Understanding the Company's Infrastructure Context Questions

Research the company's public infrastructure information (engineering blog, tech talks, published case studies, job description). Understand what systems they operate at scale, what problems they likely face, and what your role would contribute to.

HardTechnical

20 practiced

Given only public performance claims about throughput and latency, propose a realistic load and chaos testing strategy to validate the inferred architecture. Include types of tests, traffic profiles, test data generation approaches, ramping patterns, failure injection scenarios, metrics to collect and how to interpret the results to refine capacity and resiliency assumptions.

Sample Answer

Goal: validate inferred architecture (stateless vs stateful tiers, caches, queues, DB sharding) against public throughput/latency claims and reveal capacity/resiliency boundaries.

Assumptions & clarifications:- Confirm SLAs (p95/p99 latency targets, sustained throughput, burst allowance), expected workload mix (read/write ratio), and deployment topology (regions, autoscale rules).

Test types & sequence:1. Baseline capacity: light synthetic traffic to verify correctness and measure cold-start latencies.2. Load profile testing: steady-state at claimed throughput, and at 50/75/100/125% to validate headroom.3. Stress testing: push to saturation to find breaking points (latency degradation, error increase).4. Soak testing: sustained 24–72h at 80–100% to expose memory leaks, thread pool exhaustion, GC pauses.5. Spike testing: sudden 5–10x bursts for short windows to validate autoscaling and throttling.6. Chaos experiments: inject instance/network/storage/db faults, packet loss, latency, partition region failures, and dependency slowdowns.

Traffic profiles & ramping:- Mix realistic request types (API read-heavy vs write-heavy), session stickiness, authenticated vs anonymous, background batch jobs.- Ramping patterns: linear ramp (to steady load over 10–30 min), step ramp (10% increments), sudden spike (instant +5x), and randomized jitter to simulate real-world variance.

Test data generation:- Use production-like data distributions (IDs, payload sizes, cache hit ratios). Seed synthetic users with realistic flows (think-time, think-multipage sessions). For databases, mirror schema and use anonymized snapshots or synthetically scaled datasets preserving cardinality and index distributions.

Failure injection scenarios:- Kill app instances, simulate slow downstream DB (increase latency 100–500ms), S3/Blob throttling, network partitions between services, DNS failures, full disk on node, auth service outage, and chaos during autoscale events.

Metrics to collect:- System: CPU, memory, disk I/O, network, kernel run-queue, GC metrics.- App: request rates, success/error rates, p50/p90/p95/p99 latencies, timeouts, queue depth, thread-pool exhaustion.- Infra: pod/container restarts, provisioning time, cold start counts, load balancer latencies.- Dependencies: DB connection pool usage, query latencies, cache hit/miss rates, queue lag.- Business: transactions/sec, revenue-impacting error counts.

How to interpret and refine:- If p95/p99 exceed SLAs while CPU <80%: identify contention (locks, DB hotspots, thread exhaustion) and optimize code or scale horizontally.- High error rates with autoscale triggered late: reduce scaling cooldowns, add predictive scaling, or provision buffer capacity.- Memory growth in soak: fix leaks or restart strategy; constrain heap or use rolling restarts.- Cache miss spikes cause DB overload: increase cache size or redesign keys, add read replicas, or introduce rate-limiting/backpressure.- Long provisioning times: shift to warm pool/keep-alive instances or use faster instance types.- For single-point failures revealed by chaos, add redundancy, circuit breakers, bulkheads, and defensive timeouts; validate recovery time objective (RTO)/recovery point objective (RPO).

Deliverables:- Test plan, traffic scripts, data generation tooling, dashboards (Grafana), automated runbooks, and recommended capacity/resilience changes with quantified headroom and cost trade-offs.

This approach converts public claims into validated assumptions, producing actionable architecture changes and capacity recommendations.

HardTechnical

22 practiced

You find evidence of deep reliance on a proprietary managed service in a case study. As a Solutions Architect advising sales, construct a risk assessment template that evaluates commercial, operational and technical risks of that dependency and suggest contractual or architectural mitigations such as SLAs, exit plans, or abstraction layers.

Sample Answer

Context & purpose: a one-page risk-assessment used in sales engagements to evaluate a customer’s reliance on a proprietary managed service, score exposure, and recommend contractual + architectural mitigations.

1) Header- Customer, service name, date, assessor, criticality (1–5)

3) Sample risk items- Commercial: Pricing volatility — vendor can increase fees unexpectedly. Likelihood 3 Impact 4 Score 12. Contract: fixed-term pricing, caps, volume discounts, audit rights. Arch: multi-cloud cost abstraction, ability to throttle usage.- Operational: Single-vendor outage / support quality. Likelihood 2 Impact 5 Score 10. Contract: SLA with credits, escalation paths, monthly SL reporting, penalty clauses. Arch: active/passive fallback, cross-region redundancy, monitoring + runbooks.- Technical: API/protocol lock‑in. Likelihood 4 Impact 4 Score 16. Contract: data portability clause, export formats, timely export timelines, vendor escrow for code/specs. Arch: abstraction layer (adapter/driver), use standard protocols, side-by-side pilot with alternative.- Security & compliance: shared responsibility gaps. Likelihood 2 Impact 5 Score 10. Contract: compliance attestations, right-to-audit, breach notification windows. Arch: encryption-in-transit/at-rest, key management under customer control.- Exit risk: Data egress costs & timelines. Likelihood 3 Impact 5 Score 15. Contract: capped egress fees, defined export format + SLA, transition support. Arch: continuous export snapshots, ETL connectors to alternate stores.

