Company Specific Technology Knowledge Questions

Deep knowledge of the specific company's technology stack, engineering architecture, platform components, and major technical challenges. This includes familiarity with the languages, frameworks, cloud providers, orchestration and infrastructure tools, internal platforms, common performance and scalability concerns, and recent engineering initiatives or launches. Interviewers probe this area to evaluate whether a candidate understands the precise technical environment they would join, can speak to tradeoffs in architecture and tooling, and can explain how their own technical skills map to the company specific needs.

HardTechnical

34 practiced

You're an SRE lead deciding whether to block a major reliability refactor (estimated to reduce incidents by 40%) or deliver a high-visibility customer feature requested by executives. Describe how you would make the decision, quantify tradeoffs, communicate to stakeholders, and propose a plan that balances reliability and product goals.

Sample Answer

Situation: Our team has two conflicting priorities — a major reliability refactor estimated to cut incidents by ~40% (multi-sprint effort) and a high-visibility customer feature requested by executives with revenue/retention implications.

Decision approach / analysis:- Quantify impact: - Reliability refactor: historical incident rate 12/month → expect ~7.2/month (Δ = −4.8 incidents/month). Average incident cost = outage minutes × business impact; estimate $X/hr and developer-hours for remediation (e.g., 20 dev-hours/incident). Projected annual savings = incidents reduced × (cost + dev-hours*salary). - Feature: estimated to increase conversion/retention by Y% producing $Z ARR and strategic customer goodwill.- Risk/SLO perspective: - Check current SLOs and error budget. If error budget exhausted or trending to breach, reliability work may be prioritized. - Consider downstream risks: launching feature while system is fragile increases probability of customer-impacting incidents, potentially negating revenue gains.- Opportunity cost: - Time to deliver refactor T_refactor vs time to deliver feature T_feature. Consider incremental value per sprint for each.

Decision: propose balanced, data-driven compromise rather than absolute block.Plan:1. Short-term: Negotiate a phased delivery — allocate 60/40 split first two sprints (feature + reliability gating work). Deliver a minimal viable variant of the feature (feature flagged, limited rollout) to reduce blast radius.2. Reliability-first micro-deliverables: break refactor into vertical slices that deliver immediate risk reduction (e.g., instrumenting, automated failover, rate-limited endpoints) with measurable metrics after each slice.3. Safety gates: require automated canary tests, SLO guardrails, and rollback playbooks before broad rollout of feature. Use progressive exposure (1%, 10%, 100%) tied to observability thresholds.4. Metrics & tracking: define KPIs — incident rate, MTTR, SLO burn rate, feature adoption, revenue delta. Weekly dashboard updates.5. Communication: - To executives: present quantified tradeoffs (incident reduction value vs expected revenue), recommended phased approach, and timeline with gating criteria; propose the feature launch under feature flags and limited rollout to protect customers. - To product and engineering: shared roadmap with milestones, owner for each slice, and explicit acceptance criteria (monitoring, runbooks). - To stakeholders: regular cadence updates and a one-page RACI + decision log.6. Contingency: If SLOs worsen during rollout, pause feature expansion and reallocate team to critical refactor slices.

Outcome expectation: preserves executive visibility and near-term product goals while reducing risk of major incidents. Measurable checkpoints let us re-evaluate priorities with data and ensure we’re protecting customers and business value.

EasyTechnical

36 practiced

You're joining an SRE team at a company whose stack includes: Kubernetes (GKE), Go microservices, Istio service mesh, Prometheus/Grafana, Jaeger tracing, ArgoCD for deployments, and Google Cloud. Describe your 30/60/90 day plan to get productive and contribute to reliability. Include access, learning milestones, oncall ramp, measurable goals, and initial small wins you would aim for.

MediumTechnical

48 practiced

Propose a monitoring architecture using Prometheus and Thanos for a company running multiple Kubernetes clusters in several regions. Address local scrapes, global queries, long-term storage, alert routing to teams, query latency, and storage cost controls.

Sample Answer

Requirements (clarify): local scraping for fast alerting, global query/forensics, long-term durable storage, per-team alert routing, low query latency for dashboards, and cost controls.

Architecture (high-level):- Per-cluster Prometheus instances (one per cluster/namespace) running in Kubernetes for local scraping and rule evaluation. Use ServiceMonitors/PodMonitors (Prometheus Operator) and relabeling to keep scrape configs consistent.- Deploy Thanos Sidecar alongside each Prometheus to upload blocks and serve as a store/gRPC gateway.- Regional Thanos Ruler for global alerting and recording rules that need cluster-wide view; it can query sidecars/store-gateway.- Global query layer: Thanos Query components in each region behind HA load balancer, federated to a Global Thanos Query that aggregates results for cross-region dashboards.- Long-term storage: Thanos Store Gateway backed by object storage (S3/GCS/Azure) with chunk caching on fast SSD-backed instances to reduce egress/latency.- Compaction/retention: Use Thanos Compactor to downsample and enforce retention policies; keep high-resolution recent data (e.g., 12 weeks) and downsample older data to lower resolution (e.g., 1m→5m) to cut cost.

