups would be stored in multiple locations, including off-site storage. I'd set up monitoring to alert us if backups fail and implement periodic recovery testing to ensure the backups are actually usable. For databases, we'd use native backup tools along with transaction log backups to minimize data loss."

Poor Response: "I'd configure automated backups to run nightly when the system load is low. We'd keep several weeks of backups and store them on a separate backup server or in cloud storage. I'd make sure the backup system sends alerts if the backups fail. To test the backups, we could restore them to a test environment occasionally to make sure they work properly."

9. How would you approach implementing a zero-downtime deployment strategy?

Great Response: "I'd implement a blue-green deployment strategy with a traffic shifting approach. I'd start by ensuring our application is stateless or has proper state management, with database schema changes designed for backward and forward compatibility using techniques like expand-contract pattern. For the deployment process, I'd set up identical blue and green environments and implement comprehensive health checks that verify both basic connectivity and business functionality. During deployment, I'd use a progressive traffic shifting approach, starting with canary testing (routing a small percentage of traffic to the new version), then gradually increasing traffic while monitoring for errors, latency spikes, or other anomalies. I'd implement automated rollback triggers based on predefined error thresholds. For data consistency, I'd use feature flags to control functionality activation separate from code deployment. The entire process would be automated through CI/CD pipelines with proper validation at each stage. Finally, I'd ensure we maintain sufficient capacity to run both environments simultaneously during the transition."

Mediocre Response: "I would use a rolling deployment strategy where we update one server at a time while the others keep handling traffic. Before deploying, we'd make sure any database changes are backward compatible. We'd implement health checks to verify each server is working properly before sending it traffic. If we detect problems with the new version, we can quickly roll back to the previous version. This approach requires having enough capacity to handle the load even with some servers being updated."

Poor Response: "I'd implement a deployment process where we deploy to servers in batches. We'd take half the servers out of the load balancer, update them, verify they're working, and then put them back in rotation. Then we'd do the same with the other half. We might have a brief period of reduced capacity, but not complete downtime. If something goes wrong, we can quickly roll back to the previous version."

10. What metrics do you consider most important when monitoring a production service?

Great Response: "I focus on a layered approach to metrics. First, I track the four golden signals: latency (including percentiles like p50, p95, p99), traffic (requests per second, concurrent users), error rate (HTTP 5xx, 4xx, application errors), and saturation (resource utilization). Beyond these, I monitor system-level metrics like CPU, memory, disk I/O, and network throughput. For application-specific metrics, I track business KPIs that reflect user experience and success rates of critical transactions. I also implement SLI/SLO-based metrics that align with our service level objectives. For dependencies, I monitor latency and error rates of all external service calls. Additional metrics include queue lengths, thread pool utilization, garbage collection metrics for JVM applications, and database connection pool stats. Finally, I track deployment and operational metrics like deployment frequency, lead time, MTTR, and change failure rate to gauge overall system health and team effectiveness."

Mediocre Response: "I would monitor the availability and response time of the service as the primary metrics. Error rates across different components are important too. On the system level, I'd track CPU usage, memory consumption, disk space, and network I/O. For applications, I'd monitor request rates, success/failure ratios, and latency. Database metrics like connection pool usage and query performance are also important. I'd set up alerts for any metrics that exceed their normal range."

Poor Response: "The most important metrics are CPU usage, memory usage, and disk space to make sure we don't run out of resources. I'd also monitor if the service is up or down and how many requests it's handling. Error rates are important too so we know if users are experiencing problems. If any of these metrics look bad, we know we need to investigate."

11. How would you secure a Linux server that hosts a public-facing application?

Great Response: "I'd implement defense-in-depth starting with network security by using a firewall (iptables/nftables) with default-deny policies, opening only necessary ports, and implementing rate limiting. I'd place the server behind a WAF to protect against common web vulnerabilities. For system hardening, I'd follow CIS benchmarks, including disabling unnecessary services, implementing mandatory access controls with SELinux/AppArmor, and setting up proper file permissions. Authentication would be secured by disabling root SSH login, implementing key-based authentication only, using sudo with limited privileges, and setting up MFA where possible. All software would be kept updated with automated security patches, and I'd implement vulnerability scanning tools. For monitoring and detection, I'd set up HIDS like Wazuh/OSSEC, centralized logging with anomaly detection, and file integrity monitoring. Additionally, I'd implement proper secrets management using tools like HashiCorp Vault, regular security audits, and immutable infrastructure practices where feasible to ensure consistent security posture."

