Domande di Colloquio DevOps: Guida Completa 2026
Preparati ai colloqui DevOps con le domande fondamentali su CI/CD, Kubernetes, Docker, Terraform e pratiche SRE. Risposte dettagliate incluse.

DevOps collega sviluppo software e operazioni IT in un'unica cultura orientata all'automazione e alla consegna continua. Questa guida raccoglie le domande più frequenti nei colloqui DevOps, organizzate per dominio, con risposte strutturate che dimostrano padronanza reale dei concetti.
Oltre alle competenze tecniche, i recruiter valutano la capacità di spiegare concetti complessi in modo semplice e di condividere esperienze concrete nella risoluzione di problemi.
Fondamenti e Cultura DevOps
Le domande iniziali valutano la comprensione complessiva della filosofia DevOps.
D1: Cos'è DevOps e quali problemi risolve questo approccio?
DevOps rappresenta una cultura e un insieme di pratiche che unificano lo sviluppo software (Dev) e le operazioni IT (Ops). L'obiettivo è ridurre il ciclo di sviluppo mantenendo alta la qualità.
# devops-principles.yaml
# The pillars of DevOps culture
principles:
collaboration:
description: "Breaking silos between teams"
practices:
- "Shared responsibility for production code"
- "Continuous communication via ChatOps"
- "Blameless post-mortems"
automation:
description: "Automate repetitive tasks"
practices:
- "Infrastructure as Code (IaC)"
- "CI/CD pipelines"
- "Automated testing at all levels"
measurement:
description: "Measure to improve"
metrics:
- "Deployment frequency"
- "Lead time for changes"
- "Mean time to recovery (MTTR)"
- "Change failure rate"
sharing:
description: "Share knowledge"
practices:
- "Documentation as Code"
- "Automated runbooks"
- "Regular knowledge sharing sessions"I problemi risolti includono deployment lenti e rischiosi, mancanza di visibilità tra i team e inconsistenza tra gli ambienti.
D2: Qual è la differenza tra CI, CD (Continuous Delivery) e CD (Continuous Deployment)?
Questi tre concetti formano una progressione nell'automazione del ciclo di consegna.
# ci-cd-pipeline-stages.sh
# Illustration of CI/CD stages
# ============================================
# CI (Continuous Integration)
# ============================================
# Goal: Frequently integrate code into a shared repository
# Automation: Build + Tests
echo "CI: Code commit → Build → Unit Tests → Integration Tests"
# ============================================
# CD (Continuous Delivery)
# ============================================
# Goal: Code always deployable to production
# Automation: CI + Staging deployment + Manual approval
echo "CD Delivery: CI → Deploy Staging → Manual Approval → Deploy Prod"
# ============================================
# CD (Continuous Deployment)
# ============================================
# Goal: Automatic deployment to production
# Automation: Entire pipeline without human intervention
echo "CD Deployment: CI → Deploy Staging → Auto Tests → Auto Deploy Prod"La distinzione chiave risiede nel livello di automazione: la Continuous Delivery richiede validazione manuale prima della produzione, mentre il Continuous Deployment automatizza completamente il processo.
CI/CD e Pipeline
Le domande su CI/CD testano la capacità di progettare e ottimizzare pipeline di consegna.
D3: Come strutturare una pipeline CI/CD robusta?
Una pipeline ben progettata segue fasi progressive con checkpoint a ogni livello.
# .gitlab-ci.yml
# Complete CI/CD pipeline with parallel and sequential stages
stages:
- validate
- build
- test
- security
- deploy-staging
- integration-tests
- deploy-production
variables:
DOCKER_IMAGE: $CI_REGISTRY_IMAGE:$CI_COMMIT_SHA
# ============================================
# Stage 1: Fast validation (< 2 min)
# ============================================
lint:
stage: validate
script:
- npm run lint
- npm run type-check
# Run on every commit
rules:
- if: $CI_PIPELINE_SOURCE == "merge_request_event"
- if: $CI_COMMIT_BRANCH
# ============================================
# Stage 2: Application build
# ============================================
build:
stage: build
script:
- docker build -t $DOCKER_IMAGE .
