Cloud certifications are losing their edge. Here’s what to do instead.

Cloud badges once meant something. You put in the hours. You memorised a ton. You passed. You posted the badge on LinkedIn. Recruiters noticed.

That signal is fading. Fast. IT certifications help in the learning process for new engineers, but they communicate less value, because exams measure recall rather than delivery, and AI reduces the requirement for recall, therefore, employers prioritise shipped work, operations maturity, and code.

Reasons for the Decline of Certifications

Examinations Do Not Measure Real Delivery Skills

Issue:
Azure certification exams (e.g., AZ-104, AZ-204, AZ-700, AZ-305) primarily assess recognition and recall — understanding service capabilities, configuration options, RBAC scopes, and best practices.
They do not evaluate the ability to:

• Build a multi-stage Azure DevOps pipeline with approvals, secret management, and rollback.

• Design resilient, cost-optimised architectures across availability zones using Managed Identities and Key Vault.

• Troubleshoot live incidents using Log Analytics, KQL, and AI-assisted root cause analysis.

• Execute zero-downtime deployments using Azure Front Door, Traffic Manager, and blue-green/canary methods.

Outcome:
Many professionals can pass exams but struggle with practical implementation.
They often lack experience in operational reliability, automation, and real-time troubleshooting — the skills that matter in delivery environments.

Reference:

• Microsoft Learn – Certification Exams Overview
  (Exam objectives confirm focus on knowledge validation, not end-to-end delivery.)

• Skillsoft IT Skills and Salary Report 2024 – Two-thirds of leaders report skill gaps despite high certification rates.

AI Reduces the Value of Recall-Based Knowledge

Issue:
Generative AI now handles a large portion of what exams traditionally tested:

• Infrastructure as Code: AI generates ARM, Bicep, and Terraform modules for Azure resources.

• Automation Scripts: AI assists in writing Azure CLI, PowerShell, and Python for operations.

• Application Integration: AI drafts Azure Functions with Managed Identities and Graph API calls.

• Observability and Querying: AI builds KQL queries for Log Analytics.

• DevOps Pipelines: AI scaffolds YAML for Azure Pipelines or GitHub Actions.

This reduces the need for rote memorisation, which forms the basis of most certification exams.
As AI handles the “recall” layer, the remaining differentiators are verification, adaptation, and judgment — skills not assessed by certifications.

Reference:

• Stack Overflow Developer Survey 2024 – Around 75% of developers use or plan to use AI tools, but only 43% strongly trust AI accuracy.
  This highlights that AI accelerates drafting, but human verification remains essential.

Certifications Fail to Reflect Applied Competence

Issue:
Certifications measure the ability to absorb and recall vendor content, but not the capacity to:

• Deliver production-ready automation with Terraform, Ansible, and pipelines.

• Enforce governance, tagging, and policy compliance.

• Optimise cost, security posture, and performance across environments.

As a result, hiring managers treat certifications as baseline indicators, not proof of delivery capability.

Reference:

• Skillsoft IT Skills and Salary Report 2024:
  “Certifications remain valuable for structure and career entry, but do not guarantee job-ready skills.”

Shift Toward Demonstrated Delivery and Portfolio Evidence

Trend:
Employers increasingly value public repositories, live demos, and measurable project outcomes over certification counts.
Examples include:

• GitHub projects deploying secure, automated Azure environments.

• Dashboards showing cost optimisation or reliability metrics.

• Short video walkthroughs explaining architecture, trade-offs, and incident resolution.

Reason:
These demonstrate applied knowledge, troubleshooting, communication, and delivery — the dimensions AI and exams cannot replicate.

Reference:

• LinkedIn Emerging Jobs Report 2024 – “Demonstrated delivery and automation capability” cited as critical for cloud roles.

Hiring is moving to proof of work

Certifications are still listed as “important attributes” by many hiring managers, but employers are prioritising outcomes:

• Leaders report skill gaps, longer project timelines, and stress from under‑skilled teams. They invest in upskilling, practical labs, and real projects. They also plan more hiring, with certifications among the signals used to screen. Skillsoft / Global Knowledge 2024–2025 IT Skills & Salary

In practice, this shifts screening towards portfolios, GitHub repos, and case studies. Not tutorial repos. Not forks. Real projects with architecture decisions, IaC, CI/CD, tests, and production‑grade trade‑offs.

So… should you still get certified?

• If you’re new or switching roles: Maintain one or two role-aligned certifications to show structured learning and capability.

• Use certifications as curriculum: Apply the concepts by building the projects listed earlier. Learning is validated through implementation.

• If you’re mid/senior level: Focus on measurable outcomes — delivery, optimisation, security posture — not exam counts. That’s what demonstrates real ability.

What to build (and how to present it)

Aim for three to five projects that each prove a core cloud skill. Keep them small, end‑to‑end, and production‑ish. Some ideas for Azure project that demonstrating genuine expertise:

1.Azure Hybrid Infrastructure Automation Blueprint

Goal: Deploy a secure hybrid environment replicating an on-prem data centre in Azure.

Tech Stack:

• Terraform: Provision VNETs, subnets, NSGs, Azure Firewall, Bastion, and site-to-site VPNs.