Mediocre Response: "I would start by keeping the system updated with security patches and configure the firewall to only allow traffic on necessary ports. I'd disable root login via SSH and set up key-based authentication instead of passwords. I'd ensure proper file permissions, especially for configuration files and sensitive data. I'd install and configure fail2ban to prevent brute force attacks. For the application, I'd make sure it runs with the least privileges necessary and possibly use a WAF to protect against common web attacks."

Poor Response: "I'd make sure the server has a strong root password and that we keep the software updated. I'd configure the firewall to block unnecessary ports and install an antivirus solution. For the application, I'd make sure it runs behind a load balancer that can help protect against DDoS attacks. I'd also set up regular backups so we can restore quickly if something happens."

12. How would you design a scalable logging solution for a distributed system?

Great Response: "I'd implement a comprehensive solution with multiple layers. For collection, I'd use agents like Fluentd or Filebeat on each host with buffering capabilities to handle network issues. I'd standardize on structured logging with consistent JSON format including trace IDs, service names, and timestamps in UTC. For transport, I'd implement a reliable message queue like Kafka to decouple producers from consumers and handle traffic spikes. The storage layer would use a combination of hot and cold storage—Elasticsearch for recent, searchable logs and S3/GCS with lifecycle policies for long-term archival. For processing, I'd implement log enrichment to add metadata, sampling for high-volume logs, and real-time analysis for anomaly detection. The solution would include centralized configuration management for all logging components and comprehensive monitoring of the logging infrastructure itself. Finally, I'd implement proper access controls with role-based permissions and audit trails for log access, ensuring compliance with data retention policies and privacy regulations."

Mediocre Response: "I would use the ELK stack (Elasticsearch, Logstash, Kibana) or a similar solution. Each service would generate structured logs with consistent fields like timestamp, service name, and severity. We'd use Filebeat or Fluentd to collect logs from different services and ship them to a central Logstash instance for processing. Logstash would parse and enrich the logs before sending them to Elasticsearch. We'd set up index lifecycle management in Elasticsearch to handle log retention and use Kibana for visualization and searching. I'd also set up alerts for important error patterns."

Poor Response: "I'd use a centralized logging system where all applications send their logs to a single place. We could use something like Syslog or directly write to a logging service. The logs would be stored in a database that supports fast searches. We'd need to make sure we don't run out of disk space by implementing log rotation and archiving older logs. Developers could access a web interface to search and filter logs when troubleshooting issues."

13. Explain how you would implement autoscaling for a web application.

Great Response: "I'd implement a multi-dimensional autoscaling strategy. For metrics selection, I'd use a combination of resource metrics (CPU, memory) and application-specific metrics (request queue length, response time) to trigger scaling actions. I'd implement predictive scaling using historical patterns alongside reactive scaling. The architecture would include horizontal scaling for stateless components using instance groups or Kubernetes HPA, vertical scaling for databases or stateful components where appropriate, and automated database read replica management. I'd implement proper warm-up periods for new instances with gradual traffic shifting and pre-warming strategies like keeping a pool of standby instances for sudden traffic spikes. To prevent scaling thrashing, I'd configure appropriate cooldown periods and dampening algorithms. The entire configuration would be infrastructure-as-code with scaling policies defined as code. For monitoring, I'd track scaling events, resource utilization before/after scaling, and cost metrics to continuously optimize the scaling policies."

Mediocre Response: "I would set up autoscaling groups that monitor CPU utilization and request count metrics. When these metrics exceed certain thresholds, like 70% CPU usage or 1000 requests per minute, the system would automatically add more instances. I'd configure proper health checks to ensure new instances are fully operational before receiving traffic. To avoid unnecessary scaling, I'd set cooldown periods between scaling events. I'd also implement predictive scaling for known traffic patterns, like increasing capacity before business hours."

Poor Response: "I'd configure autoscaling based on CPU usage, adding more servers when CPU goes above 80% and removing them when it drops below 20%. I'd make sure the load balancer knows about the new servers automatically. We'd need to set minimum and maximum instance counts to control costs while ensuring we can handle peak loads. The application needs to be stateless so we can add and remove servers without losing user data."

