300-910 DEVOPS: Retired Cisco DevOps Professional Exam

Cisco retired the 300-910 DEVOPS exam on February 2, 2026. Implementing DevOps Solutions and Practices using Cisco Platforms v1.1 had been a professional concentration in the former DevNet program, covering CI/CD pipelines, application packaging, infrastructure automation, cloud and multicloud deployment, observability, and secure software delivery. Cisco lists no direct replacement for the exam.

The retirement does not make the subject obsolete. In fact, Cisco’s current Automation portfolio places even more emphasis on infrastructure as code, Git-based workflows, CI/CD, containers, telemetry, and AI-assisted automation. The important distinction is certification status: 300-910 is historical, while the practices it tested are still widely used in modern network and platform engineering.

A useful way to study the old blueprint is to treat it as a systems-engineering workflow. Source code enters version control, automated tests validate it, a pipeline packages and deploys an application or infrastructure change, monitoring verifies the result, and security controls protect the process. Each stage creates evidence that the next stage can trust.

CI/CD is a controlled change system, not only a deployment shortcut

The DEVOPS blueprint devoted major attention to CI/CD pipeline design, build and deployment tools, test integration, troubleshooting, and release strategies such as canary, rollback, and blue/green deployment. The central idea is that change becomes safer when it is repeatable, testable, observable, and reversible. A pipeline should make assumptions explicit rather than relying on an engineer to remember a sequence of manual steps.

Candidates should still understand pipeline stages such as source, build, test, security validation, artifact creation, deployment, and post-deployment verification. When a pipeline fails, isolate whether the problem originates in code, dependencies, the runner, credentials, environment configuration, tooling compatibility, or the target platform. DevOps maturity comes from shortening the time to identify and correct the actual failure domain.

Containers package dependencies but do not remove operational responsibility

The retired exam covered Dockerfiles, containerization, microservices, golden images, and application packaging. Containers improve consistency because the application and its runtime dependencies travel together, but the resulting image still needs secure construction, versioning, vulnerability management, resource limits, configuration, and lifecycle control.

Microservices add another layer of operational complexity. Services need network connectivity, service discovery, secrets, health checks, telemetry, and a strategy for partial failure. A diagram with many small services is not automatically better architecture. The design should reflect real business boundaries and should be supportable by the team that will operate it.

Infrastructure as code makes network and platform state reviewable

DEVOPS required candidates to understand Ansible, Terraform, infrastructure as code, and pre-change validation. These practices are especially important in networking because an infrastructure change can affect many applications at once. Representing intended state as code enables peer review, automated testing, version history, and repeatable deployment across environments.

Terraform and Ansible solve different parts of the problem. Terraform is often used to declare resource state and lifecycle, while Ansible is frequently used for configuration and orchestration. Neither tool replaces domain knowledge. A syntactically correct playbook can still apply the wrong routing policy or firewall rule, so the pipeline needs validation that understands network behavior, not only code format.

Kubernetes introduced orchestration, desired state, and distributed failure modes

The old blueprint expected candidates to describe Kubernetes concepts, deploy applications, use objects to meet requirements, and validate deployment success. Kubernetes makes desired application state explicit through objects such as deployments, services, configuration, and storage resources. The control plane continually works to reconcile actual state with that desired state.

That model changes troubleshooting. A failed application may involve the image, scheduling, resource limits, readiness probes, service routing, DNS, network policy, storage, or the application itself. The Kubernetes administration discipline remains a useful supporting concept because it teaches candidates to reason about clusters as systems rather than as a collection of containers.

Cloud and multicloud design adds cost, portability, and dependency decisions

DEVOPS covered deployment to multiple environments, high availability, disaster recovery, portability testing, public-cloud cost tracking, and build-versus-buy decisions. Multicloud should therefore be understood as an architecture choice rather than a slogan. Every additional provider or environment creates differences in identity, networking, managed services, logging, cost models, and operational tooling.

Portability has levels. A container image may run in several environments while the surrounding database, load balancer, secrets service, observability stack, and identity integration remain provider-specific. A strong design identifies which components truly need portability and which are better served by native managed services. Cost visibility belongs in the same discussion because operational flexibility can be expensive when resources are duplicated without clear business value.