- docker push $DOCKER_IMAGE
# Cache Docker layers to speed up builds
cache:
key: docker-$CI_COMMIT_REF_SLUG
paths:
- .docker-cache/
# ============================================
# Stage 3: Parallel tests
# ============================================
unit-tests:
stage: test
script:
- npm run test:unit -- --coverage
coverage: '/Lines\s*:\s*(\d+\.?\d*)%/'
artifacts:
reports:
coverage_report:
coverage_format: cobertura
path: coverage/cobertura-coverage.xml
integration-tests:
stage: test
services:
- postgres:16-alpine
- redis:7-alpine
script:
- npm run test:integration
# Parallelization with unit tests
parallel: 3
# ============================================
# Stage 4: Security analysis
# ============================================
sast:
stage: security
script:
- trivy image --exit-code 1 --severity HIGH,CRITICAL $DOCKER_IMAGE
allow_failure: false
dependency-scan:
stage: security
script:
- npm audit --audit-level=high
allow_failure: true # Alert without blocking
# ============================================
# Stage 5: Staging deployment
# ============================================
deploy-staging:
stage: deploy-staging
script:
- kubectl set image deployment/app app=$DOCKER_IMAGE -n staging
- kubectl rollout status deployment/app -n staging --timeout=300s
environment:
name: staging
url: https://staging.example.com
only:
- develop
# ============================================
# Stage 6: E2E tests on staging
# ============================================
e2e-tests:
stage: integration-tests
script:
- npm run test:e2e -- --base-url=https://staging.example.com
artifacts:
when: on_failure
paths:
- cypress/screenshots/
- cypress/videos/
only:
- develop
# ============================================
# Stage 7: Production deployment
# ============================================
deploy-production:
stage: deploy-production
script:
- kubectl set image deployment/app app=$DOCKER_IMAGE -n production
- kubectl rollout status deployment/app -n production --timeout=300s
environment:
name: production
url: https://app.example.com
# Manual deployment with protection
when: manual
only:
- mainQuesta pipeline illustra le best practice: fasi parallele per la velocità, artefatti per la tracciabilità e ambienti protetti per la produzione.
D4: Come gestire i segreti in una pipeline CI/CD?
La gestione dei segreti richiede un approccio a più livelli che combina crittografia, rotazione e principio del minimo privilegio.
# kubernetes-secrets-management.yaml
# Approach 1: External Secrets Operator with HashiCorp Vault
apiVersion: external-secrets.io/v1beta1
kind: ExternalSecret
metadata:
name: app-secrets
namespace: production
spec:
refreshInterval: 1h # Automatic rotation
secretStoreRef:
name: vault-backend
kind: ClusterSecretStore
target:
name: app-secrets
creationPolicy: Owner
data:
# Reference to secret in Vault
- secretKey: DATABASE_PASSWORD
remoteRef:
key: secret/data/production/database
property: password
- secretKey: API_KEY
remoteRef:
key: secret/data/production/api
property: key
---
# SecretStore configuration
apiVersion: external-secrets.io/v1beta1
kind: ClusterSecretStore
metadata:
name: vault-backend
spec:
provider:
vault:
server: "https://vault.example.com"
path: "secret"
version: "v2"
auth:
kubernetes:
mountPath: "kubernetes"
role: "external-secrets"
# Dedicated ServiceAccount with minimal permissions
serviceAccountRef:
name: external-secrets-saLe pratiche raccomandate includono: non memorizzare mai i segreti in chiaro nel codice, usare gestori di segreti dedicati (Vault, AWS Secrets Manager) e abilitare la rotazione automatica.
Evitare le variabili d'ambiente CI/CD visibili nei log. Mascherare sempre i segreti con le funzionalità native della piattaforma CI (masked variables).
Kubernetes e Orchestrazione
Le domande su Kubernetes valutano la comprensione dei concetti di orchestrazione e la capacità di risolvere problemi concreti.
D5: Spiega l'architettura di Kubernetes e il ruolo di ciascun componente.
Kubernetes segue un'architettura master-node con componenti che hanno responsabilità distinte.