14. How would you approach database performance tuning?

Great Response: "I follow a systematic approach starting with establishing performance baselines and clear goals based on business requirements. For analysis, I use a combination of query performance monitoring tools, slow query logs, and execution plan analysis to identify problematic queries. My optimization strategy addresses multiple layers: schema optimizations including proper indexing strategies, normalized/denormalized design based on access patterns, and partitioning for large tables; query optimizations through rewriting inefficient queries, implementing prepared statements, and query caching where appropriate; configuration tuning specific to the database type (MySQL, PostgreSQL, etc.) including buffer sizes, connection pools, and memory allocation; and infrastructure scaling using read replicas, sharding, or vertical scaling as needed. Throughout the process, I implement observability with comprehensive metrics collection and visualization, focusing on throughput, latency, cache hit rates, and lock contention. Every change is methodically tested and measured against the baseline with a controlled approach—making one change at a time and validating improvements through benchmarking."

Mediocre Response: "I would first identify the slow queries using the database's slow query log and monitoring tools. For these problematic queries, I'd analyze their execution plans to understand what's causing the slowness. Common improvements include adding appropriate indexes, optimizing the queries themselves, and ensuring we're not retrieving unnecessary data. I'd also look at the database configuration settings like buffer sizes and connection pools to make sure they're appropriate for our workload. For tables with lots of writes, I might consider partitioning them to improve performance."

Poor Response: "I'd start by looking at the queries that are taking the longest to run and add indexes to speed them up. If that's not enough, we could upgrade the database server to one with more CPU and memory. For tables that are accessed frequently, we could implement caching to reduce the load on the database. If we're still having problems, we might need to denormalize some tables to reduce the number of joins required."

15. How would you design a resilient microservice architecture?

Great Response: "I'd implement a comprehensive resilience strategy across multiple dimensions. For service isolation, I'd use Kubernetes namespaces or separate clusters, with resource quotas to prevent resource contention. Communication between services would use asynchronous patterns where appropriate and implement circuit breakers, retries with exponential backoff, and rate limiting to handle failures gracefully. For data resilience, I'd implement proper eventual consistency patterns, CQRS where appropriate, and idempotent APIs to handle duplicate requests safely. The system would include comprehensive health checking with both liveness and readiness probes that check dependencies before accepting traffic. For observability, I'd implement distributed tracing, structured logging with correlation IDs, and detailed metrics for each service. The deployment strategy would include canary releases, feature flags, and automated rollbacks. To verify resilience, I'd implement chaos engineering practices with regular failure injection tests. Finally, I'd ensure proper documentation of service dependencies, failure modes, and recovery procedures."

Mediocre Response: "I would design services with clear boundaries and their own databases following the bounded context pattern. Each service would implement circuit breakers to handle dependencies failing and have retry logic with backoff strategies. I'd make sure services are independently deployable and scalable. For communication, I'd use a combination of synchronous REST APIs and asynchronous messaging for different use cases. I'd implement proper health checks and monitoring for each service. Data consistency would be maintained using eventual consistency patterns and compensating transactions where needed."

Poor Response: "I'd break down the application into separate services that can be deployed independently. Each service would have its own database to avoid dependencies. I'd put everything behind a API gateway to route requests to the appropriate service. If one service goes down, the others should continue working. We'd need good monitoring to know when something fails, and we'd implement retry logic to handle temporary failures in dependencies."

16. What approach would you take to diagnose and fix memory leaks in a production application?

Great Response: "I'd implement a methodical process starting with detection and data collection. I'd use monitoring tools to identify memory growth patterns and correlate them with application activities. Once suspected, I'd capture heap dumps at strategic intervals—when memory is normal and when it's high—while minimizing production impact. For analysis, I'd use specialized tools like JProfiler, VisualVM, or language-specific memory analyzers to compare dumps and identify growing object collections and their references. I'd look for dominators (objects preventing garbage collection) and reference paths from GC roots. To confirm findings, I'd create a controlled reproduction environment and validate hypotheses there. For fixing the issue, I'd implement the minimal necessary change, focusing on patterns like unclosed resources, inappropriate caching, or reference mismanagement. Throughout the process, I'd maintain detailed documentation and implement memory usage alerts to catch similar issues earlier in the future. For complex cases, I might implement byte code instrumentation to track object allocations or use allocation profiling to identify hot spots."

