Site Reliability Engineering Interview Preparation

Translate a customer-facing restaurant reservation journey into explicit reliability requirements.

Construct a dependency map for a cloud-native Python service running on AWS and Kubernetes.

Perform a failure mode and effects analysis for a distributed service architecture.

Produce a prioritized production-readiness gap list using customer impact, likelihood, detectability, and blast radius.

Select and scope a reliability problem for a containerized Python reservation service.

Write a concise technical proposal covering customer impact, constraints, alternatives, risks, and success measures.

Differentiate service-level indicators, service-level objectives, and service-level agreements in a reliability proposal.

Select user-centric SLIs for availability, latency, correctness, and freshness.

Calculate a request-based availability SLI from valid and successful event counts.

Interpret latency distributions and choose an appropriate percentile-based latency SLI.

Set a defensible SLO target and measurement window from customer and business requirements.

Calculate and interpret error-budget consumption and burn rate from an incident or telemetry dataset.

Write an error-budget policy that maps budget status to release and reliability decisions.

Select suitable metric types for counters, gauges, rates, and latency distributions.

Configure the Datadog Agent to collect Kubernetes, container, and application telemetry from a Python service.

Configure Datadog's AWS integration for relevant EC2, load-balancer, and database metrics.

Instrument a Python service with custom business and operational metrics.

Design a tagging strategy that avoids uncontrolled cardinality.

Build a Datadog dashboard around traffic, errors, latency, and saturation.

Diagnose a simulated degradation from metric trends on a Datadog dashboard.

Emit structured application logs with consistent severity and diagnostic fields from a Python service.

Configure centralized log collection, parsing, and search in Datadog.

Propagate a correlation identifier across service boundaries and background tasks.

Instrument a distributed request path with Datadog APM or OpenTelemetry tracing.

Locate a latency bottleneck by analyzing spans, service maps, and flame graphs.

Choose a trace-sampling strategy that balances diagnostic value, traffic volume, and cost.

Trace a browser performance problem from a Datadog RUM event to related backend telemetry.

Configure a customer-relevant and actionable threshold monitor with an appropriate evaluation window, recovery condition, and owner.

Create a multi-window error-budget burn-rate alert for an SLO.

Configure alert routing, severity, escalation, and ownership in Datadog.

Write a concise alert runbook containing validation, diagnosis, mitigation, and escalation steps.

Validate an alert by injecting a controlled failure and measuring detection behavior.

Tune a noisy monitor using alert-history evidence without hiding genuine customer impact.

Explain how metrics, logs, traces, SLOs, alerts, and error budgets work together in an interview scenario.

Diagnose CPU, memory, disk, and process pressure on a Linux host using system evidence.

Localize a DNS, TLS, connection, or load-balancer failure along a network request path.

Diagnose Kubernetes pod failures using events, logs, health checks, and resource state.

Configure Kubernetes probes, disruption budgets, and resource controls for workload reliability.

Identify a PostgreSQL performance bottleneck from query plans, locks, connections, and resource metrics.

Profile a Python service to locate a CPU, memory, or I/O bottleneck.

Correlate metrics, logs, and traces to isolate a fault in a distributed request path.

Set connection and request timeouts from latency budgets and dependency behavior.

Implement bounded retries with exponential backoff and jitter for a transient failure.

Make a retried API or background task idempotent.

Choose between circuit breakers and bulkheads for a specific dependency failure mode.

Implement load shedding or graceful degradation for an overloaded service.

Run a load test that identifies the service's saturation point and failure behavior.

Create a capacity plan that derives Kubernetes resource allocations and scaling thresholds from demand, measured throughput, utilization targets, and expected growth.

Prepare an on-call shift using access, handoff, escalation, and service-health checks.

Assign incident severity from customer impact, scope, duration, and urgency.

Establish incident-command roles and a communication channel during a production event.

Triage an unfamiliar production incident using a structured telemetry-first workflow.

Maintain a timestamped incident log that separates observations, hypotheses, decisions, and actions.

Deliver a concise stakeholder update that states impact, response status, and the next update time.

Lead a time-boxed incident simulation from detection through stabilization and handoff.

Reconstruct an evidence-based incident timeline from telemetry and response records.

Describe customer impact using affected journeys, scope, duration, and severity.

Separate an incident trigger from contributing technical and organizational conditions.

Apply systems-oriented causal analysis without assigning individual blame.

Write corrective actions that are specific, owned, prioritized, and verifiably complete.

Facilitate a blameless post-incident review that surfaces disagreement and learning.

Track corrective actions and recurring incident patterns to verify reliability improvement.

Rank a toil-reduction opportunity using frequency, duration, manual effort, operational risk, automation cost, and expected benefit.

Automate a repeatable diagnostic or remediation runbook in Python with validation, dry-run, audit, and rollback controls.

Design Terraform module boundaries for a reusable and independently changeable AWS service stack.

Configure secure S3 remote state, state locking, and resource-deletion safeguards for a Terraform AWS stack.

Implement automated Terraform plan execution and drift detection in a GitHub Actions workflow.

Add reliability checks and an explicit rollback criterion to a GitHub Actions deployment pipeline.

Design a canary rollout that limits blast radius and uses telemetry to decide promotion or rollback.

Break the reliability proposal into prioritized user stories and independently deliverable tasks.

Instrument the capstone Python service with structured JSON logging and trace-correlation identifiers.

Instrument the capstone service with custom application metrics and APM distributed tracing.

Configure an SLO and construct a Datadog operational dashboard for the capstone service's critical user journey.

Configure a multi-window burn-rate alert for the capstone SLO and link an actionable incident runbook.

Run a controlled load or failure experiment and collect evidence about system behavior.

Present the initiative's decisions, trade-offs, experimental results, prioritized reliability roadmap, and next steps in an engineering-review format.

Rewrite existing backend and cloud achievements as evidence-based SRE résumé bullets without overstating production-operations experience.

Build and rehearse an evidence bank of STAR narratives covering technical ownership, failure response, collaboration, constructive disagreement, and feedback using genuine past experience.

Design a reliable restaurant reservation platform while reasoning about dependencies, failure modes, capacity, and trade-offs.

Lead a verbal incident-response exercise while communicating clearly under changing conditions.

Parse a stream of structured logs in Python and aggregate error rates and latency statistics.

Solve a timed data-structures-and-algorithms problem and communicate complexity and trade-offs.

Complete a timed mixed-format SRE mock interview and turn the resulting evidence into a targeted improvement plan.