LPI 701-200 Practice Test Questions, LPI 701-200 Exam Practice Test Questions

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LPI 701-200 DevOps Tools Engineer

701-200 is the current version 2.0 exam for the Linux Professional Institute DevOps Tools Engineer certification. As of October 2026, the Linux Professional Institute lists a 90-minute exam with 60 multiple-choice and fill-in-the-blank questions, no prerequisite certification, and a five-year validity period. LPI recommends that candidates also maintain solid competence in a primary discipline such as system administration or software development.

Version 2.0 replaced 701-100 after June 30, 2026. The current blueprint keeps source control and CI/CD but places modern application design, container technologies, orchestration, configuration, security, and observability at the center. LPI specifically names technologies such as Docker and Podman, Kubernetes, Helm, Prometheus, and Git.

The exam should be prepared as a delivery-system exam rather than a collection of tool trivia. A modern service is designed for automation, stored in version control, built into an artifact, deployed into an environment, configured, observed, and secured. If any stage requires an undocumented manual step, the delivery process is harder to reproduce and troubleshoot.

Modern software architecture shapes the rest of the toolchain

Understand service-based applications, APIs, persistence, sessions, transactions, scaling, load balancing, messaging, and cloud-native design. The ideas in cloud-native technologies help connect architecture with runtime expectations. A stateless service, for example, is easier to scale horizontally only if persistent state has been placed in an appropriate external service.

Design a simple application on paper with an API, persistent database, cache or queue, and a background worker. Mark which components hold state, which can be restarted freely, and which credentials or network policies they need. This architecture exercise creates context for later questions about containers, secrets, monitoring, and deployment because every tool is serving an application requirement.

Git should make changes traceable and automation-friendly

Candidates need working knowledge of repositories, branches, tags, remotes, submodules, merging, and conflict resolution. Use Git commands and workflow to practice, but build habits around review and reproducibility. A deployment should be traceable to a commit or tag, and a rollback should identify a known prior state rather than depend on someone remembering which files changed.

Put application code, container definitions, and deployment configuration under version control, then use a pull-request-style workflow. Make a branch, change one behavior, update its tests, merge after review, and tag the result. The exercise links collaboration with automation: a pipeline can only make reliable decisions when the repository expresses clear versions and ownership.

CI/CD connects source changes to controlled releases

The current objectives cover builds, unit/integration/acceptance tests, artifacts, delivery, deployment, GitOps, semantic versioning, and deployment patterns such as blue-green and canary. A strong conceptual refresher is CI/CD pipeline design, but the exam demands that you understand why each stage exists.

Build a pipeline that produces an immutable artifact once and promotes that same artifact between environments. Do not rebuild different binaries for test and production if the goal is to prove that production receives what was tested. Add a manual or policy gate, record metadata such as commit and version, and demonstrate how a failed test or health check prevents promotion.

Containers are now a core runtime model

701-200 expects operation of Docker and Podman containers, networks, volumes, image registries, Dockerfiles or Containerfiles, image security, and Compose-style multi-container applications. Review container image construction and Docker Compose with focus on reproducibility, least privilege, and persistent state.

Practice building an image as a non-root user where possible, using a small base, pinning important dependencies, excluding secrets, and separating configuration from the image. Run two services on an isolated network with a named volume, recreate the containers, and prove that persistent data survives only because it lives outside the ephemeral container layer.

Kubernetes and Helm add declarative orchestration

The version 2.0 track includes orchestration concepts beyond a single host. Understand Kubernetes objects such as Pods, Deployments, Services, configuration and secrets, then learn how Helm charts package deployable configuration through templates and values. Focus on reconciliation: the platform repeatedly compares desired state with actual state and acts to reduce the difference.

A useful lab is to deploy a small stateless service with multiple replicas, expose it through a Service, update the image, and watch a rolling replacement. Then change a configuration value through the declared manifest or chart rather than editing a running container. This teaches why declarative systems require administrators to change the source of truth instead of patching runtime state manually.

