300-635
Automating Cisco Data Center Solutions (DCAUTO)
Automating Cisco Data Center Solutions (DCAUTO) Exam Guide
The current Cisco listing identifies this certification exam as 300-635 DCNAUTO, Automating Cisco Data Center Networking Solutions v2.0, rather than the legacy DCAUTO title. It validates practical knowledge of data-center automation across infrastructure as code, network-element programmability, operations, and AI in automation. This guide helps you decide whether your preparation should follow the current v2.0 blueprint, how to allocate study time across its domains, and which hands-on skills to practise before scheduling.
Confirm which exam you are preparing for
Start with the current exam record, not an older page or course description. Cisco currently lists 300-635 DCNAUTO, Automating Cisco Data Center Networking Solutions v2.0. The older 300-635 DCAUTO v1.1 material remains useful for understanding the certification’s history, but it should not be treated as the current blueprint without checking Cisco’s latest information.
The naming change matters because the current exam includes technologies and topics that are not expressed by the legacy title. The v2.0 blueprint specifically includes Cisco NX-OS, Cisco Nexus Dashboard, Cisco ACI in network-centric mode, and Cisco Nexus Hyperfabric.
A practical first action is to create a study folder containing the current exam page and current exam-topics PDF. Label any older DCAUTO notes as legacy material. This prevents a common preparation error: investing heavily in a familiar course outline while overlooking the current blueprint’s emphasis on operations, infrastructure as code, and newer automation platforms.
What the older material can and cannot do
The legacy v1.1 blueprint covered programming concepts, orchestration, and automation tools for implementing automated data-center solutions. Those subjects still provide useful foundations, especially if you need to strengthen Python, automation workflow, or tool knowledge.
However, legacy material is not a substitute for mapping your preparation to v2.0. Cisco’s current exam listing identifies the current certification outcome as Cisco Certified Automation Specialist – Data Center Automation and Programmability, while the legacy course page associated passing DCAUTO v1.1 with Cisco Certified DevNet Specialist – Data Center Automation and Programmability. Treat those as different certification contexts rather than interchangeable labels.
Who benefits most from this exam
This exam is best suited to candidates who already understand data-center networking and want to automate configuration, provisioning, validation, and operational workflows. It is particularly relevant to engineers working with Cisco Nexus environments, ACI, automation pipelines, programmable interfaces, or infrastructure-as-code practices.
The exam can also serve a broader certification plan. The current exam satisfies the concentration-exam requirement for both the CCNP Data Center and CCNP Automation certifications. Cisco also states that it can be used toward recertification requirements.
The audience should not be defined only by job title. A network engineer who can read a playbook but cannot explain idempotence, model selection, or failure handling will need a different preparation plan from an automation engineer who writes code comfortably but lacks familiarity with NX-OS or ACI. Use the blueprint to identify the gap rather than assuming that experience in one tool covers the entire exam.
A useful readiness test
You are closer to exam readiness if you can explain why you would choose an API, model, orchestration tool, or validation method for a particular data-center task—not merely reproduce syntax. You should also be able to follow the flow from intended state, through automation code, to device response and operational verification.
Before buying a course or scheduling the exam, write down three automation workflows you can perform and three you can only describe. Include at least one workflow involving a network device interface, one using an orchestration or infrastructure-as-code tool, and one involving troubleshooting. The gaps in that list should determine your first study activities.
Use the blueprint to set study priorities
Allocate the largest share of study time to the three current domains weighted at 25% each: Infrastructure as Code, Network Element Programmability, and Operations. Network Automation Foundation carries 15%, so it deserves deliberate coverage but should not consume the time required for the three larger domains.
These percentages are blueprint weights, not a promise about the number or form of questions. They are most useful for deciding how to distribute revision, labs, and review. A candidate who spends nearly all preparation time on basic Python or tool installation may feel productive while leaving the higher-weight operational and programmability areas underdeveloped.
Network Automation Foundation — 15%
The Network Automation Foundation domain is the base layer for the rest of the exam. Review the purpose of automation, the relationship between network state and intended state, and the role of APIs, data models, version control, and repeatable workflows.
Study this area as a set of decisions. Ask what makes a task suitable for automation, what information must be represented as data, how a change should be reviewed, and how success should be verified. These questions connect foundation concepts to the more implementation-focused domains.
Do not let the lower blueprint weight encourage superficial reading. Weak foundational reasoning can make every other topic harder, particularly when a scenario asks you to select between a device-native mechanism, a model-driven interface, an Ansible workflow, or a Terraform-based approach.
Infrastructure as Code — 25%
Infrastructure as Code is one of the current domains weighted at 25%. Focus on the lifecycle of declaring infrastructure, reviewing a proposed change, applying it, and checking the resulting state rather than memorizing isolated commands.
The current topics include Jinja2 templates, Ansible playbooks, Terraform plans, GitOps, and troubleshooting Ansible- and Terraform-based automation. Prepare to distinguish the roles of templates, playbooks, plans, state, repositories, and execution environments in a controlled workflow.
A strong lab exercise is to represent a small, repeatable data-center change in more than one style. Compare what the template or configuration describes, how variables are supplied, what the tool determines should change, and how you would detect drift or an incomplete result. The goal is not to claim that one tool is always superior; it is to understand the consequences of the chosen workflow.
