Top 30 Most Common Aws Interview Questions And Answers For Experienced Scenario Based You Should Prepare For
Top 30 Most Common Aws Interview Questions And Answers For Experienced Scenario Based You Should Prepare For
How would you migrate a legacy on-premise application to AWS with minimal downtime?
Direct answer: Plan a phased migration—assess, replicate data, use blue/green or canary cutovers, and automate rollback to keep downtime minimal.
Set up parallel infrastructure in AWS (VPC, subnets, security controls).
Use data replication: AWS Database Migration Service (DMS) for RDBMS, DataSync or S3 replication for files.
Use blue/green or canary deployments with Route 53 weighted routing or ALB target groups.
Use feature flags and phased traffic shifting to validate behavior.
Automate CI/CD and infrastructure as code (CloudFormation/Terraform) so rollbacks are fast and repeatable.
Expand: Start with a thorough discovery (inventory apps, dependencies, network, data size). Choose a migration pattern—rehost (lift-and-shift) for speed, replatform or refactor for long-term value. For near-zero downtime:
Example: For large stateful apps, replicate databases continuously, promote a read replica in AWS to primary only when application-level readiness checks pass. For very large data sets, ingest via Snowball or Snowball Edge, then validate in the cloud.
Why it matters for interviews: Show you can balance speed, risk, and business continuity with concrete tools and rollback plans—interviewers want that operational certainty.
(See practical migration scenarios in CloudZero’s AWS interview scenarios and Curotec’s migration Q&A for reference.)
How do you design an AWS architecture for high availability and fault tolerance?
Direct answer: Use multi-AZ for resilience, multi-region for disaster recovery, stateless services, autoscaling, and managed services that handle failover.
Distribute across Availability Zones (AZs) at minimum; for critical systems, replicate across regions.
Keep services stateless where possible (store state in ElastiCache, DynamoDB, or RDS with Multi-AZ).
Use Elastic Load Balancers, Auto Scaling Groups, and health checks to detect and replace failed instances.
Prefer managed services with built-in high availability (Aurora Multi-AZ/Global DB, S3, DynamoDB).
Design for graceful degradation—serve partial functionality if some systems fail.
Implement cross-region replication for backups and storage (S3 Cross-Region Replication, Aurora Global DB).
Expand: Design principles:
Example: For global microservices, use regional clusters with a global control plane (Route 53 latency-based routing + health checks) and secure inter-region communication via Transit Gateway, VPC peering, or PrivateLink, encrypted with KMS-managed keys.
Takeaway: In interviews, describe concrete redundancy choices, trade-offs (cost vs RTO/RPO), and how you validate failover with runbooks and DR drills.
(Architectural best practices referenced by Curotec and Turing’s AWS guides.)
How would you investigate and resolve unexpected EC2 cost spikes?
Direct answer: Triage with Cost Explorer, CloudWatch, and resource tags; then remediate by stopping unused resources, rightsizing, or fixing autoscaling/config errors.
Immediate triage: Use AWS Cost Explorer and Cost & Usage Reports to find which service/region causes the spike.
Identify resource culprits: filter by tags, account, region. Check EC2 instance hours, EBS volume usage, snapshot frequency, NAT gateway or data transfer charges.
Check operational causes: look for runaway processes, faulty CI/CD that spawns instances, misconfigured Auto Scaling policies, or unintentional on-demand instances.
Remediate: stop or terminate idle instances, detach/clean orphaned EBS volumes, consolidate snapshots, enforce lifecycle policies, enable instance hibernation or use Spot instances where appropriate.
Longer-term controls: implement tagging standards, set budgets/alerts, use AWS Trusted Advisor, Savings Plans/Reserved Instances for steady-state, and CI visibility to avoid repeat spikes.
Expand: A structured investigation:
Example: If EBS snapshots are unexpectedly frequent, disable automated snapshot policies temporarily and audit the backup system.
Takeaway: In interviews, show how you combine tooling, governance, and operational fixes—employ both immediate controls and longer-term cost guardrails.
(CloudZero’s cost-exploration scenarios and SecondTalent’s cost-optimization questions are useful references.)
How do you respond to a security incident related to compromised IAM credentials?