Infrastructure and configuration automation should have clear ownership

Current DevOps work still depends on infrastructure as code and configuration management. The principles in Terraform for DevOps and Ansible automation illustrate two layers: one commonly allocates infrastructure resources, while the other can converge operating-system or application configuration. Avoid duplicating the same responsibility across both unless the design has a clear reason.

Create a small environment definition, apply it, then change one parameter and inspect the planned difference before applying. After the resources exist, run a configuration playbook twice and verify that the second run has minimal or no change. These two behaviors—previewable infrastructure changes and idempotent configuration—are central to safe automation.

Security and software supply chains must be part of delivery

Version 2.0 includes software composition, licensing, supply-chain risk, authentication, authorization, TLS, certificates, CVEs, and common application risks. Track dependencies and know what enters the build. An SBOM is useful because it records component composition, but it does not by itself prove that components are trusted, patched, correctly licensed, or securely configured.

Protect the pipeline credentials, restrict registry access, scan images or dependencies, and keep secrets out of source control and image layers. The cryptographic foundation in PKI matters because automated systems also need machine identities and trusted channels. Treat pipeline security as production security: an attacker who controls the build can bypass many downstream controls.

Observability combines metrics, logs, and tracing

Prometheus, Grafana, logging stacks, and OpenTelemetry appear in the current objective landscape because distributed services need evidence across many components. Logging and monitoring should be paired with metrics and traces so that a symptom can be correlated with resource behavior, application events, and the path of one request across services.

Instrument a small application with a request counter, latency measure, structured logs, and a trace identifier. Deploy a faulty version, observe the alert or metric change, use logs to identify the error, and follow a trace to the failing component. Then roll back or fix the change through the normal pipeline. That closed loop is the practical center of DevOps operations.

Service-level thinking should remain visible throughout preparation. Define an availability target, a latency target, and one correctness indicator for the lab application, then choose metrics that actually measure those properties. A CPU graph is useful only if resource pressure explains user impact. This prevents observability from becoming a dashboard-collection exercise and teaches candidates to connect telemetry to the service contract that development and operations jointly own.

A useful final security exercise is to trace one secret from creation to use. Identify where the secret is stored, how a pipeline retrieves it, how the deployment passes it to a workload, how rotation occurs, and where accidental logging could expose it. Repeat the exercise for a short-lived token or certificate. This connects identity, delivery automation, runtime configuration, and observability while highlighting why secrets should not be embedded in repositories or images.

Prepare by rehearsing the whole delivery chain

For final review, begin with an empty repository and work all the way to an observable deployment: commit code, build and test, create an image, store it, deploy it, configure the environment, expose it, monitor it, and recover from a bad release. Record which artifact or configuration source owns each step. This exercise exposes gaps faster than reading another list of commands.

701-200 is broad because DevOps itself crosses boundaries. The strongest candidate is not the person who has used every named product; it is the person who can explain how modern software moves safely and repeatably from source to service, how automation reduces undocumented drift, and how security and observability provide feedback throughout that lifecycle.

A final rehearsal should include failure injection. Break a unit test, publish an image with the wrong tag, deny a deployment permission, remove a required configuration value, and trigger an alert condition in a disposable environment. For each failure, identify where the pipeline should stop, what evidence an operator sees, and what action restores service. This is a practical way to connect CI/CD, containers, automation, security, and observability without treating them as separate tool lists.

Artifact traceability is another useful end-to-end check. Starting from a running workload, work backward to the image or package, build job, source revision, dependency set, and approval or policy decision that produced it. Then rotate one secret or rebuild one dependency and verify that the new artifact can be distinguished from the old one. The exercise reinforces supply-chain reasoning and makes rollback decisions safer because the deployed state is tied to identifiable inputs.

Use the objective weights to decide where repeated hands-on practice is most valuable, but do not convert them into a shortcut for ignoring lower-weight topics. The current exam expects a connected DevOps workflow. A candidate who can explain why a tool exists, what state it owns, what evidence it produces, and how it fails will be better prepared than one who memorizes commands without understanding the delivery system around them.

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