DAY 1

No—you are not lazy. 😄

My previous list was a question bank, not a realistic one-day study plan. Trying to prepare 280 questions today would be counterproductive. Your own master prompt says the goal is interview readiness, not comprehensive knowledge, and specifically says not to overwhelm you with hundreds of questions.

For Day 1, I would cut it down aggressively.

Day 1: The Minimum Viable Preparation

I recommend 30 questions total.

Not 100. Not 280.

And even these 30 are designed around 7 reusable stories, so you are really preparing a small number of underlying answers rather than 30 unrelated answers.

🔴 Block 1 — Your Personal Story: 5 questions

These are almost guaranteed territory.

1. Tell me about yourself.

2. Walk me through your career progression.

3. Why AWS?

4. Why this Solutions Architect role?

5. Why should AWS hire you for this role?

If you nail these five, the interviewer immediately understands your positioning as an AI + Cloud Solutions Architect, rather than seeing you primarily as a DevOps/Kubernetes engineer.

🔴 Block 2 — Your #1 Story: Autonomous Engineering Platform

6 questions

Your resume gives us a particularly strong anchor here: an enterprise autonomous engineering platform combining agentic AI, multi-cloud infrastructure, Kubernetes, Terraform, GitOps, security, governance, FinOps and observability.

6. Tell me about your Enterprise Autonomous Engineering Platform.

7. What problem were you solving and why did you need AI agents?

8. Walk me through the architecture end-to-end.

9. What was your specific contribution?

10. What were the biggest technical challenges and tradeoffs?

11. How did you make autonomous infrastructure changes safe?

These six questions are incredibly reusable.

For example, Q11 can lead into:

• governance
• human-in-the-loop
• security
• GitOps
• policy
• failure handling
• autonomous agents

🔴 Block 3 — RAG / Enterprise GenAI

4 questions

Your resume already supports a solid RAG story involving ingestion, chunking, embeddings, vector search, retrieval, prompt orchestration, LLM generation, security and observability.

12. Tell me about the Enterprise GenAI/RAG platform you designed.

13. Why did you choose RAG, and how did the architecture work?

14. How did you address security, PII and enterprise data protection?

15. How did you evaluate and monitor the quality of the system?

That's enough for Day 1.

We can do the AWS-specific RAG service mapping on Day 2.

🔴 Block 4 — AI Governance

3 questions

Your resume specifically mentions Policy-as-Code covering identity, security, data residency, approved services, infrastructure, model usage and cost controls.

16. Tell me about your AI governance architecture.

17. How did you implement Policy-as-Code and what policies did you enforce?

18. How would you make an AI agent safe enough for an enterprise environment?

Again, these three questions can generate a huge number of follow-ups.

🔴 Block 5 — Business Impact

4 questions

You have unusually useful metrics on the resume, so we need to be able to defend them.

19. Tell me how you achieved the 80% reduction in LLM inference costs.

20. How did you measure the 80% improvement?

21. Tell me how you achieved the 45% reduction in infrastructure provisioning time.

22. How did you measure the 45% improvement?

Your resume also contains 35% lower manual engineering intervention, 25% lower network latency, 20% improvement in incident resolution and 40%+ MTTR reduction.

You don't need separate stories for all of them.

🟠 Block 6 — Customer & Leadership

4 questions

This is important because AWS is hiring you as a customer-facing Solutions Architect, not just as an AI engineer. The JD emphasizes discovery workshops, CTO/engineering engagement, technical scoping and long-term customer relationships.

23. How do you approach a customer discovery workshop?

24. How do you translate a business problem into an architecture?

25. Tell me about a time you disagreed with a stakeholder or customer.

26. Tell me about a time you influenced people without having direct authority.

🟡 Block 7 — Difficult Questions

Only 4.

27. Tell me about a failure or project that didn't go as planned.

28. Tell me about an architecture decision you would change today.

29. What is your biggest weakness?

30. The role asks for 10+ years of experience. Your resume shows around 8 years. Why do you believe you're ready for this role?

The last one is particularly important because we should prepare an honest, confident answer rather than trying to explain away the difference. Your master prompt explicitly identifies this as something we should handle accurately.

That's It. Seriously.

Your Day 1 target is 30 questions.

But here's the important part:

You're not memorizing 30 answers.

We're going to create approximately 7 core stories:

                    YOUR AWS INTERVIEW
                           │
           ┌───────────────┴───────────────┐
           │                               │
      PERSONAL STORY                  TECHNICAL STORIES
           │                               │
     5 questions                   ┌───────┼────────┐
                                   │       │        │
                               Autonomous  RAG   Governance
                                   │       │        │
                                   └───────┼────────┘
                                           │
                              Business Impact Stories
                                   │          │
                                80% cost    45% IaC

                              + Customer/Leadership

One good story can answer 10+ different interview questions.

How Much Time Do You Need?

I'd structure Day 1 like this:

You don't necessarily need to do it all in one sitting. 6–7 focused hours is already a serious preparation day.

And if you only have 4 hours, we can compress it.

The More Important Point

I actually don't want you to "prepare" 30 polished answers.

I want you to reach this state:

Question → recognize which story applies → tell story → handle follow-up → explain tradeoff → explain result.

That's much closer to what the real interview will feel like.

For example:

Interviewer: "Tell me about a time you dealt with an ambiguous problem."

You don't need a memorized answer.

You think:

Ambiguity → Autonomous Engineering Platform → Situation → Action → tradeoff → result.

Another question:

"Tell me about a complex architecture."

Same story.

"Tell me about innovation."

Same story.

"Tell me about AI agents."

