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The LPI DevOps Tools Engineer 701-200 exam is the current version 2.0 certification exam for professionals working across software delivery and operations. Compared with the previous 701-100 blueprint, version 2.0 is broader and more explicitly cloud-native: it covers modern software engineering, Git, CI/CD, software composition and licensing, Docker and Podman, Kubernetes and Helm, security, Prometheus monitoring, logging, and distributed tracing.
LPI does not impose a certification prerequisite, but it recommends that candidates maintain competence equivalent to an active certification in system administration or software development. That recommendation matters because the exam is about connecting disciplines. Someone who understands only pipeline syntax but not applications, Linux, networking, or runtime behavior will struggle to reason across the delivery lifecycle.
Prepare by building one small service through the whole chain. The same application can teach source control, build automation, container images, deployment, Kubernetes resources, secrets, monitoring, logs, and rollback. Integration is the skill the exam is trying to measure.
LPI’s version 2.0 objectives begin with software design: services, APIs, persistence, sessions, transactions, concurrency, security, performance, availability, scaling, messaging, and cloud-native characteristics. That is deliberate. Operations tools make more sense when the candidate understands the behavior of the software they are delivering.
Take a simple service and identify stateful versus stateless behavior, synchronous versus asynchronous dependencies, and what must happen during a rolling deployment. Ask how the application handles retries, schema changes, or two versions running at once. These design choices determine whether automation can deliver safely.
Include failure-tolerant design in the software-engineering exercises. Ask what happens when a database is slow, a message is delivered twice, an external API times out, or two application versions are active during a rollout. Delivery tooling can only automate safely when the application has sensible behavior under partial failure.
The exam expects practical source-control knowledge, including repositories, branches, tags, remotes, submodules, merges, and conflicts. Use Git command-line practice to build workflows rather than isolated command memory.
Create a feature branch, merge conflicting work, tag a release, inspect a regression, and revert a faulty change. Then connect the repository to the delivery pipeline. This demonstrates why version control is the source of change history for both application and infrastructure definitions.
Repository policy matters too. Protect release branches, require review for sensitive infrastructure changes, and keep credentials out of source history. Practice removing a mistakenly committed secret and then explain why rotation is still required: deleting the file from the latest revision does not erase exposure from earlier history or clones.
Continuous integration and delivery include builds, tests, artifact management, deployment strategies, semantic versioning, GitOps awareness, and platforms such as Jenkins. The key idea is provenance: the artifact that passed tests should be the artifact that moves toward production.
Design a CI/CD pipeline with visible gates. Separate code validation, build, unit tests, integration tests, image creation, security checks, and deployment. Then practice blue-green or canary thinking: how would traffic shift, how would health be measured, and what event should trigger rollback?
Artifact promotion should make it possible to answer “what exactly is running?” Tag images or packages immutably, record checksums where appropriate, and connect deployments to source revisions. This strengthens both rollback and incident analysis because operators can compare the failing environment with the build that produced it.
Version 2.0 explicitly covers dependencies, software bills of materials, proprietary versus open-source software, and common license models. This reflects the reality that modern applications are assembled from large dependency graphs. Delivery teams need to know what they are shipping, not only whether it compiles.
Create an inventory for a small application and identify direct and transitive dependencies. Ask where versions are pinned, how vulnerable components would be found, and whether license obligations are compatible with distribution. This connects governance to the same artifact pipeline used for testing and deployment.
Software bills of materials become useful when they feed decisions. Generate or inspect one for a small project, identify a vulnerable dependency, and trace whether it is actually included in the deployed artifact. Then update the dependency and verify the new build. This closes the loop between inventory, remediation, and release evidence.
Container objectives cover running images, networks, volumes, registries, rootless awareness, Docker Compose or Podman Compose, and image building. Study container image construction as a reproducibility problem: every layer should have a reason, build context should be controlled, and runtime configuration should remain outside the image where appropriate.
Networking deserves its own lab. Use container networking to trace service discovery, published ports, internal networks, and persistent volumes across a multi-container application. If the application works only because all containers share the host network or local filesystem, the architecture has hidden coupling.