Direct answer: Immediately revoke credentials, isolate affected systems, collect forensic data from CloudTrail, then follow containment, eradication, and recovery with a post-incident review.
Detection & Alerts: Use CloudTrail, GuardDuty, and CloudWatch to detect unusual API calls or new access patterns.
Containment: Revoke or rotate compromised access keys and credentials, enforce password change/MFA, disable the user or role until validated.
Forensic collection: Preserve logs (CloudTrail, VPC flow logs, Config history), snapshots, and relevant alerts for root-cause analysis. Avoid wiping evidence prematurely.
Eradication: Remove backdoors, validate EBS AMIs, patch systems, and rotate secrets in Secrets Manager.
Recovery: Recreate credentials with least privilege, validate permissions, and restore services from hardened images or backups.
Postmortem: Update IAM policies to enforce least privilege, adopt role-based access patterns, and add guardrails (AWS Organizations SCP, permission boundaries).
Expand: Incident response steps:
Best practices: Prefer IAM roles for EC2/ECS tasks instead of long-lived keys, use short-lived credentials with STS, enable MFA and strong password policy, and implement service control policies for critical accounts.
Takeaway: For interviews, demonstrate you know both the tactical steps (revoke, isolate, analyze) and strategic controls (least privilege, IAM roles, monitoring).
(See security and IAM guidance in Curotec and NetCom Learning’s AWS security Q&A.)
When should I choose Fargate vs ECS vs EKS for container workloads on AWS?
Direct answer: Use Fargate for serverless container runs with minimal infra management, ECS for simple AWS-native container orchestration, and EKS when you need Kubernetes portability and ecosystem tools.
Fargate: No server management, per-second billing for tasks, faster ops, ideal for small teams or unpredictable workloads. Limits on low-level control may affect advanced networking or performance tuning.
ECS: AWS-managed scheduler with deep AWS integration. Lower operational overhead than bare EC2 while allowing more control than Fargate (when using EC2 launch type). Good for teams invested in AWS-centric tooling.
EKS: Full Kubernetes control and portability. Best when you require Kubernetes ecosystem features (Helm, custom operators) or multi-cloud consistency. Requires more operations skill and potential cost for control plane and worker management.
Expand with trade-offs:
Example: If you need to run thousands of short-lived tasks and want zero infra management, Fargate is attractive. For complex, tuned network policies and custom schedulers across clusters, EKS is the right choice.
Takeaway: Interviewers expect you to weigh developer velocity, control, cost, and team maturity when recommending a container platform.
(Curotec and SecondTalent both include reasoning-style questions comparing these services.)
What are the top considerations for migrating a petabyte-scale data warehouse to Redshift?
Direct answer: Plan staged ingestion, use S3 as the landing zone, pick the right Redshift node type (RA3 for large storage), and optimize distribution, sort keys, and compression to control performance and cost.
Ingestion strategy: For very large volumes, move raw data into S3 first (or use Snowball/Snowball Edge), then use Redshift COPY with optimized file sizes and parallel loads.
Node sizing: RA3 nodes decouple compute and storage for better cost control at petabyte scale.
Schema and distribution: Choose distribution keys and sort keys to minimize data movement during joins, use compression encodings, and vacuum/analyze regularly.
Hybrid access: Use Redshift Spectrum for querying S3 data without loading it all into the cluster.
Parallelize and test: Use batch windowing and parallel COPY operations; run realistic performance testing and cost modeling to size concurrency scaling.
Expand:
Example: For a petabyte data lift, ship cold historical data with Snowball, ingest recent delta via DataSync, land in S3, then COPY into Redshift using compressed, columnar file formats (Parquet) for efficiency.
Takeaway: Demonstrate you can balance ingestion logistics, query performance, and cost—critical topics for data roles in interviews.
(See Curotec and Turing for data migration examples and practical advice.)
How do you explain DynamoDB consistency models and design for performance in interviews?
Direct answer: DynamoDB offers eventual consistency by default and strongly consistent reads as an option; design tables with access patterns, partition keys, and adaptive capacity to achieve performance.
Consistency models: Eventually consistent reads may show stale data briefly but are faster and less costly; strongly consistent reads always return the most recent write but consume more read capacity.