Same story.

"Tell me about automation."

Same story.

"Tell me about security in autonomous systems."

Same story, different slice.

That's why the 7-story approach in your master prompt is the right strategy.

My recommendation

Don't study anything else right now.

DAY 1 = 30 questions + 7 stories.

And I would make Questions 1–11 our first session, because your personal narrative + Autonomous Engineering Platform are the highest-leverage part of your preparation.

If you're ready, we'll start with Q1: "Tell me about yourself." You answer naturally, and I'll coach you like an AWS interviewer—not give you a canned answer to memorize.

DAY 2

Yes. If Day 1 = your story + behavioral + customer/leadership, then Day 2 should be AWS technical architecture + GenAI deep dive + mock interview.

And I would keep Day 2 to ~30 core questions, not hundreds.

Day 2 — 30 Core Questions

🔴 Block 1 — Amazon Bedrock & GenAI Fundamentals — 6

1. What is Amazon Bedrock, and why would an enterprise use it?

2. How would you select an LLM/model for an enterprise workload?

3. How do you manage model versioning and prevent unexpected model changes in production?

4. What are the major security concerns when building GenAI applications on AWS?

5. How would you control LLM costs in a production enterprise application?

6. How would you design a production-grade GenAI application rather than a PoC?

🔴 Block 2 — RAG — 5

The JD explicitly calls out Knowledge Bases for Bedrock, OpenSearch Serverless and Aurora PostgreSQL/pgvector, while your resume already gives you a RAG architecture story.

7. Design an enterprise RAG architecture on AWS.

8. When would you use RAG instead of fine-tuning?

9. How would you choose between Knowledge Bases for Bedrock, OpenSearch Serverless and Aurora PostgreSQL/pgvector?

10. A RAG application is producing poor answers. How would you troubleshoot it?

11. How would you secure RAG so users can only retrieve documents they are authorized to see?

🔴 Block 3 — Agents / AgentCore / Strands / MCP — 6

This is probably the most important new technical area relative to your existing experience because the JD explicitly emphasizes agentic systems, AgentCore, Strands Agents and MCP.

12. What is an AI agent, and how is it different from a traditional LLM application?

13. When should you use an agent versus a conventional workflow?

14. How would you design a multi-agent system on AWS?

15. What is Amazon AgentCore, and where does it fit in an agentic architecture?

16. What are Strands Agents, and how would you use them?

17. What is MCP, and why is it useful for enterprise agents?

🔴 Block 4 — Agent Security & Governance — 4

Your resume already has a strong foundation here through Policy-as-Code, identity, security, data residency, model usage and cost controls.

18. How would you secure an AI agent that can call enterprise tools?

19. How would you use Bedrock Guardrails in an enterprise architecture?

20. What are Automated Reasoning Checks, and where would you use them?

21. How would you design an audit-ready GenAI architecture for a regulated enterprise?

Think:

identity → data → model → guardrails → actions → logging → audit

🔴 Block 5 — Production AI / Evaluation / Observability — 4

The JD specifically calls for eval frameworks, LLM-as-judge, observability, auditability and production hardening.

22. How would you evaluate an LLM application before production?

23. What is LLM-as-a-judge, and what are its limitations?

24. What metrics would you monitor for a production GenAI application?

25. How would you troubleshoot an AI application with high latency and high cost?

🟠 Block 6 — AWS Architecture & IaC — 3

Your resume already demonstrates Terraform/CloudFormation and reusable deployment patterns, while the JD specifically calls for AWS CDK.

26. How would you deploy a production GenAI platform using IaC?

27. When would you choose CDK vs CloudFormation vs Terraform?

28. How would you design the networking, IAM, encryption and secrets architecture for an enterprise GenAI workload?

🟠 Block 7 — Final Architecture Challenge — 2

29. Design an enterprise AI platform for a regulated Indian customer from discovery through production.

You should cover:

Requirements → architecture → Bedrock → RAG → agents/MCP → security → Guardrails → data residency → evaluation → observability → IaC → cost → operations

30. A customer says: "We want an autonomous AI platform that can analyze our applications, modify code, provision infrastructure and deploy to production without humans." How would you architect it?

This question intentionally combines almost the entire JD with your Enterprise Autonomous Engineering Platform story.

The Day 2 Priority

If you're short on time, don't even treat all 30 equally.

🔥 Must-master: 15

1, 6, 7, 8, 9, 10, 12, 14, 15, 16, 17, 18, 19, 22, 29

Those are the questions I'd spend the majority of Day 2 on.

Then know conceptually:

2–5, 11, 13, 20–21, 23–28, 30

And One Important Change From Day 1

On Day 1, I want you to answer primarily from your actual experience.

On Day 2, I want two layers in every answer:

"Here's what I have actually done."

followed by:

"Here's how I would translate that into an AWS-native architecture for this customer."

That's particularly important because your background is multi-cloud, while this role is heavily centered on AWS's GenAI stack. Your resume explicitly covers AWS, Azure and GCP, Bedrock, RAG, agents, governance, Kubernetes, Terraform and FinOps.

So we're not trying to pretend you already implemented every new AWS capability named in the JD.

We're demonstrating that you understand the underlying architecture and can map your existing experience into AWS.

Your 2-Day Preparation in One Line

Day 1: 🧑‍💼 "Who is Kaustubh and what has he actually accomplished?"

Day 2: ☁️ "Can Kaustubh architect this on AWS and intelligently defend his decisions?"

That's enough.

You do not need to learn all of AWS in two days. You need to become exceptionally good at defending the architecture you already understand and mapping it to the AWS services the JD names.