Rootless container operation is worth testing because it demonstrates how runtime privilege can be reduced. Compare the filesystem, networking, and port implications with a rootful runtime. The goal is not to declare one mode universally correct, but to understand which host privileges a workload genuinely needs.
The current exam includes Kubernetes architecture, kubectl usage, Pods, Deployments, Services, Ingress, persistent volume claims, ConfigMaps, Secrets, and other workload types. A clear understanding of Kubernetes cluster architecture is necessary because failures often originate in the relationship between controllers rather than inside the application process.
Practice reading state rather than immediately changing it. Compare desired replicas with actual pods, inspect events, describe resources, check logs, and verify service selectors. This creates a disciplined troubleshooting sequence and reinforces the controller reconciliation model.
Kubernetes troubleshooting should also include configuration rollout. Change a ConfigMap or Secret reference, update a deployment, and observe whether existing pods receive the change automatically. This teaches the boundary between stored configuration and process lifecycle and prevents assumptions that a changed object always changes running application state.
Helm charts add templates, values, releases, upgrades, and rollback to the deployment workflow. They reduce repetition, but a candidate still needs to understand the Kubernetes resources being rendered. When a release fails, the problem may be in values, templates, generated manifests, cluster policy, image availability, or the application.
Render charts locally during study and inspect the resulting YAML. Change values deliberately and predict what resources will change. This keeps Helm from becoming a black box and makes upgrade behavior easier to reason about.
Helm upgrades deserve rollback practice. Install a release, change values to produce an unhealthy deployment, inspect revision history, and roll back. Then determine whether application data also needs recovery. Package rollback and data rollback are separate problems, and reliable operations must account for both.
Version 2.0 includes infrastructure threats, application risks, dependency and supply-chain security, public-key concepts, certificates, authentication standards, MFA, and secure credential handling. Security is not a final scanner stage; it influences repository permissions, build workers, artifacts, deployment identities, runtime secrets, and network exposure.
Create a simple threat model for the delivery chain. What happens if a developer token leaks, a build dependency is compromised, a registry image is replaced, or a deployment service account has excessive privileges? Map preventive and detective controls to each point.
Pipeline security should protect automation identities with the same care as administrator accounts. A CI runner or deployment controller may have broad access to repositories, registries, cloud APIs, or clusters. Apply least privilege, short-lived credentials where possible, isolation between untrusted builds, and logging that makes sensitive actions attributable.
The current objectives include Prometheus architecture and PromQL, common exporters, logging stacks, Grafana, and OpenTelemetry tracing. Reviewing the Prometheus and Grafana stack is most useful when tied to service questions: is the application available, is latency changing, which dependency is failing, and where did a request spend its time?
The previous 701-100 exam remained available until June 30, 2026, so older material is still common. Use it for foundational Git, automation, and delivery concepts, but use 701-200 as the authoritative blueprint. The modern exam expects a professional who can connect code, containers, orchestration, security, and observability into one operating system for software delivery.
Build an observability lab around one request. Use metrics to notice rising latency, logs to reveal an application error, and a trace to show which downstream call consumed the time. The three signals answer different questions. Learning to combine them is more valuable than memorizing the components of an observability stack separately.
Do not neglect rollback dependencies. Reverting an application image may be easy while reverting a database schema is not. Practice forward-compatible schema changes, phased deployments, and the idea of separating destructive cleanup from the initial release. This is where software engineering and delivery engineering meet most clearly.
Prometheus practice should include alert quality as well as queries. Create an alert for a user-impacting symptom, trigger it deliberately, and decide whether the message contains enough context for another operator to act. Alert fatigue usually begins when thresholds measure noisy infrastructure behavior rather than a condition that requires intervention.
Tracing becomes particularly valuable in service-based applications. Add two dependent services, propagate trace context, and introduce latency in the downstream service. A distributed trace should reveal where the time is spent, while metrics show the broader pattern and logs provide detailed events. Understanding those complementary roles is central to modern operations.
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