Mediocre Response: "I would start by confirming there's a memory leak by monitoring memory usage over time and looking for a pattern of growth that doesn't return to baseline. Then I'd capture heap dumps at different points—when memory usage is low and when it's high—and compare them using tools like MAT or VisualVM. I'd look for objects that are accumulating and not being garbage collected. Once I identify the problematic objects, I'd trace back to the code that's creating them and fix the issue, usually by properly closing resources or fixing reference handling. After deploying the fix, I'd monitor to confirm the memory usage stabilizes."

Poor Response: "I'd use monitoring tools to confirm that memory usage is consistently growing. Then I'd look at recent code changes that might have introduced the leak. I would take a heap dump when memory is high and use analysis tools to see what objects are using the most memory. Based on that, I could identify which part of the code needs fixing. If necessary, we could restart the application more frequently while we work on a permanent fix."

17. How would you approach implementing a disaster recovery plan for critical infrastructure?

Great Response: "I'd develop a comprehensive DR plan starting with a business impact analysis to identify critical systems and establish RPO/RTO requirements for each. Based on this, I'd implement tiered recovery strategies—active-active for the most critical systems with real-time replication across multiple regions, active-passive with automated failover for moderately critical systems, and backup-restore for less critical components. The infrastructure would be completely defined as code with configuration synchronized across environments. For data protection, I'd implement multi-region replication with appropriate consistency models and regular testing of restore procedures. The plan would include detailed recovery runbooks with clear decision trees and escalation paths, automated where possible but with manual circuit breakers for critical decisions. I'd implement comprehensive monitoring to detect disasters early, with automated alerting and dashboards showing system health across regions. Most importantly, I'd establish a regular testing schedule including tabletop exercises, simulated partial failures, and full regional failover drills at least quarterly. After each test or actual incident, we'd update the plan based on lessons learned."

Mediocre Response: "I would start by identifying our critical systems and determining acceptable recovery time and point objectives (RTO/RPO). Based on these requirements, I'd implement appropriate backup strategies—from regular snapshots to continuous replication depending on importance. I'd set up infrastructure in a secondary region capable of running our critical workloads and create detailed recovery playbooks for different disaster scenarios. We'd need to regularly test the recovery procedures through planned drills, at least quarterly. I'd also ensure there's a clear communication plan for during outages, with defined roles and responsibilities."

Poor Response: "I'd make sure we have regular backups of all our data and configurations stored in a different location than our primary systems. I'd document the steps needed to restore services and create recovery scripts where possible. We should have spare capacity available that we can quickly deploy to if needed. We'd test the recovery plan occasionally to make sure it works. During an actual disaster, we'd follow our documented procedures to get systems back online as quickly as possible."

18. Explain your approach to container orchestration and how you would manage a Kubernetes cluster.

Great Response: "My approach to Kubernetes management combines infrastructure automation, security hardening, and operational excellence. I'd implement the cluster using infrastructure-as-code tools like Terraform or Pulumi with a multi-environment promotion pipeline. For security, I'd follow the principle of least privilege with RBAC, implement network policies by default, use Pod Security Standards, scan images in CI/CD, and regularly audit cluster access. For operational management, I'd implement GitOps using tools like Flux or ArgoCD for declarative configuration, use Helm or Kustomize for templating, and establish namespace governance with resource quotas and limit ranges. Monitoring would include comprehensive observability with Prometheus for metrics, distributed tracing, and centralized logging with context preservation. For reliability, I'd implement pod disruption budgets, anti-affinity rules, topology spread constraints, and proper resource requests/limits. I'd use tools like Velero for backup/restore and implement disaster recovery procedures. For ongoing maintenance, I'd establish a regular update strategy for both Kubernetes versions and node images, preferably using a blue-green approach for control plane upgrades and rolling updates for worker nodes."

Mediocre Response: "I would use a managed Kubernetes service when possible to reduce operational overhead. For cluster configuration, I'd use infrastructure as code tools like Terraform or CloudFormation. I'd organize applications using namespaces based on teams or environments and implement RBAC for access control. For deployments, I'd use CI/CD pipelines with Helm charts for templating. I'd set up monitoring with Prometheus and Grafana, and centralized logging with the EFK stack. For cluster maintenance, I'd implement regular update procedures and use node pools to manage different workload requirements."

Poor Response: "I'd set up a Kubernetes cluster with enough nodes to handle our workloads. I'd use kubectl and YAML files to deploy applications and make sure they have the right resource allocations. For monitoring, I'd install dashboards that show pod status and resource usage. When we need to update applications, I'd use rolling updates to avoid downtime. If the cluster gets overloaded, we can add more nodes through the cloud provider's console."