4) Scoring guidance & recommended actions- 1–8 Low: monitor- 9–14 Medium: require contractual mitigations + technical pilot- 15+ High: escalate to deal team; require firm contractual protections, proof-of-concept for exit, and architectural alternatives before close

5) Deliverables checklist for Sales- Redlined contract clauses (pricing cap, SLA, portability, escrow, audit)- Technical appendix: adapter design, monitoring dashboard, failover runbooks, PoC plan- Estimated TCO impact and migration timeline

Why this works: combines quantifiable scoring, actionable contractual language, and concrete architectural controls so sales can negotiate risk-aware deals while giving architects an implementation path.

HardTechnical

20 practiced

Public job listings and blog posts suggest a monolith is being decomposed. As a Solutions Architect, propose a staged migration strategy from monolith to microservices that minimizes customer impact. Include criteria for service boundaries, data ownership strategies, integration patterns, strategies for transactional integrity, and verification steps for each stage.

Sample Answer

High-level approach: use a staged “strangler” migration that incrementally extracts bounded contexts into independently deployable services to minimize customer impact. Each stage has clear exit criteria, verification, and rollback paths.

Stage 0 — Discover & Plan- Activities: domain decomposition workshops (DDD), telemetry analysis, performance/coupling mapping, data-flow inventory, risk assessment.- Service boundary criteria: high cohesion, low coupling, single source of business rules, independent release cadence, clear data ownership, aligned SLAs.- Verification: agreed domain model, prioritized roadmap, integration test matrix, rollback plan.

Stage 1 — Stabilize & Facade- Activities: add an API gateway and adapter layer; implement façade/proxy routing to enable feature toggles.- Integration: synchronous API proxying; consumer requests continue to hit monolith; new services can be introduced behind flags.- Verification: end-to-end functional parity tests, synthetic traffic validation, canary routing for a subset of users.

Stage 2 — Extract Read Paths (CQRS)- Activities: introduce read-model services via event replication or CDC; implement materialized views for new services.- Data ownership: monolith remains write owner; new service owns its read replicas (eventually consistent).- Integration patterns: Change Data Capture (Debezium), event streams (Kafka), anti-corruption layer for mapping.- Verification: compare read-model responses vs monolith for X% of requests, latency SLA checks, data drift monitoring.

Stage 3 —Extract Write Paths Incrementally- Activities: identify low-risk write operations; implement service API and migrate write ownership.- Transactional integrity: adopt Saga pattern for multi-service workflows; use orchestration for complex flows, choreography for simple ones; avoid distributed 2PC unless unavoidable.- Integration: events for eventual consistency, compensating transactions designed and tested.- Verification: contract tests, consumer-driven contract (PACT), end-to-end business scenario tests, chaos tests for failure modes, rollback via feature toggles.

Stage 4 —Cutover & Decommission- Activities: route all traffic to services, remove monolith modules, migrate remaining data via backfill/one-time migration tools.- Data ownership: final authoritative sources validated; one source of truth for each aggregate.- Transactional integrity: ensure no pending sagas; reconcile ledgers via idempotent consumers.- Verification: full reconciliation reports, production smoke tests, rollout in waves with canary and metrics gating.

Cross-cutting concerns & practices- Observability: distributed tracing, correlation IDs, business-level metrics, alerting on error budgets.- Security/Compliance: centralized auth (OAuth2/OIDC), consistent ACLs, data masking where required.- Operational: CI/CD per service, automated migrations, runbooks, feature flags, circuit breakers.- Rollback & Safety: per-stage feature toggles, database shadow writes for verification, blue/green or canary deployments.

Example: migrate “Orders” bounded context first. Use CDC to populate Order read-service, verify via A/B responses for 48 hours, then migrate payment write to new service using a saga that compensates failed downstream steps. Monitor order completion rate and revenue continuity; if metrics deviate beyond SLA thresholds, flip flag to monolith and investigate.

Outcome: incremental risk reduction, measurable verification at each handoff, eventual decoupling with maintained customer experience.

MediumTechnical

26 practiced

Public materials show the company produces high cardinality logs with long retention periods. As a Solutions Architect with cost constraints, propose a tiered retention and storage strategy for logs, metrics and traces that preserves investigative capability while reducing cost. Which signals or use cases determine what to keep long term?

Sample Answer

Requirements & constraints (assumed): preserve investigatory capability (root cause, compliance, security), minimize storage & query costs, support long retention (months–years), and handle high-cardinality logs.

