Structured Problem Solving and Frameworks Questions
Assessment of a candidate's ability to apply repeatable, logical frameworks to break ambiguous problems into manageable components, identify root causes, weigh options, and recommend a defensible solution with an implementation plan. Topics include defining the problem and success criteria, gathering context and constraints, decomposing the problem using mutually exclusive collectively exhaustive thinking, generating alternatives, evaluating trade offs by impact and effort, and sequencing execution. Interviewers will look for clear narration of the thinking process, use of data and evidence, awareness of assumptions, and the ability to adapt a framework to different domains such as product, operations, or analytics. This canonical topic also covers systematic analysis techniques, methodological rigor, and presentation of conclusions so others can follow and act on them.
MediumTechnical
41 practiced
A client runs a Java monolith with 10 TB of data and limited engineering resources; they need to move to AWS within 2 months. Propose three alternative migration approaches (for example: lift-and-shift, replatform, selective refactor), evaluate each approach across cost, time-to-value, operational complexity, data migration risk, and rollback difficulty, and recommend the most defensible option given the constraints.
Sample Answer
Situation: Client has a Java monolith with 10 TB of data, limited engineering resources, and a hard 2‑month deadline to move to AWS.Approach 1 — Lift-and-shift (Rehost)- What: Move VMs/containers and attached storage to EC2/EBS (or VM Import), keep app unchanged.- Cost: Moderate short‑term (migration tools, bandwidth, larger EC2/EBS bills); lower dev cost.- Time-to-value: Fastest — can be done within weeks.- Operational complexity: Low initially (same app ops), but may miss cloud optimizations.- Data migration risk: Medium — bulk transfer of 10 TB manageable (AWS Snowball if bandwidth constrained).- Rollback difficulty: Low — keep snapshot-based rollback to on-prem if needed.Approach 2 — Replatform (lift, tweak)- What: Move to AWS managed services (e.g., RDS/Aurora for DB, EKS/ECS for app) with minimal code changes.- Cost: Higher (migration effort, managed service costs) but better medium-term savings.- Time-to-value: Medium — likely at edge of 2 months depending on changes and testing.- Operational complexity: Medium — reduces some operational burden but requires migration expertise.- Data migration risk: Medium-high — schema/compatibility changes and cutover coordination for 10 TB.- Rollback difficulty: Medium — fallback requires careful data sync; not as trivial as rehost.Approach 3 — Selective refactor (Strangler pattern)- What: Incrementally extract services, move parts to cloud-native stacks.- Cost: Highest up front (engineering time), long-term best for scalability.- Time-to-value: Low short-term — won’t meet 2‑month deadline for full migration.- Operational complexity: High — new pipelines, testing, and monitoring.- Data migration risk: High — complex partial data split and consistency.- Rollback difficulty: High — distributed pieces increase rollback complexity.Recommendation:Given the 2‑month constraint and limited engineering resources, recommend a phased strategy: primary action = Lift-and-shift to meet the deadline (fast, low-risk rollback), combined with a short replatform pilot for one noncritical component (prove Aurora/ECS migrations). After cutover, schedule a 3–12 month refactor roadmap using the strangler pattern targeting high‑value services. This minimizes business risk, delivers quick cloud presence, and creates an incremental path to cloud-native benefits. Operational steps: use Snowball or accelerated network transfer, automated infra as code (CloudFormation/Terraform), snapshot-based cutover plan, and run a production‑parallel test environment before final DNS switch.
MediumTechnical
61 practiced
Design a dashboard and metric set to track an incremental rollout (canary to full) of a new microservice across multiple regions. Identify leading and lagging indicators, alert thresholds, decision gates for widening the rollout, and how different personas (engineer, product manager, exec) should use the dashboard.