Alert routing and latency:- Local alerts: Primary alerting on each Prometheus for SLO-critical, high-priority alerts (page on x minutes) to minimize latency.- Global alerts: Thanos Ruler evaluates cross-cluster rules; non-urgent or business-level alerts come from global rules.- Use Alertmanager federation: local Alertmanagers per region with routing to team-specific receivers, plus a global Alertmanager that can deduplicate and escalate. Use inhibition and deduplication to reduce noise.- Route by labels (cluster/region/service/owner) and use silences and schedules centrally managed via API/AD.

Query latency and cost controls:- Cache dashboards on Grafana using datasource caching and query_frontend component of Thanos to parallelize/aggregate queries and reduce latency.- Use query timeout/limits and per-tenant or per-query concurrency limits.- Implement multi-tier storage: hot (recent blocks on local SSD + store-gateway cache), warm (object storage with cache), cold (archived downsampled).- Enforce retention and downsampling policies. Use per-tenant quotas and rate limits to prevent expensive ad-hoc queries.- Monitor cost metrics (S3 egress/put/list, CPU) and create alerts for cost anomalies.

Operational concerns:- Authentication/authorization: mTLS between components; RBAC for Thanos/Grafana.- HA: run Prometheus/sidecar in HA pairs (or replica), Thanos components replicated, compactor with leader election.- Backups & restores: snapshot Prometheus before upgrades; object storage lifecycle policies.- Testing & rollout: canary Thanos Query and Alertmanager changes; chaos tests for failovers.

This design balances low-latency local alerting with global visibility and long-term, cost-controlled storage via downsampling, caching, and quotas.

HardTechnical

35 practiced

Several dependent microservices have tight SLOs and a downstream outage threatens to burn the product error budget. How would you coordinate across teams to reallocate or temporarily relax SLOs, trigger rollbacks or throttles, and design guardrails to prevent cascading error-budget burn while maintaining customer trust?

Sample Answer

Situation: A critical downstream service (payments) is degraded and our dependent microservices are burning SLO error budget fast. If unchecked, upstream services will cascade and the product-level error budget will be exhausted, risking customer impact and regulatory exposure.

Immediate actions I’d take (first 0–30 minutes)- Triage & scope: confirm SLIs (latency/error rate) and identify which services’ requests are failing vs timing out using dashboards (Prometheus/Grafana, tracing).- Short-term protective controls: - Apply circuit breakers and bulkheads (Envoy/Istio) to prevent retries from amplifying load. - Throttle non-essential traffic (batch jobs, analytics, low-tier customers) using rate-limiting rules. - Switch failing calls to a degraded path / fallback cached responses where safe.- Trigger an incident bridge (PagerDuty) and notify stakeholders (product, engineering, infra, legal, comms).

Coordinating SLO relaxation, rollbacks, throttles (30–120 minutes)- Convene decision authority (SRE lead + product + service owners). Present current burn rate, projected budget exhaustion time, and impact matrix (who loses what function).- Use a predefined error-budget policy: if projected burn > X% in Y hours, allow temporary SLO relaxation or traffic reduction for lower-priority tiers. Example: relax non-customer facing read-only SLOs from 99.95% to 99.5% for up to 24 hours approved by SRE lead + product manager.- If a recent deploy likely caused regression, trigger automated safe rollback via CI/CD with canary abort and quick rollback playbook. If rollback risky, apply feature-flag cut or throttles instead.- Apply customer-tier throttles: preserve premium customer traffic while shedding best-effort workloads.

Guardrails to prevent cascading burn (longer-term/proactive)- Implement automated circuit-breaker and retry-budgeting per-service; retries consume retry budget tracked by metrics to avoid amplification.- Enforce bulkheads: isolate resource pools per downstream dependency to prevent global resource exhaustion.- Implement adaptive throttling with backpressure signals (HTTP 429 propagation, gRPC headers) and central policy engine (Kong/Istio).- Per-customer quotas and priority queues to protect SLAs for high-value users.- Automate detection + automated mitigation: runbooks encoded in tools (Playbook-as-code) that, upon patterns (error-rate spike + downstream latency), automatically enact safe throttles and notify humans.- Ensure SLO governance: preapproved escalation matrix for SLO relaxation, maximum duration, rollback criteria, and mandatory post-incident review.

Customer trust & communication- For customer-visible degradation, coordinate comms: clear status page updates, targeted notifications to impacted customers, estimated ETA, and mitigations taken. Preserve transparency and compensation policy triggers for premium customers.

Post-incident- Run blameless postmortem with data: root cause, why protections failed/weren’t in place, and concrete remediation (e.g., add caching, increase capacity, refactor dependency calls).- Update SLOs/SLIs if they no longer reflect customer expectations and add automation to prevent repeat (tests, chaos experiments).- Measure effectiveness: track reduced time-to-mitigate, reduction in error-budget burn in future incidents, and customer satisfaction.

Why this approach works- Mixes immediate automated protections to stop amplification with governance to avoid unsafe permanent SLO changes.- Protects highest-value customers while maintaining transparency.- Encodes decisions into policies and automation to reduce manual coordination latency in future incidents.

EasyTechnical

30 practiced

Explain the sidecar pattern as used with service meshes and proxies in this company's architecture. What problems does it solve (observability, security, routing), what are the operational costs (resource overhead, lifecycle complexity), and when might you avoid using sidecars?

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