DWG‑01 · INTERVIEW PREP SET

Senior Solution Architect — Data & AI

Client: Sonata Software Via: Talentvis Malaysia Location: MY / SG · Remote Rounds: 3 Language: Bahasa Melayu required
0 / 0 mastered
01 · ROADMAP
02 · DATA STACK
03 · FOUNDRY
04 · Q&A DRILL
05 · GLOSSARY
06 · SOFT SKILLS
ROUND 1 · RECRUITER / HR FIT

Background, motivation & logistics

Likely with Talentvis / Sonata HR · 30–45 min

Screens for narrative fit, remote-work discipline, and the mandatory Bahasa Melayu fluency — not deep tech.

  • 2-minute career walkthrough that ends on why data+AI architecture, now
  • Confirm comfort with 100% remote, client-facing work
  • Be ready to switch into Bahasa Melayu mid-answer if asked
  • Have a salary range and notice period ready
SAMPLE QUESTIONS + MODEL ANSWERS
  • Why Sonata / this role?
    Global modernization-engineering firm, deep Microsoft & Snowflake partnership; role blends data architecture with applied AI — my exact growth direction.
  • How do you stay disciplined fully remote with customers?
    Structured calendar blocks, async status updates, camera-on/agenda-set for every call — treat it like being in the room.
  • Led a customer through a tough technical decision?
    STAR: conflicting requirement → ran a trade-off workshop with stakeholders → decision adopted → quantify the outcome.
ROUND 2 · TECHNICAL DEEP DIVE

Hiring manager / principal architect grill

Whiteboard or verbal architecture questions · 60 min

Tests real depth across the Azure data stack, and — the differentiator for this role — whether you can connect data platforms to AI use cases, not just describe each in isolation.

  • Quantify past projects: data volume, users, SLAs, team size
  • For every data answer, add "and here's how I'd layer AI on top"
  • Be honest about breadth vs. depth — JD explicitly says you don't need to be equally strong everywhere
SAMPLE QUESTIONS + MODEL ANSWERS
  • Fabric platform for near-real-time retail inventory?
    Event Hubs/ADF ingest → OneLake landing zone → Fabric Lakehouse on Direct Lake → Power BI live dashboards; Data Activator fires stock-out alerts.
  • Securely ground Azure OpenAI on SQL/Fabric data?
    RAG: index data in Azure AI Search (hybrid/vector), enforce row-level permission trimming, feed only retrieved context to the model, apply content filters.
  • Cosmos DB partition-key strategy for high writes?
    Pick a high-cardinality key matching the main query pattern (e.g. customerId) to spread load; avoid low-cardinality keys like date that create hot partitions.
  • Databricks or Fabric Spark — and why?
    Databricks for heavy MLOps/MLflow or multi-cloud; Fabric Spark to stay Microsoft-native with unified governance and less ops overhead.
ROUND 3 · CASE STUDY / CLIENT PRESENTATION

Simulated customer engagement

Leadership + possible client stakeholder · 45–60 min

You'll likely present a solution to a fictional customer brief. Judged on structure, business framing, and how you handle pushback — the actual day-to-day of the role.