• Ansible: Configure Windows and Linux VMs post-deployment (e.g., domain join, patching, agent install).

• Docker: Host a monitoring stack (Prometheus + Grafana) in containers on an Azure VM.

• Serverless SQL: Use Azure SQL Serverless to store performance metrics from Ansible runs.

• Security:

  • Implement Key Vault for secrets.

  • Azure Policy and Defender for Cloud for compliance.

  • Role-based access (RBAC) via Terraform.

Outcome: A fully automated, auditable infrastructure that mirrors an enterprise hybrid deployment.

2. Secure DevOps Platform on Azure (CI/CD with IaC)

Goal: Build an end-to-end DevOps pipeline using Azure DevOps and GitHub Actions with strong security controls.

Tech Stack:

• Terraform: Define core infrastructure – AKS, ACR, App Gateway, and Private Endpoints.

• Ansible: Configure AKS nodes and deploy container security agents (CrowdStrike / Defender).

• Docker: Containerise an internal web app or API for deployment to AKS.

• Serverless SQL: Store audit logs and build metadata for pipeline runs.

• Security:

  • Use Azure Key Vault + Managed Identities for secrets.

  • Enable Microsoft Entra workload identities for AKS pods.

  • Configure private links and NSG restrictions.

Outcome: A hardened CI/CD environment demonstrating DevSecOps in action.

3. Cloud Security Posture Management (CSPM) Lab

Goal: Build an environment that continuously audits and remediates Azure resources for misconfigurations.

Tech Stack:

• Terraform: Deploy multiple resource types (VMs, Storage, SQL, AKS) intentionally misconfigured.

• Ansible: Run compliance scans (CIS, DISA STIG) and remediate drift automatically.

• Docker: Deploy a custom compliance dashboard app (Flask/Streamlit) showing non-compliant resources.

• Serverless SQL: Store scan and remediation data for analytics.

• Security:

  • Integrate with Azure Security Centre and Defender.

  • Enable diagnostic logging and centralised Log Analytics workspace.

Outcome: A live, automated compliance system simulating real-world cloud governance.

4. Serverless Data Platform with Secure Pipeline

Goal: Automate a data pipeline that ingests CSVs from Azure Blob, processes them in containers, and stores data in Serverless SQL.

Tech Stack:

• Terraform: Create Storage Account, Event Grid, Container Apps, and Serverless SQL Database.

• Ansible: Configure container hosts, mount storage securely, deploy Docker workloads.

• Docker: Build a Python or Node.js app to transform CSVs.

• Serverless SQL: Store final structured data and generate cost-optimised analytics queries.

• Security:

  • Enforce private endpoints and TLS for SQL and Blob.

  • Use Managed Identity to connect container app → SQL.

Outcome: A secure, automated, and scalable data-processing solution using serverless architecture.

5. Automated Patch and Vulnerability Management System

Goal: Deploy a security-focused automation that patches Azure VMs and containers, stores results, and alerts on non-compliance.

Tech Stack:

• Terraform: Provision Windows/Linux VMs, Log Analytics, and Automation Accounts.

• Ansible: Execute OS patching, update Docker images, and validate compliance.

• Docker: Package custom patch-reporting service for deployment to Azure Container Apps.

• Serverless SQL: Log patch states, vulnerabilities, and remediation results.

• Security:

  • Integrate with Azure Sentinel for alerting.

  • Apply Key Vault for secrets and tokens.

  • Enforce least privilege via Terraform RBAC.

Outcome: A self-healing patching system demonstrating automation, observability, and security best practice.

How to Leverage AI Without Pretending to Know

• Use AI for drafting: Generate Bicep/Terraform, CLI, KQL, and pipeline YAML templates. This shows you can apply AI for design and prototyping.

• Verify outputs: Always run what-if, terraform plan, unit tests for modules, and small proof-of-concepts. Verification proves competence.

• Enhance quality: Focus on idempotency, tagging, policy compliance, error handling, retries, and timeouts. These improvements reflect engineering maturity.

• Document clearly: Record trade-offs, SLOs, and rollback steps in your README. Documentation demonstrates accountability.

Action Items for This Week

• Create dev, test, and prod subscriptions under a shared landing zone baseline. Demonstrates architecture and governance skills.

• Apply consistent policies and Defender plans across all subscriptions. Demonstrates operational control and security awareness.

• Create a reusable repo template — Bicep/Terraform modules, GitHub Actions workflows, linting, what-if/plan, and environment approvals. Demonstrates DevOps maturity.

• Select one project from your portfolio — deploy an MVP to dev, promote to test, run chaos and rollback drills, and document everything. Demonstrates delivery under real conditions.

• Add monitoring artefacts — dashboards, budgets, alerts, and runbooks. Demonstrates observability and sustainability.

• Record a 3-minute demo video explaining design, automation, and results. Demonstrates communication and presence

Final Thought

Azure certifications teach you the terrain. Hiring managers want to watch you drive.
AI reduces the value of memorisation — execution, troubleshooting, resilience, and cost awareness are what get you hired.
The fastest way to prove those skills is to build a production-like environment with working automation, visibility, and guardrails