Sample Answer
Requirements (assumptions): phased rollout (canary → regional → global), multi-region traffic splitting, automated rollback, SLOs per region, ability to compare old vs new.Dashboard layout (panels):- Overview (single-glance): % traffic to new service, global error rate, latency p95/p99, successful deploys, active rollouts.- Per-region comparison: traffic split, error rate, latency distribution, request rate (RPS), CPU/memory, instance health.- Canary vs baseline diff: A/B comparison charts for errors, latency, user impact.- Logs/trace links + recent releases.- Automation & gates: current gate status, time in stage, manual override controls.Leading indicators (early warnings):- Error rate spike (5xx rate) over baseline (e.g., +0.5% absolute or >50% relative)- Latency p95 increase vs baseline (>20% or +200ms)- Increase in trace error spans or new exception types- Request success ratio drop for key endpoints- Infrastructure signals: crashloop / restart rate >1/hr per instanceLagging indicators:- User-facing business metrics (checkout conversion, API throughput)- Sustained SLO breaches (availability over 1h)- Support tickets/CSAT changes- Data inconsistency reports (if applicable)Alert thresholds (examples, tune per service):- SEV-1: global 5xx rate >1% and >2x baseline for 5 minutes OR p99 latency >2s AND error rate >0.5%- SEV-2: per-region error >0.5% and >2x baseline for 10 minutes- SEV-3: p95 latency increase >20% for 10 minutes- Infra: instance restart rate >1/hour OR health-check failure >3 instancesDecision gates for widening rollout:- Canary hold (15–30 min minimum): pass automated health checks: - Error rate within +20% relative to baseline and absolute <0.5% - p95 latency within +15% and p99 within +20% - No new uncaught exception types >1 per 1000 reqs - No infra failures - Business metric delta within acceptable margin (if measurable)- Regional widen: sustain above for 1–4 hours with at least 10k requests or representative traffic mix- Global rollout: sustained stability for 24 hours, SLO burn rate acceptable, no open SEV-1/2, business KPIs stable- Rollback trigger: any SEV-1 or repeated gate failures; automated rollback if critical thresholds breached.How personas use the dashboard:- Engineer: drill into per-region panels, traces, logs; watches leading indicators and infra, responds to alerts, runs roll-forward/rollback, inspects error types and stack traces.- Product Manager: monitors business KPIs, conversion and user-impact panels, uses A/B comparison to decide benefit; cares about rollout timing and risk appetite; authorizes widening after gate windows.- Executive: high-level overview: % rollout, SLO compliance, major alerts, business impact; wants clear recommended action (hold/widen/rollback) and risk summary.Operational notes:- Automate gate checks and integrate with CI/CD (feature flags, deploy orchestration).- Annotate dashboards with deploy events and traffic changes.- Use statistical tests (e.g., sequential testing) for canary comparisons to avoid noise-driven decisions.- Maintain runbook with thresholds, who-to-pager, and rollback steps.
MediumSystem Design
43 practiced
You are responsible for a 9-month migration of a revenue-critical monolith to the cloud with minimal downtime. Using MECE decomposition, outline the top-level workstreams (for example: discovery & planning, data migration, infra automation, API contracts, testing & QA, cutover & rollback, monitoring), the key milestones and deliverables for each, and how you would make the decomposition mutually exclusive and collectively exhaustive for tracking and reporting.
Sample Answer
I'll decompose the 9-month migration into MECE top-level workstreams, list key milestones and deliverables for each, and explain how the decomposition stays mutually exclusive and collectively exhaustive for tracking and reporting.Workstreams (MECE)1. Discovery & Planning- Milestones: Stakeholder alignment (wk1–3), current-state inventory complete (wk3–6), target architecture & migration plan approved (wk7–9)- Deliverables: Inventory of services/data, dependency graph, cost/ROI model, migration runbook, risk register, project timeline & RACI2. Infra & Platform Automation- Milestones: Landing zone design approved (wk8–10), IaC modules ready (wk10–16), CI/CD pipelines in place (wk16–20)- Deliverables: Terraform/CloudFormation modules, pipeline configs, environment scaffolding (dev/stage/prod), security baseline3. Application Refactor & API Contracts- Milestones: App decomposition plan (wk9–12), API contract definitions (wk12–14), containerization/build artifacts (wk14–22)- Deliverables: Service-level design docs, OpenAPI specs, container images, backward-compatibility strategy4. Data Migration & Consistency- Milestones: Data schema mapping complete (wk10–14), ETL/CDC tooling ready (wk14–22), dry-run migrations & validation (wk22–28)- Deliverables: Data migration scripts, validation reports, cutover data sync plan, rollback data snapshots5. Testing & QA (including Performance & Resilience)- Milestones: Test plans approved (wk13–16), automated integration & contract tests (wk16–24), performance & chaos tests passed (wk24–30)- Deliverables: Test suites, test results, SLO verification, acceptance criteria signoffs6. Cutover, Rollback & Release Management- Milestones: Pilot cutover (canary) (wk28–30), staged rollouts (wk30–36), final cutover (wk36–38)- Deliverables: Cutover checklist, rollback procedures, communication plan, runbooks7. Observability, Ops & Post-Migration Stabilization- Milestones: Monitoring dashboards in prod (wk18–26), SLO/alerting thresholds set (wk26–30), 30/60/90-day stabilization complete (wk38–48)- Deliverables: Dashboards, alerts, runbooks, incident response plans, knowledge transfer docsRisk & Compliance (cross-cutting)- Milestones: Compliance signoffs, security reviews at gates- Deliverables: Security assessment, compliance artifactsWhy this is MECE- Mutually exclusive: Each stream groups distinct responsibilities (planning vs infra vs app vs data vs test vs cutover vs ops). No deliverable is owned by more than one stream—e.g., API contracts live in App stream; IaC lives in Infra.- Collectively exhaustive: Cross-cutting risks/compliance and stabilization are included so nothing essential to migration (planning, build, test, move, operate) is missing.- For tracking/reporting: map milestones to a single source-of-truth Gantt, link deliverables to owners (RACI), track KPIs per stream (e.g., infra: time-to-provision, app: contract compliance %, data: migration lag/errors, ops: MTTR, availability). Use weekly health dashboard with red/amber/green per stream and integration gates before cutover.This structure supports clear ownership, measurable milestones, and controlled, low-downtime cutovers for a revenue-critical migration.