Proposed tiered strategy- Hot (0–7 days): Full-fidelity logs, traces, high-resolution metrics in fast storage (managed TSDB + traces backend + log indexing). Enable full-text search, traces linked to logs. Use SSD-backed cloud service for low latency.- Warm (7–90 days): Move to cheaper block storage/backed index with partial indexing (keep indexed fields, drop full-text), store full traces but sampled (retain all errors). Keep high-cardinality fields as tags compressed.- Cold (90–365 days): Store aggregated metrics (1m/5m resolution), trace skeletons (span metadata), and compressed logs with limited indexing (partitioned by service/severity). Use object storage with lifecycle to Glacier/Archive tiers.- Archive (>1 year): Only what regulatory/security needs: aggregated metrics, sampled error traces, and hashed/searchable audit indices for retention/forensics. Full logs kept only if compliance requires.

Cost controls & techniques- Sampling: retain 100% of errors/security events; sample 1–5% of success traces and info logs, stratified by service and user segments.- Rollups & downsampling: metrics at higher resolution early, then 1m/5m/1h aggregates.- Index pruning: index only fields needed for queries (request_id, user_id when required), use tokenization sparingly.- Compression & columnar formats for logs in cold storage (Parquet/ORC).- TTL + lifecycle policies automated via pipeline (e.g., ILM in Elasticsearch or S3 lifecycle).- Alerting & retention policies driven by business: security, billing, SLAs, compliance.

Signals/use cases to keep long-term- Security/audit: auth events, privilege changes, IDS alerts — keep long (1–7 yrs).- Compliance/legal: transactions, PII access logs — keep per regs.- Billing/usage analytics: aggregated metrics and key identifiers.- SLA/root-cause: errors, exceptions, incident traces — keep full fidelity longer than normal ops logs.- Product analytics: sampled user flows and aggregates.

Trade-offs- Sampling reduces forensic completeness — mitigate by adaptive sampling (capture full traces around anomalies).- Indexing fewer fields saves cost but increases retrieval latency for ad-hoc queries.

Implementation note- Use cloud object storage + lifecycle + query-in-place (e.g., Athena) for cold data; managed observability platform or OSS stack with ILM for hot/warm. Define clear SLAs per retention tier and automate enforcement.

MediumTechnical

25 practiced

You find public Terraform modules that create networking and compute constructs. As a Solutions Architect, how would you evaluate those modules for reuse in client projects, what red flags would disqualify them, and how would you design a safe fork and extension strategy that preserves compliance and upgradeability?

Sample Answer

Approach: treat public Terraform modules as design artifacts — perform security, maintainability, and compatibility reviews before reuse; if acceptable, fork with governance to enable safe extension and upgrades.

Evaluate modules:- Functional fit: maps to required network/topology patterns, inputs/outputs documented, supports required cloud provider features (VPC peering, AZs, subnets, route tables, security groups/NACLs).- Code quality: uses Terraform 1.x and provider version constraints, no hard-coded IDs, clear variables with sensible defaults, outputs for integration, no deprecated resources.- Testing & CI: has unit/integration tests (terratest/chek), example usage, automated plan/apply in CI.- Security & compliance: secrets not embedded, IAM roles scoped least-privilege, network ACLs/SGs reviewed, logging/monitoring hooks present.- Maintenance: active upstream, semantic versioning, changelog, license compatible with client.

Red flags (disqualifying):- Hard-coded credentials or account IDs, or writes secrets to state without protection.- Uses deprecated Terraform features or exact provider pins preventing updates.- No tests, no examples, or abandoned repo (no recent commits or PRs).- Broad IAM permissions, insecure defaults (wide-open SGs), or undocumented breaking behavior.- GPL-incompatible license for client use.

Safe fork & extension strategy:1. Fork upstream into internal repo under governance namespace; preserve original remote as upstream remote.2. Implement wrapper pattern: keep core module logic minimal; create internal “composition” or wrapper modules that call the upstream module and add client-specific policies (tags, logging, guardrails).3. Introduce a compatibility layer: create thin input/output adapter to normalize variable names and defaults for internal standards.4. CI/CD: add automated Terraform fmt, validate, static checks (tflint), unit/integration tests, and policy-as-code (OPA/Rego or Sentinel) checks in pipeline.5. Versioning & upstream sync: adopt semantic versioning for internal fork; subscribe to upstream releases, triage upstream changes in a staging branch, run tests, and merge after policy checks. Keep an automated dependency update mechanism (dependabot).6. Compliance: embed policy checks (IaC scanning, CIS benchmarks), enforce state backend with encryption and role-based access, and require PR review by security/architecture owners.7. Documentation & deprecation: document extension points, supported variables, and upgrade procedures; mark internal changes clearly and schedule periodic upstream rebase windows.

Outcome: This preserves upgradeability (by tracking upstream), enforces client compliance via CI/policies, and limits divergence by using wrappers and adapters so most upstream fixes remain consumable.

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