# kubernetes-architecture.yaml
# Control Plane components (Master)
control_plane:
api_server:
role: "Entry point for all API requests"
responsibilities:
- "Validation and configuration of API objects"
- "Authentication and authorization"
- "REST interface for kubectl and other clients"
etcd:
role: "Distributed key-value database"
responsibilities:
- "Cluster state storage"
- "Source of truth for configuration"
- "Consensus via Raft algorithm"
scheduler:
role: "Assigning Pods to nodes"
responsibilities:
- "Evaluating constraints (resources, affinity)"
- "Selecting the optimal node"
- "Respecting PodDisruptionBudgets"
controller_manager:
role: "Control loops for desired state"
controllers:
- "ReplicaSet Controller"
- "Deployment Controller"
- "Service Controller"
- "Node Controller"
# Worker Node components
worker_nodes:
kubelet:
role: "Agent on each node"
responsibilities:
- "Communication with Control Plane"
- "Pod lifecycle management"
- "Node status reporting"
kube_proxy:
role: "Network proxy on each node"
responsibilities:
- "iptables/IPVS rules for Services"
- "Intra-cluster load balancing"
container_runtime:
role: "Container execution"
options:
- "containerd (recommended)"
- "CRI-O"Questa architettura garantisce alta disponibilità: il Control Plane può essere replicato e i workload vengono distribuiti tra i Worker Node.
D6: Come fare il debug di un Pod che non si avvia?
Il debug in Kubernetes segue un approccio metodico analizzando i diversi livelli.
# kubernetes-debugging.sh
# Workflow for debugging a failing Pod
# Step 1: Check Pod status
kubectl get pod my-app-pod -o wide
# STATUS: CrashLoopBackOff, ImagePullBackOff, Pending, etc.
# Step 2: Pod details and events
kubectl describe pod my-app-pod
# Important sections:
# - Conditions (PodScheduled, Initialized, Ready)
# - Events (scheduling, pull errors, etc.)
# Step 3: Container logs
kubectl logs my-app-pod --previous # Previous crash logs
kubectl logs my-app-pod -c init-container # Init container logs
# Step 4: Interactive execution for debugging
kubectl exec -it my-app-pod -- sh
# Check: env vars, mounted files, network
# Step 5: Check available resources
kubectl describe node <node-name>
# Sections: Allocatable, Allocated resources
# Step 6: Debug with ephemeral Pod (K8s 1.25+)
kubectl debug my-app-pod -it --image=busybox --share-processesLe cause più comuni includono: risorse insufficienti, immagine non trovata, segreti mancanti o probe mal configurate.
# pod-debugging-checklist.yaml
# Debugging checklist by status
debugging_by_status:
Pending:
causes:
- "Insufficient resources on nodes"
- "PersistentVolumeClaim not bound"
- "Affinity/Taints not satisfied"
commands:
- "kubectl describe pod <name> | grep -A 20 Events"
- "kubectl get pvc"
- "kubectl describe nodes | grep -A 5 Allocated"
ImagePullBackOff:
causes:
- "Non-existent image or incorrect tag"
- "Private registry without imagePullSecrets"
- "Docker Hub rate limiting"
commands:
- "kubectl get events --field-selector reason=Failed"
- "kubectl get secret <pull-secret> -o yaml"
CrashLoopBackOff:
causes:
- "Application error at startup"
- "Missing configuration (env vars, configmaps)"
- "Liveness probe too aggressive"
commands:
- "kubectl logs <pod> --previous"
- "kubectl describe pod <pod> | grep -A 10 Liveness"
OOMKilled:
causes:
- "Memory limit too low"
- "Memory leak in application"
commands:
- "kubectl describe pod <pod> | grep -A 5 Last State"
- "kubectl top pod <pod>"Pronto a superare i tuoi colloqui su DevOps?
Pratica con i nostri simulatori interattivi, flashcards e test tecnici.
Infrastructure as Code
Le domande sull'IaC valutano la padronanza degli strumenti di provisioning e delle best practice.
D7: Terraform vs Ansible: quando usare ciascuno strumento?
Questi strumenti hanno filosofie e casi d'uso distinti.
