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DevOps Tools Engineer 701-200: Modern Delivery from Git to Kubernetes and Observability

Exam 701-200 is Linux Professional Institute’s current version 2.0 exam for the DevOps Tools Engineer certification. LPI does not require another certification to earn it, although the institute recommends active system-administration or software-development knowledge around the LPIC-1 level. The exam is designed for professionals who build, deliver, and operate software through collaborative, automated processes using open-source technology.

Version 2.0 modernizes the original DevOps blueprint substantially. It covers software architecture, cloud service models, Git, CI/CD, licensing and software composition, Docker and Podman, container images, Kubernetes, Helm, cloud-native security, Prometheus, logging stacks, and distributed tracing. The exam’s breadth reflects how DevOps has moved from a collection of build and configuration tools toward an operating model for cloud-native systems.

Software architecture determines whether delivery can be automated cleanly

The first objective area asks candidates to reason about service-based applications, APIs, persistence, sessions, transactions, concurrency, availability, scaling, load balancing, messaging, and cloud-native properties. A deployment pipeline cannot compensate for software that assumes fixed hosts, hides health state, or stores critical data on an ephemeral local filesystem.

Design a small service with an explicit API, externalized configuration, defined health behavior, and a clear persistence boundary. Then ask how it would scale horizontally or recover after replacement. Those questions turn architectural vocabulary into operational constraints and make later Kubernetes or container topics much easier to understand.

Cloud platforms are treated as composable capabilities rather than brands

701-200 expects familiarity with object storage, relational and NoSQL databases, message systems, analytics services, IaaS, PaaS, SaaS, FaaS, content delivery, and identity and access management. The objective lists representative open-source and common service technologies but emphasizes the properties of the capability.

For each service category, know what state it holds, how clients authenticate, how failure appears, how it scales, and what operational responsibility remains with the team. Moving from a self-managed database to a managed service may reduce patching work while making quotas, API limits, backups, and provider identity controls more important.

Git remains the source-of-truth layer for code and configuration

The current exam retains practical Git work with branches, tags, remotes, submodules, merges, and conflict resolution. The deeper DevOps implication is traceability. Application code, deployment definitions, container build files, and infrastructure configuration should be associated with reviewed changes that can be connected to a release.

A useful exercise is to make one production change entirely from versioned material. Commit the application update, container definition, and deployment change separately, review the diff, tag the release, and then reconstruct what changed from history. If an incident occurs later, that history provides a much better starting point than an administrator’s memory.

CI/CD now includes GitOps and controlled deployment patterns

Version 2.0 reduces tool-specific Jenkins depth and emphasizes the principles of continuous integration and delivery, build and test stages, artifacts, caches, semantic versioning, GitOps, staged environments, feature toggles, preview releases, reconciliation loops, and blue-green or canary deployments. Jenkins and GitLab CI appear as representative platforms rather than as the entire subject.

A pipeline should produce immutable, identifiable artifacts and promote them through environments without rebuilding different binaries at each step. Deployment strategy should match risk: a canary release exposes a small fraction of users first, while blue-green keeps two complete environments for fast switching. Rollback must be designed before a release, especially when database schemas or persistent state change.

Software composition and licensing are also part of delivery risk.

The current blueprint adds software dependencies, package managers, open-source licensing, license compatibility, and software bills of materials. Modern services are assembled from libraries, containers, base images, operating-system packages, and transitive dependencies. Teams need to know what they are shipping, not only what they wrote.

Track a small application’s dependency tree and note which components enter the final artifact. Record versions and licenses, then update one dependency and observe what changes. This exercise also supports supply-chain security because an SBOM and reproducible build make it easier to identify whether a newly disclosed vulnerability is actually present in the deployed software.

Application containers cover both Docker and Podman operating models

701-200 includes Docker and Podman architecture, image registries, container lifecycle, networks, DNS service discovery, volumes, rootless operation, Compose, and image building through Dockerfiles or Containerfiles. Candidates should understand both the user-facing commands and the persistent or network resources created underneath.

Build an image, run two services on a private network, attach persistent storage, and then recreate the containers. The container-image deployment workflow can provide additional context, but the LPI objectives are more general: know what belongs in the image, what must be injected at runtime, and what data must survive replacement.

Kubernetes introduces declarative desired state and controllers

Kubernetes is now a dedicated exam domain. Candidates should understand cluster components, kubectl configuration and common operations, Pods, Deployments, Services, Ingress, PersistentVolumeClaims, ConfigMaps, Secrets, StatefulSets, DaemonSets, Jobs, CronJobs, scaling, rolling updates, and the use of YAML to declare resources.