  • Structure: Discover → Design → Validate (POC/MVP) → Roadmap
  • One simple diagram beats ten technical bullet points
  • Tie every component to a business outcome or risk it removes
  • Expect at least one "what if the budget/timeline is cut" curveball
PRACTICE PROMPT + ANSWER SCAFFOLD
  • "A logistics company wants to modernize its on-prem SQL Server estate and add an AI copilot for support tickets. Present your architecture and a 90-day roadmap."
  • Scaffold:
    Discover — current SQL Server estate, ticket volume, pain points.
    Design — Azure SQL Managed Instance migration + Azure AI Search over ticket history + Azure OpenAI copilot grounded via RAG.
    Validate — 2-week POC on one ticket category.
    Roadmap — Days 1–30 migrate/assess, 31–60 build+POC, 61–90 pilot with one support team, then scale.
Tap an item once you can explain it out loud in under 30 seconds without notes. Progress is saved on this device.
Core (deep expertise expected)
Strong working knowledge
Additional exposure
Heads up: "Azure AI Foundry" was rebranded to Microsoft Foundry in 2026. The JD's terminology (Azure AI Foundry, Agent Service, Semantic Kernel, Azure AI Search) is still accurate — just know the newer name and features below so you sound current.
What changed / what's new
Rebrand
Azure AI Foundry → Microsoft Foundry: one platform to build, evaluate, deploy and govern both models and agents.
Agent Service
Managed, enterprise-grade runtime for hosting agents — identity, memory, tool-calling, observability and safety built in, no containers to run yourself.
Hosted agents
Session-isolated managed runtime for production agents, reaching general availability mid-2026 — the "just deploy it" tier.
Foundry IQ
Unified knowledge/retrieval layer connecting agents to Work IQ, Fabric IQ, Azure SQL, file search and MCP sources behind one SLA-backed endpoint — cuts custom RAG plumbing.
Memory
Agent Service now supports session, user and procedural memory — the latter lets an agent get better at a task across repeated runs.
Toolboxes
Curated, intent-based sets of tools (APIs, MCP servers, functions) an agent can call, managed centrally for governance and reuse.
MCP support
Model Context Protocol lets agents call remote or custom tool servers (e.g. Azure DevOps MCP) through one consistent interface — 1,400+ MCP-enabled tools in the catalog.
Entra Agent ID
Agents get their own governed identity in Microsoft Entra — same authentication/permission/audit model as users and service principals.
Governance & eval
ASSERT, Agent Control Specification (ACS), Guided Guardrail Setup and Rubric move evaluation and trust checks into the dev loop, not just post-hoc.
How to position it in the interview

Copilot Studio vs. Foundry: Copilot Studio is low-code, for business users building copilots inside Microsoft 365. Foundry is for developers — code-first, custom, multi-agent, enterprise-grade.

Semantic Kernel vs. Agent Service: Semantic Kernel is the open-source orchestration SDK you write code with; Agent Service is where you deploy and run agents (built with Semantic Kernel or any other framework) in production.

The "so what" for this JD: your Azure data platform expertise (Fabric, SQL, Cosmos DB) is exactly what Foundry IQ needs to ground agents in real enterprise data — frame every data answer as the foundation an AI agent will sit on top of.

CATEGORY
Question
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ANSWER
Answer
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This role is customer-facing by design — the JD says you'll lead workshops, present to business leaders, and run POCs face-to-face with clients. Interviewers will weigh how you communicate as heavily as what you know.
SELF-INTRODUCTION · ~90 SECONDS

Present → Past → Future script

Fill in your own details, then rehearse until it's automatic — not memorized word-for-word.
  • Open (10s): "Hi, I'm [name], a Solution Architect with [X] years focused on Azure data platforms, and more recently applied AI."
  • Present (20s): "Right now at [current company], I [what you do day-to-day — e.g. design Azure data architectures for enterprise clients, covering SQL, Fabric, and integration with AI]."
  • Past (30s): "Before that, I [1–2 concrete highlights with a number — e.g. led a Fabric migration that cut reporting time by 40%, or built a Cosmos DB platform handling X requests/sec]."
  • Why this role (20s): "What draws me to Sonata is combining deep Azure data architecture with hands-on AI solutioning in a customer-facing role — that's exactly the direction I want to grow in."
  • Close (10s): "Happy to go deeper into any part of that."
Core soft skills this JD is testing for
Discovery
Asking the right questions to surface a customer's real priorities, not just their stated request.
Facilitation
Running a workshop so every voice in the room is heard and the session ends with a clear decision.
Translation
Converting technical trade-offs into business language — cost, risk, speed — without losing accuracy.
Objection handling
Responding to pushback with a calm, specific answer instead of getting defensive or over-explaining.
Executive presence
Staying concise and confident under pressure — leading with the answer, then the reasoning if asked.
Active listening
Reflecting back what a stakeholder said before responding, so they feel heard and you avoid solving the wrong problem.
Remote discipline
Camera on, clear agendas, and proactive written follow-ups — since there's no hallway to catch up in.
Cross-cultural fluency
Comfortably switching register — and language — between Malaysian/Singaporean business stakeholders.
QUICK TIPS

Before you walk in

  • Use STAR (Situation, Task, Action, Result) for every behavioral answer — end on a number if you can.
  • Match the interviewer's technical depth: go deep with an architect, stay outcome-level with HR or a client.
  • Have 2–3 specific reasons "why Sonata" ready — generic answers stand out for the wrong reason.
  • Rehearse your intro once in Bahasa Melayu too — even a short bilingual switch signals genuine fluency.
  • End every answer with a natural stopping point; don't trail off or over-explain past the point.