# terraform-example.tf
# Terraform: Infrastructure provisioning (declarative)
# Ideal for: cloud resources, networking, infrastructure state
terraform {
required_providers {
aws = {
source = "hashicorp/aws"
version = "~> 5.0"
}
}
# Remote state for collaboration
backend "s3" {
bucket = "terraform-state-prod"
key = "infrastructure/terraform.tfstate"
region = "eu-west-1"
dynamodb_table = "terraform-locks"
encrypt = true
}
}
# Declarative resource: Terraform manages the lifecycle
resource "aws_eks_cluster" "main" {
name = "production-cluster"
role_arn = aws_iam_role.eks_cluster.arn
version = "1.29"
vpc_config {
subnet_ids = module.vpc.private_subnets
endpoint_private_access = true
endpoint_public_access = false
}
# Implicit dependencies managed by Terraform
depends_on = [
aws_iam_role_policy_attachment.eks_cluster_policy
]
}
# Reusable modules for standardization
module "vpc" {
source = "terraform-aws-modules/vpc/aws"
version = "5.0.0"
name = "production-vpc"
cidr = "10.0.0.0/16"
azs = ["eu-west-1a", "eu-west-1b", "eu-west-1c"]
private_subnets = ["10.0.1.0/24", "10.0.2.0/24", "10.0.3.0/24"]
public_subnets = ["10.0.101.0/24", "10.0.102.0/24", "10.0.103.0/24"]
enable_nat_gateway = true
single_nat_gateway = false # HA: one NAT per AZ
}# ansible-example.yml
# Ansible: Configuration management (procedural/declarative)
# Ideal for: OS configuration, app deployment, orchestration
---
- name: Configure application servers
hosts: app_servers
become: yes
vars:
app_version: "2.5.0"
tasks:
# System package management
- name: Install required packages
ansible.builtin.apt:
name:
- nginx
- python3-pip
- supervisor
state: present
update_cache: yes
# Configuration via Jinja2 templates
- name: Deploy nginx configuration
ansible.builtin.template:
src: templates/nginx.conf.j2
dest: /etc/nginx/sites-available/app
owner: root
group: root
mode: '0644'
notify: Reload nginx
# Application deployment
- name: Deploy application
ansible.builtin.git:
repo: "https://github.com/org/app.git"
dest: /opt/app
version: "v{{ app_version }}"
notify: Restart application
handlers:
- name: Reload nginx
ansible.builtin.service:
name: nginx
state: reloaded
- name: Restart application
ansible.builtin.supervisorctl:
name: app
state: restartedIn sintesi: Terraform per l'infrastruttura (ciò che esiste), Ansible per la configurazione (come è configurata). Entrambi gli strumenti vengono spesso combinati in un flusso di lavoro completo.
D8: Come strutturare un progetto Terraform per una grande organizzazione?
Una struttura modulare con separazione degli ambienti facilita la manutenzione e la collaborazione.
# terraform-project-structure
# Recommended structure for enterprise projects
terraform-infrastructure/
├── modules/ # Reusable modules
│ ├── networking/
│ │ ├── main.tf
│ │ ├── variables.tf
│ │ ├── outputs.tf
│ │ └── README.md
│ ├── kubernetes/
│ │ ├── main.tf
│ │ ├── variables.tf
│ │ └── outputs.tf
│ └── database/
│ ├── main.tf
│ ├── variables.tf
│ └── outputs.tf
│
├── environments/ # Per-environment configuration
│ ├── dev/
│ │ ├── main.tf # Calls modules
│ │ ├── variables.tf
│ │ ├── terraform.tfvars # Dev values
│ │ └── backend.tf # Dev state
│ ├── staging/
│ │ ├── main.tf
│ │ ├── terraform.tfvars
│ │ └── backend.tf
│ └── production/
│ ├── main.tf
│ ├── terraform.tfvars
│ └── backend.tf
│
├── shared/ # Shared resources
│ ├── iam/
│ └── dns/
│
└── .github/
└── workflows/
└── terraform.yml # CI/CD pipeline# environments/production/main.tf
# Example of module usage
module "networking" {
source = "../../modules/networking"
environment = "production"
vpc_cidr = var.vpc_cidr
azs = var.availability_zones
enable_flow_logs = true
}
module "kubernetes" {
source = "../../modules/kubernetes"
environment = "production"
cluster_name = "prod-cluster"
vpc_id = module.networking.vpc_id
subnet_ids = module.networking.private_subnet_ids
node_groups = var.node_groups
# Production: HA configuration
cluster_version = "1.29"
enable_cluster_autoscaler = true
}
module "database" {
source = "../../modules/database"
environment = "production"
vpc_id = module.networking.vpc_id
subnet_ids = module.networking.database_subnet_ids
instance_class = "db.r6g.xlarge"
multi_az = true # HA in production
backup_retention = 30
}Questa struttura permette: versioning dei moduli, revisione delle modifiche per ambiente e riuso del codice.
Monitoraggio e Osservabilità
Le domande sul monitoraggio valutano la capacità di progettare sistemi osservabili.
D9: Quali sono i tre pilastri dell'osservabilità?
L'osservabilità si basa su tre tipi di dati complementari che aiutano a comprendere lo stato interno di un sistema.