The central idea is reconciliation. A Deployment is not a script that starts a process once; it is a declaration the controllers continually try to satisfy. The management of Kubernetes objects becomes coherent when candidates understand owner relationships, desired replicas, rollout state, and the difference between changing an object and imperatively repairing an individual container.

Helm and GitOps connect application packaging with cluster delivery.

The exam covers Helm charts, releases, values, installation, upgrade, and removal, plus awareness of Kustomize, Flux CD, and Argo CD. These tools address the problem of packaging and reconciling larger application configurations rather than editing dozens of Kubernetes manifests manually.

Practice by installing a small chart with default values, overriding a few settings, upgrading it, and then reviewing which cluster objects changed. Keep those values in version control. This makes a release reproducible and prepares candidates to understand how GitOps systems use a repository as the declared source of desired cluster state.

Cloud-native security begins before the application reaches the cluster

701-200 explicitly includes infrastructure threats, supply-chain risk, application vulnerabilities, certificates, TLS, authentication and authorization standards, SSO, OAuth2, OpenID Connect, SAML, MFA, password hashing, API permissions, rate limits, and common web risks. DevOps teams influence these controls through build and deployment automation, so security cannot be left to a final manual review.

A practical exercise should verify the provenance of a base image, scan it, run the workload with a non-root identity where possible, limit secrets exposure, and protect deployment credentials. Then test the application’s authentication path separately from cluster access. This helps distinguish software security, runtime security, and pipeline security as related but different control planes.

Prometheus, logs, and tracing form an observability stack

The current exam goes beyond basic monitoring. Prometheus objectives include exporters, service discovery, Alertmanager, Grafana awareness, PromQL, labels, instant and range vectors, and monitoring containers and microservices. The Prometheus monitoring model is most useful when candidates learn to ask a question first and then choose the metric and labels needed to answer it.

Logging objectives cover syslog, journald, Elasticsearch or OpenSearch, Logstash, Beats, Fluentd, Fluent Bit, Kibana, Loki, promtail, Grafana, and Graylog awareness. Tracing adds OpenTelemetry, spans, trace context, attributes, Jaeger, and Tempo. Metrics show patterns, logs explain events, and traces connect work across distributed services. None is a complete substitute for the others.

Version 2 replaces the older tool list with a cloud-native operating model

The retired 701-100 exam ended on June 30, 2026. It emphasized Jenkins, Ansible, Vagrant, and Packer more directly. Those tools remain useful in real environments, but the current exam shifts weight toward container ecosystems, Kubernetes, security, software composition, and observability. Old study material should therefore be checked carefully before being reused.

Candidates who come from development should strengthen Linux operations, networking, identity, storage, and troubleshooting; the LPIC-1 track can provide that foundation. Administrators should strengthen source control, software architecture, APIs, testing, and release mechanics. DevOps competence lives in the overlap.

A final project can exercise almost the entire blueprint: store an application in Git, build and tag an OCI image in CI, publish it, deploy it to Kubernetes with declarative YAML or Helm, secure the runtime identity, expose it through a service, collect Prometheus metrics and logs, add a trace, and perform a controlled rollout. Then deliberately break DNS, credentials, storage, or a deployment value and follow the evidence. That is the practical meaning of bridging development and operations.

Container-image construction should minimize unnecessary attack surface and maximize repeatability. Use explicit base-image versions, a small build context, a meaningful .dockerignore, non-root users where practical, and multi-stage builds when they reduce runtime contents. Rebuild periodically so security updates enter the artifact through the same pipeline rather than through undocumented changes inside a running container.

Kubernetes troubleshooting should move from desired state to observed state. Inspect the Deployment, ReplicaSet, Pods, events, container logs, service selectors, endpoints, and Ingress path in order. Restarting Pods repeatedly may hide the underlying cause if the controller keeps recreating the same bad configuration. The exam’s kubectl objectives are most useful when every command answers a specific hypothesis.

Prometheus queries should also start from service questions. A counter of total requests is useful only when converted into a rate over a relevant interval; a latency histogram becomes valuable when interpreted through buckets or quantiles appropriate to the service target. Labels should add useful dimensions without creating unbounded cardinality that makes the monitoring system expensive and unstable.

Distributed tracing becomes most valuable when a request crosses several services. Instrument a small two- or three-service path, propagate trace context, and inspect a slow request. Compare what the trace reveals with metrics and logs from the same time window. This shows why observability is not one dashboard but a set of correlated evidence sources.

Release engineering should include database change strategy. A container can be rolled back quickly, but a destructive schema migration may not be reversible. Prefer backward-compatible phases when possible: add new structures, deploy code that can use both forms, migrate data, then remove the old path later. This is the kind of cross-boundary judgment that separates automated delivery from simply running scripts faster.

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