EasyTechnical
38 practiced
You're given a vague requirement: 'Improve system reliability.' As a Solutions Architect, describe how you would translate this into measurable success criteria. Provide example metrics (SLA/SLO), acceptance thresholds, measurement windows, and how you would align these with business objectives and stakeholders across product, ops, and sales.
Sample Answer
First I’d clarify objectives and constraints: what “reliable” means for this product, customer impact, acceptable cost, and regulatory needs. Then translate into measurable SLAs/SLOs aligned to business impact.Example SLOs / metrics (with suggested SLA-backed targets and measurement windows):- Availability (user-facing): 99.95% monthly uptime (SLA 99.9% for enterprise customers). Window: rolling 30 days. Measurement: synthetic health checks + real traffic success rate.- Error rate (5xx): <0.1% of requests per 24-hour window. Alert if >0.3% sustained for 1 hour.- Latency (p95): <300ms for API critical path, measured over 1-hour windows. SLA: p99 <1s for paid tiers.- Mean Time to Recovery (MTTR): Restore service for P1 incidents within 60 minutes, measured per incident and averaged monthly.- Data durability: 11 nines annualized for storage component, validated via checksum/audit weekly.- Deployment failure rate: <1% of releases require rollback in 7-day window.Acceptance thresholds: define error budgets (e.g., 0.05% downtime budget/month). If budget exhausted, freeze risky changes and prioritize reliability work.Aligning with stakeholders:- Product: map SLOs to user journeys and prioritize which SLOs drive retention/ARPU. E.g., for e-commerce checkout, availability and latency SLOs get highest weight.- Ops/SRE: convert SLOs into SLO dashboards, automated alerts, runbooks; agree incident severity definitions and MTTR targets.- Sales/Customer Success: bake SLAs into contracts and escalation paths; offer tiered SLAs for different price points and ensure compensation/credits formula tied to measured SLA breaches.Measurement & governance:- Implement dashboards (Grafana) with rolling windows and synthetic probes; store raw telemetry for audits.- Monthly reliability review with stakeholders, present SLO attainment, error-budget burn, and proposed remediation.- Continuous improvement: convert recurring breaches into prioritized engineering work, tracked in roadmap and gated by acceptance tests.This approach ensures “improve system reliability” becomes concrete, measurable, and tied to business outcomes and customer expectations.
MediumTechnical
44 practiced
Sales promised a customer delivery in 6 weeks but engineering estimates 4 months. As the Solutions Architect, describe a structured negotiation process you would run to resolve the conflict while protecting the customer and company. Include rapid options analysis, proposed phased delivery or minimum viable scope, contractual options (e.g., SOW milestones), and how you document the agreed trade-offs.
Sample Answer
Situation: Sales committed to a 6‑week delivery but engineering’s estimate is ~4 months (≈16 weeks). As the Solutions Architect I run a structured, time‑boxed negotiation to protect the customer and company.1) Rapid alignment workshop (1–2 days)- Invite Sales, Eng lead, PM, Customer rep, Legal. Clarify scope: list MUST/SHOULD/WON’T requirements, acceptance criteria, regulatory/non‑functional constraints.- Agree objectives for negotiation (customer business outcome, hard deadline if any, acceptable risk).2) Rapid options analysis (24–48 hours)- Create 3 concrete options with engineering input: A. Accelerated MVP (6–8 weeks): de‑scoped core features that deliver ~50–70% business value; use COTS/integration shortcuts; higher engineering burn and acceptance of technical debt. B. Phased delivery: Phase 1 (8–10 weeks) = core transactions + API; Phase 2 (additional 6–8 weeks) = analytics, hardening. C. Full delivery (16 weeks): complete, production‑hardened, lower risk.- For each option estimate effort, cost delta, risk, quality trade‑offs, and mitigation (parallel teams, overtime cap, feature toggles).3) Negotiate and document contractual structure- Propose SOW with milestone‑based deliveries and acceptance criteria: - Milestone 1: MVP demo & customer sign‑off (date, criteria) - Milestone 2: Feature complete / UAT - Milestone 3: Production cutover- Include change control: any scope change triggers impact assessment and amended SOW/time/cost.- Add risk clauses: limited warranty, rollout support window, and optional SLA tiers for expedited work (costed).4) Governance & decision record- Record agreed trade‑offs in a Decision Log and Architecture Appendix: features removed, workarounds, technical debt items, upgrade migration path, performance expectations.- Create RAID (Risks, Assumptions, Issues, Dependencies) and assign owners.- Capture sign‑offs from Customer, Sales, Eng, and Legal on SOW and architecture.5) Communication & follow‑through- Communicate chosen option and implications to stakeholders within 24 hours.- Start weekly milestone reviews and a technical steering check at major gates.- Reassess after Phase 1: decide on fast‑track to Phase 2 or pause.Example outcome: We delivered an MVP in 8 weeks covering 60% of revenue‑critical flows, customer accepted with clear roadmap and paid change order for remaining features; engineering kept technical debt explicit and time‑boxed remediation in Phase 2.This approach balances customer value, company risk, and clear contractual governance so expectations and trade‑offs are explicit and reversible.
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