# observability-pillars.yaml
# The three pillars of observability
pillars:
metrics:
description: "Numeric data aggregated over time"
characteristics:
- "Low cardinality"
- "Efficient storage"
- "Ideal for alerting"
examples:
- "request_count (counter)"
- "response_time_seconds (histogram)"
- "active_connections (gauge)"
tools:
- "Prometheus"
- "Datadog"
- "CloudWatch"
use_cases:
- "Real-time dashboards"
- "Threshold alerts"
- "Capacity planning"
logs:
description: "Timestamped text events"
characteristics:
- "High cardinality"
- "Detailed context"
- "Large storage"
examples:
- "Application errors"
- "Audit events"
- "Debug information"
tools:
- "Loki"
- "Elasticsearch"
- "CloudWatch Logs"
use_cases:
- "Debugging"
- "Audit compliance"
- "Root cause analysis"
traces:
description: "Request tracking across services"
characteristics:
- "End-to-end view"
- "Context propagation"
- "Bottleneck identification"
examples:
- "Distributed transaction"
- "Service dependencies"
- "Latency breakdown"
tools:
- "Jaeger"
- "Tempo"
- "AWS X-Ray"
use_cases:
- "Performance optimization"
- "Service dependencies"
- "Error propagation"D10: Come configurare alert efficaci?
Alert ben progettati riducono la fatica e consentono una risposta rapida agli incidenti.
# prometheus-alerting-rules.yaml
# Prometheus alerting rules with best practices
groups:
- name: application-alerts
rules:
# Alert on symptom, not cause
- alert: HighErrorRate
# Error rate > 1% over 5 minutes
expr: |
sum(rate(http_requests_total{status=~"5.."}[5m]))
/
sum(rate(http_requests_total[5m]))
> 0.01
for: 5m # Avoid false positives
labels:
severity: critical
team: backend
annotations:
summary: "High error rate detected"
description: |
Error rate is {{ $value | humanizePercentage }}
for the last 5 minutes.
runbook_url: "https://wiki.example.com/runbooks/high-error-rate"
# Proactive alert on saturation
- alert: DiskSpaceRunningLow
expr: |
(node_filesystem_avail_bytes / node_filesystem_size_bytes)
* 100 < 20
for: 15m
labels:
severity: warning
annotations:
summary: "Disk space below 20%"
description: |
Node {{ $labels.instance }} has only
{{ $value | humanize }}% disk space remaining.
# SLO-based alerting
- alert: SLOBudgetBurnRate
# Error budget consumed too quickly
expr: |
(
sum(rate(http_requests_total{status=~"5.."}[1h]))
/
sum(rate(http_requests_total[1h]))
) > (1 - 0.999) * 14.4
for: 5m
labels:
severity: critical
annotations:
summary: "SLO budget burning too fast"
description: |
At current error rate, monthly SLO budget will be
exhausted in less than 2 days.# alertmanager-config.yaml
# AlertManager configuration with intelligent routing
global:
resolve_timeout: 5m
route:
receiver: default
group_by: [alertname, cluster, service]
group_wait: 30s # Wait to group alerts
group_interval: 5m # Interval between grouped notifications
repeat_interval: 4h # Re-alert if not resolved
routes:
# Critical alerts: immediate notification
- match:
severity: critical
receiver: pagerduty-critical
continue: true # Also notify Slack
# Alerts by team
- match:
team: backend
receiver: slack-backend
- match:
team: infrastructure
receiver: slack-infra
receivers:
- name: pagerduty-critical
pagerduty_configs:
- service_key: <pagerduty-key>
severity: critical
- name: slack-backend
slack_configs:
- channel: '#alerts-backend'
send_resolved: true
title: '{{ .Status | toUpper }}: {{ .CommonAnnotations.summary }}'
text: '{{ .CommonAnnotations.description }}'Principi chiave: alertare sui sintomi (impatto sull'utente) piuttosto che sulle cause, includere runbook e adattare le soglie agli SLO.
Sicurezza e Conformità
Le domande sulla sicurezza valutano la comprensione dei rischi e delle contromisure.
D11: Come proteggere un cluster Kubernetes?
La sicurezza di Kubernetes copre più livelli: rete, autenticazione, workload e dati.
# kubernetes-security-policies.yaml
# NetworkPolicy: network isolation between namespaces
apiVersion: networking.k8s.io/v1
kind: NetworkPolicy
metadata:
name: default-deny-all
namespace: production
spec:
# Applied to all pods in namespace
podSelector: {}
policyTypes:
- Ingress
- Egress
# No traffic allowed by default
ingress: []
egress: []
---
# Allow only necessary traffic
apiVersion: networking.k8s.io/v1
kind: NetworkPolicy
metadata:
name: api-network-policy
namespace: production
spec:
podSelector:
matchLabels:
app: api
policyTypes:
- Ingress
- Egress
ingress:
# Accept only from ingress controller
- from:
- namespaceSelector:
matchLabels:
name: ingress-nginx
ports:
- protocol: TCP
port: 8080
egress:
# Allow to database
- to:
- podSelector:
matchLabels:
app: postgres
ports:
- protocol: TCP
port: 5432
# Allow DNS
- to:
- namespaceSelector: {}
podSelector:
matchLabels:
k8s-app: kube-dns
ports:
- protocol: UDP
port: 53# pod-security-standards.yaml
# PodSecurity: workload restrictions
apiVersion: v1
kind: Namespace
metadata:
name: production
labels:
# Enforce: blocks violations
pod-security.kubernetes.io/enforce: restricted
# Warn: warns without blocking
pod-security.kubernetes.io/warn: restricted
# Audit: logs violations
pod-security.kubernetes.io/audit: restricted
---
# Pod compliant with "restricted" standards
apiVersion: v1
kind: Pod
metadata:
name: secure-pod
namespace: production
spec:
securityContext:
runAsNonRoot: true
runAsUser: 1000
fsGroup: 1000
seccompProfile:
type: RuntimeDefault
containers:
- name: app
image: myapp:latest
securityContext:
allowPrivilegeEscalation: false
readOnlyRootFilesystem: true
capabilities:
drop:
- ALL
resources:
limits:
memory: "256Mi"
cpu: "500m"
requests:
memory: "128Mi"
cpu: "250m"
volumeMounts:
- name: tmp
mountPath: /tmp
volumes:
- name: tmp
emptyDir: {}La sicurezza di Kubernetes combina più livelli: RBAC per l'autorizzazione, NetworkPolicy per l'isolamento della rete, PodSecurity per le restrizioni sui workload e crittografia dei segreti a riposo.
D12: Qual è il principio del minimo privilegio e come applicarlo?
Questo principio stabilisce che un utente o sistema deve avere solo i permessi minimi necessari per svolgere il proprio compito.
# rbac-least-privilege.yaml
# Kubernetes RBAC with minimal permissions
# Role: permissions in a specific namespace
apiVersion: rbac.authorization.k8s.io/v1
kind: Role
metadata:
namespace: production
name: deployment-manager
rules:
# Pod reading (for monitoring)
- apiGroups: [""]
resources: ["pods", "pods/log"]
verbs: ["get", "list", "watch"]
# Deployment management only
- apiGroups: ["apps"]
resources: ["deployments"]
verbs: ["get", "list", "watch", "update", "patch"]
# No create/delete on deployments
# No access to secrets or sensitive configmaps
---
# RoleBinding: Role <-> ServiceAccount association
apiVersion: rbac.authorization.k8s.io/v1
kind: RoleBinding
metadata:
name: deployment-manager-binding
namespace: production
subjects:
- kind: ServiceAccount
name: ci-cd-deployer
namespace: production
roleRef:
kind: Role
name: deployment-manager
apiGroup: rbac.authorization.k8s.io
---
# Dedicated ServiceAccount for CI/CD
apiVersion: v1
kind: ServiceAccount
metadata:
name: ci-cd-deployer
namespace: production
annotations:
# Automatic token expiration
kubernetes.io/enforce-mountable-secrets: "true"Questo principio si applica anche a AWS IAM, database e accessi di rete.
SRE e Affidabilità
Le domande sull'SRE valutano la comprensione delle pratiche di affidabilità e della gestione degli incidenti.
D13: Cos'è un SLO e come definirlo?
Gli Service Level Objectives (SLO) quantificano l'affidabilità attesa di un servizio e guidano le decisioni ingegneristiche.