Artifact provenance is part of safe delivery. A deployment should make it possible to trace the running image or configuration back to the source commit, build job, test results, and approval that produced it. Without that chain, rollback becomes guesswork and incident responders may not know exactly what changed between a healthy and unhealthy release.

Configuration and secrets should be separated from the application image. Environment-specific values, credentials, and keys need a controlled distribution mechanism and should not be baked into reusable container images. This improves portability and reduces the chance that development credentials or sensitive configuration reach production unintentionally.

Observability connects logs, metrics, traces, and user impact

The blueprint included log aggregation, search and reporting, application performance monitoring, health checks, dashboards, alerts, and Kubernetes monitoring. These topics support a simple principle: deployment is not complete when a pipeline reports success. The team needs evidence that the application is healthy, responsive, and delivering the expected user outcome.

Good observability starts with questions. Which metric indicates customer impact? Which logs reveal why a service failed? What baseline separates normal variation from degradation? Alerts should point to conditions that require action, not generate constant noise. The old exam’s focus on monitoring remains relevant because automation without feedback can scale mistakes faster than manual work.

Chaos engineering, which appeared in the old blueprint, is useful when applied carefully. The objective is not to break production for entertainment; it is to test assumptions about redundancy, retries, timeouts, dependency failure, and recovery. Start in controlled environments, define a measurable hypothesis, limit blast radius, and stop the experiment when safety thresholds are crossed.

Network behavior is part of application reliability. Kubernetes services, ingress, DNS, load balancers, firewall policy, and cloud routing can all determine whether an otherwise healthy application is reachable. NetDevOps practices are valuable because they bring network validation into the same pipeline that validates application and infrastructure changes.

DevSecOps moves security controls into the delivery process

DEVOPS explicitly covered secure software development and methods to secure applications and infrastructure during production and testing. Security therefore belongs inside the pipeline: secret scanning, dependency checks, image scanning, policy tests, code review, least-privilege credentials, and environment separation should occur before deployment wherever possible.

This does not mean every security decision should be automated. High-impact production changes may still require approval, and automated findings need context. The broader DevOps skill set is strongest when development speed and operational control reinforce each other rather than becoming competing goals.

GitOps-style workflows extend version control beyond application code by making a repository the reviewed source for infrastructure intent. The important concept is not a brand name; it is that proposed changes are visible before deployment and that an automated system can compare live state against approved state. This makes drift and unauthorized manual changes easier to detect.

Service-level objectives give observability a business target. Instead of alerting on every metric threshold, teams can define availability or latency objectives that reflect user expectations, then use error budgets to decide when reliability work should take priority over new deployment speed. This connects pipeline velocity to actual service quality.

The current Cisco automation path absorbs many of the same practices

Cisco’s current 350-901 AUTOCOR core covers infrastructure as code, operations, automation-system design, and AI, while 200-901 CCNAAUTO provides the current associate-level software and API foundation. Domain concentrations such as 300-435 ENAUTO then apply automation inside specific technology areas. These are current paths with overlapping skills, not direct replacements for 300-910.

The historical DevNet Professional page explains the program lineage, but candidates planning exams now should use Cisco’s Automation structure. The shift reflects a broader view of automation as a production network discipline rather than a separate developer specialty.

Release promotion should be evidence-based. A build that passed in development should carry the same immutable artifact into later environments rather than being rebuilt differently for production. Environment-specific configuration can change, but the application artifact should remain traceable so that test results still describe what is actually deployed.

Keep the lab intentionally small enough to understand. One repository, one pipeline, one containerized service, one infrastructure target, and one monitoring path are enough to demonstrate the lifecycle. Complexity should be added only when it teaches a new failure mode or operational control.

Use the retired exam as a blueprint for a small production-grade delivery system

A practical project can cover nearly the entire old exam: store infrastructure code in Git, use a pipeline to validate it, build a container, deploy it to Kubernetes, create infrastructure with Terraform, configure components with Ansible, expose health metrics, collect logs, and test rollback. Add secret management and security checks before treating the project as complete.

That hands-on loop is more valuable than memorizing tool names. It teaches how state moves from source code to a running system and how engineers prove that the result is correct. A focused Terraform review can deepen the infrastructure-as-code portion, but the central DEVOPS skill is integrating many tools into one dependable lifecycle.

Repeat the workflow after each tooling change so that reliability improves with the platform instead of decaying over time.