Table design: Model access patterns first—use appropriate partition and sort keys, avoid hot partitions by sharding keys, and use GSIs/LSIs where necessary.
Capacity: Use on-demand capacity for unpredictable workloads and provisioned + auto-scaling for predictable traffic to control cost.
Features: Use DynamoDB Streams for change data capture, DAX for read caching, and TTL for data lifecycle management.
Expand:
Example: If you expect a single hot partition (e.g., user ID 0), consider adding a suffix-based sharding scheme to distribute load across partitions.
Takeaway: In interviews, pair theory (consistency types) with specific table design choices and trade-offs for scalability.
(Turing and Curotec cover DynamoDB best-practices and common interview prompts.)
How should I prepare for scenario-based AWS interview questions?
Direct answer: Practice structured storytelling (STAR or CAR), quantify outcomes, draw architectures, and rehearse trade-offs—cost, security, performance—for each decision.
Frameworks: Use STAR (Situation, Task, Action, Result) or CAR (Context, Action, Result) to present clear, repeatable answers.
Technical depth: Be ready to diagram architectures, justify service choices, and explain failure modes and mitigations.
Trade-offs: Interviewers want to hear about constraints (budget, timeline), alternative solutions you considered, and why you chose one.
Practice: Use mock scenario questions—migrate a legacy app, investigate a cost spike, remediate a breach—and time your responses to be concise yet complete.
Resources: Curate a prep set across topics—migration, IAM/security, cost optimization, service comparisons, and databases—from authoritative sources and hands-on labs.
Expand:
Example: For "migrate a legacy app with minimal downtime," outline assessment, data replication plan, cutover strategy, and rollback criteria with metrics (RTO/RPO).
Takeaway: Show both methodical thinking and practical, measurable outcomes during interviews to stand out.
(Review role-based interview questions and preparation strategies at Turing and NetCom Learning.)
How Verve AI Interview Copilot Can Help You With This
Verve AI acts as a quiet co‑pilot during live interviews—analyzing the question context, suggesting structured response outlines (STAR/CAR), and offering phrasing so you stay concise and confident. With Verve AI Interview Copilot you get real-time cues for technical depth, trade-offs, and follow-up prompts tailored to scenario-based AWS questions. Verve AI helps reduce fumbling by giving quick reminders of key points (security, cost, fallback), so you can focus on delivery and clarity.
Takeaway: Use a tool like this to reinforce structure and composure under pressure.
What Are the Most Common Questions About This Topic
Q: Can Verve AI help with behavioral interviews?
A: Yes — it uses STAR and CAR frameworks to suggest concise, structured responses in real time for behavioral prompts.
Q: How should I practice AWS migration scenarios?
A: Break them into assessment, replication, cutover, and rollback; run tabletop exercises and mock sessions under time pressure.
Q: What’s the fastest way to find cost spikes in AWS?
A: Use AWS Cost Explorer with tags, enabled Cost & Usage Reports, and correlate with CloudWatch metrics and recent deployments.
Q: Should I learn Terraform or CloudFormation first?
A: Learn IaC concepts; pick Terraform for multi-cloud and portability, CloudFormation for deep AWS-native features and service integrations.
Q: How do I prepare for database design questions like Redshift or DynamoDB?
A: Practice schema modeling for access patterns, compression/distribution keys (Redshift), and partitioning/consistency (DynamoDB); run hands-on tests.
(Each answer is crafted to be direct and actionable so you can use them quickly in study sessions.)
Conclusion
Recap: Experienced AWS interviewers ask scenario-based questions that test judgment across migration planning, security incident response, cost optimization, service trade-offs, and data architecture. Use structured frameworks (STAR/CAR), quantify outcomes, and prepare concrete tooling examples to show both technical mastery and business awareness.
Final nudge: Preparation plus structured delivery builds interview confidence—try Verve AI Interview Copilot to practice responses, get context-aware prompts, and feel ready for scenario-based AWS interviews.
Practical interview scenarios and cost investigations at CloudZero.
Comprehensive AWS Q&A and migration/security scenarios from Curotec.
Role-based AWS interview questions curated by Turing.
Cost and systems design perspectives at SecondTalent.
Security and role-focused interview tips from NetCom Learning.
Further reading and references: