CNPA - Certified Cloud Native Platform Engineering Associate

Multiple-choice exam | 120 minutes | Passing score: 75% | $250 USD

Overview

The CNPA (Certified Cloud Native Platform Engineering Associate) validates foundational knowledge of platform engineering concepts, practices, and tooling in the cloud native ecosystem. It’s a multiple-choice exam — you need to understand concepts, not configure live clusters.

KubeDojo covers ~80%+ of CNPA topics through our existing Platform Engineering track. This page maps CNPA domains to existing modules so you can prepare efficiently.

CNPA is the associate-level companion to CNPE. If CNPE is “prove you can build a platform,” CNPA is “prove you understand what a platform is and why it matters.” Pass CNPA first, then level up to the hands-on CNPE.

---

Exam Domains

Domain	Weight	KubeDojo Coverage
Platform Engineering Core Fundamentals	36%	Excellent (6 discipline + 6 GitOps + 7 toolkit modules)
Platform Observability, Security & Conformance	20%	Excellent (4 foundation + 5 discipline + 10 toolkit modules)
Continuous Delivery & Platform Engineering	16%	Excellent (6 discipline + 7 toolkit modules)
Platform APIs and Provisioning Infrastructure	12%	Excellent (6 discipline + 5 toolkit modules)
IDPs and Developer Experience	8%	Excellent (6 discipline + 6 toolkit modules)
Measuring Your Platform	8%	Good (7 SRE discipline + 2 toolkit modules)

---

Domain 1: Platform Engineering Core Fundamentals (36%)

Competencies

• Declarative resource management
• DevOps principles and culture
• Application environments and lifecycle
• Platform architecture concepts
• Continuous Integration and Continuous Delivery
• GitOps fundamentals

KubeDojo Learning Path

Platform Engineering (start here):

Module	Topic	Relevance
Platform Eng 2.1	What is Platform Engineering?	Direct
Platform Eng 2.2	Developer Experience (DevEx)	Direct
Platform Eng 2.3	Internal Developer Platforms	Direct
Platform Eng 2.4	Golden Paths and paved roads	Direct
Platform Eng 2.5	Self-Service Infrastructure	Direct
Platform Eng 2.6	Platform Maturity Models	Direct

GitOps:

Module	Topic	Relevance
GitOps 3.1	What is GitOps? OpenGitOps 4 principles	Direct
GitOps 3.2	Repository strategies, mono vs multi-repo	Direct
GitOps 3.3	Environment promotion patterns	Direct
GitOps 3.4	Drift detection and reconciliation	Direct
GitOps 3.5	Secrets management in GitOps	Direct
GitOps 3.6	Multi-cluster GitOps	Direct

Architecture & IaC:

Module	Topic	Relevance
Distributed Systems 5.1	Distributed systems fundamentals	Direct
IaC 6.1	Infrastructure as Code fundamentals	Direct
Systems Thinking 1.1	Systems thinking for platform design	Partial

Tools (conceptual understanding):

Module	Topic	Relevance
ArgoCD	ArgoCD: GitOps delivery	Direct
Flux	Flux CD: GitOps controllers	Direct
Helm & Kustomize	Declarative packaging and customization	Direct
Dagger	CI/CD pipeline design	Direct
Tekton	K8s-native CI/CD pipelines	Direct
Argo Workflows	Workflow automation	Direct
Argo Rollouts	Progressive delivery: canary, blue-green	Direct

---

Domain 2: Platform Observability, Security & Conformance (20%)

Competencies

• Observability fundamentals (metrics, logs, traces)
• Secure communication patterns
• Policy engines and admission controllers
• Kubernetes security concepts
• CI/CD pipeline security

KubeDojo Learning Path

Observability Theory:

Module	Topic	Relevance
Observability 3.1	What is Observability?	Direct
Observability 3.2	Metrics, Logs, Traces	Direct
Observability 3.3	Instrumentation principles	Direct
Observability 3.4	From data to insight	Direct

Security:

Module	Topic	Relevance
Security 4.1	Security mindset	Direct
Security 4.2	Defense in depth	Direct
Security 4.3	Identity and access management	Direct
DevSecOps 4.1	DevSecOps fundamentals	Direct
DevSecOps 4.3	Security in CI/CD	Direct

Tools (know what they do):

Module	Topic	Relevance
Prometheus	Pull-based monitoring, PromQL	Direct
OpenTelemetry	OTel Collector, auto-instrumentation	Direct
Grafana	Dashboards, data sources	Direct
Loki	Log aggregation, LogQL	Direct
Tracing	Jaeger/Tempo, context propagation	Direct
OPA/Gatekeeper	Policy engine, admission control	Direct
Kyverno	YAML-native policy engine	Direct
SPIFFE/SPIRE	Workload identity, mTLS	Direct
Service Mesh	Istio/Linkerd mTLS	Direct
CKA RBAC	RBAC fundamentals	Direct

---

Domain 3: Continuous Delivery & Platform Engineering (16%)

Competencies

• CI pipeline concepts and design
• Incident response and management
• GitOps basics and workflows

KubeDojo Learning Path

Theory:

Module	Topic	Relevance
GitOps 3.1	What is GitOps? OpenGitOps principles	Direct
GitOps 3.3	Environment promotion patterns	Direct
GitOps 3.4	Drift detection and reconciliation	Direct
SRE 1.5	Incident Management	Direct
SRE 1.6	Blameless Postmortems	Direct
DevSecOps 4.2	Shift-left (CI integration)	Partial

Tools:

Module	Topic	Relevance
ArgoCD	ArgoCD: Application CRD, sync, RBAC	Direct
Flux	Flux CD: GitRepository, HelmRelease	Direct
Argo Rollouts	Progressive delivery strategies	Direct
Dagger	CI/CD pipeline design	Direct
Tekton	K8s-native CI/CD pipelines	Direct
Argo Workflows	Workflow automation	Direct
Supply Chain	Sigstore/Cosign, image signing	Partial

---

Domain 4: Platform APIs and Provisioning Infrastructure (12%)

Competencies

• Reconciliation loop pattern
• Custom Resource Definitions (CRDs)
• Infrastructure provisioning as code
• Kubernetes Operators

KubeDojo Learning Path

Theory:

Module	Topic	Relevance
Platform Eng 2.5	Self-Service Infrastructure	Direct
IaC 6.1	Infrastructure as Code	Direct
IaC 6.4	IaC at Scale	Direct
Distributed Systems 5.2	Consensus and coordination (reconciliation)	Partial
CKA CRDs	CRD creation and operator pattern	Direct
CKA Extension Interfaces	K8s extension points	Direct

Tools:

Module	Topic	Relevance
Crossplane	XRDs, Compositions, Providers	Direct
Kubebuilder	Building custom operators	Direct
Cluster API	Declarative cluster lifecycle	Direct
Helm & Kustomize	Declarative resource packaging	Partial
vCluster	Virtual clusters for provisioning	Partial

---

Domain 5: IDPs and Developer Experience (8%)

Competencies

• Service catalogs and software templates
• Developer portals
• AI/ML in platform automation

KubeDojo Learning Path

Theory:

Module	Topic	Relevance
Platform Eng 2.2	Developer Experience (DevEx)	Direct
Platform Eng 2.3	Internal Developer Platforms	Direct
Platform Eng 2.4	Golden Paths and templates	Direct
Platform Eng 2.6	Platform Maturity Models	Direct
AIOps 6.1	AIOps foundations	Direct
AIOps 6.6	Auto-remediation with AI	Partial

Tools:

Module	Topic	Relevance
Backstage	Software Catalog, Templates, TechDocs	Direct
K9s CLI	Developer CLI tooling	Partial
Telepresence/Tilt	Inner-loop development	Partial
DevPod	Reproducible dev environments	Partial
Gitpod/Codespaces	Cloud development environments	Partial
AIOps Tools	AI-powered observability features	Partial

---

Domain 6: Measuring Your Platform (8%)

Competencies

• DORA metrics (deployment frequency, lead time, MTTR, change failure rate)
• Platform efficiency and adoption metrics
• SLOs and error budgets for platforms

KubeDojo Learning Path

Theory:

Module	Topic	Relevance
SRE 1.2	SLOs (SLIs, SLAs)	Direct
SRE 1.3	Error Budgets and burn rates	Direct
SRE 1.4	Toil and automation metrics	Direct
SRE 1.7	Capacity Planning	Partial
Platform Eng 2.6	Platform Maturity Models	Direct
Reliability 2.4	Measuring reliability	Direct
Reliability 2.5	SLOs, SLIs, error budgets (theory)	Direct

Tools:

Module	Topic	Relevance
SLO Tooling	Sloth, Pyrra, error budget dashboards	Direct
FinOps	OpenCost, cost allocation, efficiency	Direct

---

Study Strategy

CNPA PREPARATION PATH (recommended order)
══════════════════════════════════════════════════════════════

Week 1-2: Core Fundamentals (36% of exam!)
├── Platform Engineering discipline (6 modules)
├── GitOps discipline (6 modules)
├── IaC 6.1 (Infrastructure as Code basics)
└── Distributed Systems 5.1 (architecture concepts)

Week 3: Observability, Security & Conformance (20%)
├── Observability Theory foundation (4 modules)
├── Security Principles foundation (4 modules)
├── DevSecOps 4.1 + 4.3 (fundamentals + CI/CD security)
└── Know your tools: Prometheus, OTel, OPA, Kyverno

Week 4: Continuous Delivery (16%)
├── Review GitOps discipline modules (from Week 1)
├── SRE 1.5 + 1.6 (incident response + postmortems)
├── CI/CD pipeline tools: Dagger, Tekton, Argo Workflows
└── ArgoCD + Flux (conceptual understanding)

Week 5: Platform APIs & IDPs (12% + 8%)
├── CKA CRDs/Operators module (reconciliation loop)
├── Crossplane + Kubebuilder (conceptual)
├── Backstage (service catalogs, developer portals)
└── AIOps 6.1 (AI/ML in automation)

Week 6: Measuring & Review (8% + exam prep)
├── SRE modules: SLOs, error budgets, toil
├── DORA metrics concepts (review Platform Eng 2.6)
├── FinOps / OpenCost (platform efficiency)
└── Full domain review, focus on 36% core fundamentals

---

Exam Tips

• This is a multiple-choice exam — focus on conceptual understanding, not hands-on configuration
• Core Fundamentals = 36% of the exam — nail platform engineering concepts, GitOps, and DevOps principles first
• Know the “why” not just the “what” — understand why GitOps uses pull-based reconciliation, why platforms need golden paths, etc.
• DORA metrics come up everywhere — know the four key metrics and what they measure
• GitOps principles — memorize the OpenGitOps four principles (declarative, versioned, automated, reconciled)
• Policy engines — understand OPA vs Kyverno at a conceptual level (when to use each)
• Time management: 120 minutes for multiple-choice is generous. Read questions carefully, flag uncertain ones, review at the end.

---

Gap Analysis

Our Platform Engineering track covers ~85%+ of the CNPA curriculum. Remaining minor gaps:

Topic	Status	Notes
DORA metrics implementation	Covered	DORA metrics (deployment frequency, lead time, MTTR, change failure rate) now covered in the SRE discipline modules alongside SLOs and error budgets
DevOps culture & history	Minor gap	Platform Eng modules assume DevOps context; KCNA cloud-native modules provide additional background
Application environment lifecycle	Covered	Spread across GitOps environment promotion and IaC modules

These gaps are minor. The 50+ modules mapped above provide comprehensive CNPA preparation.

---

Related Certifications

CERTIFICATION PATH
══════════════════════════════════════════════════════════════

Entry Level:
├── KCNA (Cloud Native Associate) — K8s fundamentals
├── KCSA (Security Associate) — Security fundamentals
└── CNPA (Platform Engineering Associate) ← YOU ARE HERE

Professional Level:
├── CKA (K8s Administrator) — Cluster operations
├── CKAD (K8s Developer) — Application deployment
├── CKS (K8s Security Specialist) — Security hardening
└── CNPE (Platform Engineer) — Hands-on platform engineering

Specialist (Coming):
└── CKNE (K8s Network Engineer) — Advanced networking

The CNPA is the natural stepping stone to CNPE. CNPA tests your conceptual understanding of platform engineering; CNPE tests your ability to build and operate platforms hands-on. If you pass CNPA, continue with KubeDojo’s platform toolkit modules to build hands-on skills for CNPE.