# slo-definitions.yaml
# SLO definitions for an API service
service: payment-api
owner: payments-team
slos:
- name: availability
description: "Service responds successfully to requests"
sli:
# SLI: measured metric
type: availability
good_events: "http_requests_total{status=~'2..'}"
total_events: "http_requests_total"
target: 99.9% # SLO: objective
window: 30d # Measurement window
# Error budget: 0.1% = 43.2 minutes/month
error_budget:
monthly_minutes: 43.2
- name: latency
description: "Response time below threshold"
sli:
type: latency
good_events: "http_request_duration_seconds_bucket{le='0.3'}"
total_events: "http_request_duration_seconds_count"
target: 99% # 99% of requests < 300ms
window: 30d
- name: throughput
description: "Ability to process transactions"
sli:
type: throughput
query: "sum(rate(transactions_processed_total[5m]))"
target: ">= 1000 TPS"
# Actions based on error budget
error_budget_policy:
- condition: "remaining > 50%"
actions:
- "Feature development prioritized"
- "Experimentation allowed"
- condition: "remaining 20-50%"
actions:
- "Balance features and reliability"
- "Increase testing coverage"
- condition: "remaining < 20%"
actions:
- "Freeze non-critical deployments"
- "Focus on reliability improvements"
- condition: "exhausted"
actions:
- "Incident response mode"
- "All hands on reliability"Gli SLO consentono decisioni obiettive: rilasciare una nuova funzionalità o rafforzare l'affidabilità.
D14: Come condurre un post-mortem efficace?
Un post-mortem blameless promuove l'apprendimento e la prevenzione di futuri incidenti.
# postmortem-template.yaml
# Blameless post-mortem template
incident:
id: "INC-2026-0042"
title: "Payment service unavailability"
severity: SEV1
duration: "45 minutes"
date: "2026-01-15"
# Factual timeline
timeline:
- time: "14:32"
event: "Alert: error rate > 5% on payment-api"
actor: "PagerDuty"
- time: "14:35"
event: "Incident declared, team notified"
actor: "On-call engineer"
- time: "14:42"
event: "Cause identified: connection pool exhausted"
actor: "Backend team"
- time: "14:55"
event: "Mitigation: deployment rollback"
actor: "Backend team"
- time: "15:17"
event: "Service restored, monitoring stable"
actor: "Backend team"
# Measurable impact
impact:
users_affected: 12500
transactions_failed: 847
revenue_impact: "~$16,500"
slo_budget_consumed: "2.3 days"
# Root cause analysis (5 Whys)
root_cause_analysis:
- question: "Why was the service unavailable?"
answer: "DB connections were exhausted"
- question: "Why were connections exhausted?"
answer: "A slow query was blocking connections"
- question: "Why was there a slow query?"
answer: "Missing index on a new table"
- question: "Why was the index missing?"
answer: "Incomplete migration deployed"
- question: "Why was the migration incomplete?"
answer: "No execution plan validation in staging"
# Corrective actions
action_items:
- id: "AI-001"
type: "prevent"
description: "Add SQL execution plan validation in CI"
owner: "DBA team"
due_date: "2026-01-22"
priority: P1
- id: "AI-002"
type: "detect"
description: "Alert on connection pool usage > 80%"
owner: "SRE team"
due_date: "2026-01-18"
priority: P1
- id: "AI-003"
type: "mitigate"
description: "Implement circuit breaker on DB queries"
owner: "Backend team"
due_date: "2026-01-29"
priority: P2
# Lessons learned
lessons_learned:
what_went_well:
- "Fast detection thanks to alerting (< 3 min)"
- "Clear communication in incident channel"
- "Rollback completed in less than 15 minutes"
what_went_poorly:
- "No load testing on new endpoint"
- "Staging didn't reflect prod data volume"
lucky:
- "Incident during daytime with full team available"L'obiettivo è migliorare il sistema, non trovare un responsabile. Le azioni sono classificate in tre categorie: prevenzione, rilevamento e mitigazione.
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Conclusione
I colloqui DevOps coprono uno spettro ampio di competenze, dalla cultura agli strumenti tecnici. La chiave del successo sta nel dimostrare una comprensione profonda dei concetti, illustrata da esempi concreti di implementazione.
Checklist di preparazione
- Padroneggiare i concetti CI/CD ed essere in grado di progettare una pipeline completa
- Comprendere l'architettura di Kubernetes e saper fare il debug dei problemi comuni
- Conoscere gli strumenti IaC (Terraform, Ansible) e i loro rispettivi casi d'uso
- Saper configurare il monitoraggio e definire alert pertinenti
- Applicare le best practice di sicurezza (minimo privilegio, difesa in profondità)
- Spiegare le pratiche SRE (SLO, error budget, post-mortem)
- Avere esempi concreti di risoluzione dei problemi
- Saper spiegare concetti complessi in modo semplice
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