Network Element Programmability — 25%
Network Element Programmability is another 25% domain. Build a clear mental model of how a controller or automation client communicates with a network element, how a data model describes configuration or telemetry, and how the response or error should be interpreted.
The current topics include OpenConfig, IETF and native YANG models, NETCONF, gNMI, gRPC, gNOI, YANG Suite, and pyang. Organize these into related groups instead of studying them as an unconnected vocabulary list: models and validation, configuration or retrieval protocols, RPC and operational interfaces, and tools for inspecting or working with models.
For each interface, ask what problem it addresses, what kind of information it carries, how a model shapes the request, and what evidence would show that the requested state was achieved. This approach is more durable than memorizing a single example payload.
Operations — 25%
Operations is weighted at 25% and should be studied as the control loop that makes automation dependable. It includes observing outcomes, validating state, handling errors, troubleshooting execution, and deciding how a workflow should respond when the environment differs from assumptions.
Build scenarios around partial failure. A playbook may complete on one device and fail on another; a Terraform plan may reveal an unexpected change; a model-driven request may be rejected because the path or value is unsuitable. For each case, identify the evidence you would collect, the layer where the fault may exist, and the safest next action.
Operational preparation should include rollback or remediation thinking, not only successful deployment. Practise separating a syntax problem from an authentication problem, a model mismatch from a device capability issue, and an automation defect from a pre-existing network condition.
Build a lab that follows the automation lifecycle
A useful lab should take you from intent to implementation to verification. Repeating disconnected command examples is less valuable than building a small workflow in which you define desired data, select an interface or tool, execute a change, inspect the response, and confirm the resulting device or platform state.
Keep the lab deliberately small. A limited topology or controlled set of configuration objects makes it easier to identify whether an error came from the model, credentials, transport, syntax, variable handling, provider behavior, or the target system. Record each failure and the evidence that resolved it.
The current blueprint names Cisco NX-OS, Cisco Nexus Dashboard, Cisco ACI in network-centric mode, and Cisco Nexus Hyperfabric. Your lab access may not cover every listed technology. Do not imply that an unavailable platform has been mastered; instead, use the official topic list to guide focused reading and use practical labs to deepen the transferable automation concepts you can actually test.
A repeatable lab cycle
Begin with a written outcome such as creating or validating a defined network state. Identify the data required, the target interface, and the expected response. Then implement the smallest working version before adding variables, templating, multiple targets, or error handling.
Run the workflow more than once. Observe whether the second run proposes or performs unnecessary changes. Review the output rather than assuming that successful task completion proves the intended state exists. Introduce a controlled variation, such as a changed variable or an incorrect value, and trace how the workflow reports it.
Finish by documenting verification commands or queries, likely failure points, and the remediation path. This record becomes a revision aid and exposes whether you understand the complete lifecycle rather than only the initial deployment step.
When a full lab is unavailable
You can still prepare productively without access to every Cisco platform. Use the current topics PDF to create decision tables, inspect model examples with the named tools where available, trace Ansible and Terraform workflows on a small test environment, and practise interpreting sample outputs from official learning material.
Be precise about the limit of that approach. Reading about Cisco Nexus Dashboard or ACI does not equal operating those systems. Mark platform-specific areas for targeted review, and prioritize understanding the architecture, interface purpose, data-model relationship, and troubleshooting logic that the official topics require.
Study the interfaces as a connected system
Do not memorize OpenConfig, IETF YANG, native YANG, NETCONF, gNMI, gRPC, and gNOI as separate flashcard definitions. Trace how a model and a protocol work together, what the client is requesting, how the network element processes it, and how the result is observed or validated.
A useful note for each interface contains four entries: the kind of task it supports, the data representation involved, the expected response or operational result, and one likely failure mode. Add a fifth entry for the tool or workflow that would help inspect or troubleshoot the interaction.
Use YANG Suite and pyang as practical study anchors because both appear in the current exam topics. Practise examining a model, checking structure or validity, locating the relevant path, and explaining how that information would affect an automation request. The objective is informed selection and diagnosis, not tool-name recognition.
Questions to ask while reviewing a model
First ask whether the model is OpenConfig, IETF, or native, and what that choice means for portability or device-specific behavior. Then identify the hierarchy, required elements, types, constraints, and operational meaning of the data being addressed.
Next ask how the request will travel to the device and what the response tells you. If the operation fails, determine whether the problem is model selection, path construction, value validation, transport, authorization, or target capability. Writing these distinctions beside a model example turns passive reading into troubleshooting practice.
Learn Ansible and Terraform by comparing responsibilities
Ansible playbooks and Terraform plans should be studied through their different workflow responsibilities. Understand what is declared, when changes are evaluated, how variables and templates affect the result, how state or execution output is used, and how a failed operation is investigated.
The current blueprint explicitly includes Jinja2 templates, Ansible playbooks, Terraform plans, GitOps, and troubleshooting Ansible- and Terraform-based automation. Create one comparison sheet with rows for input data, rendering or planning, execution, state awareness, review, repeatability, and recovery. Fill it with observations from small exercises rather than generic tool slogans.
For Jinja2, practise reading the rendered result and tracing a variable to the final configuration. For Ansible, inspect task conditions, changed status, error output, and behavior on a repeated run. For Terraform, examine the proposed plan, resource relationships, state implications, and what you would verify after applying a change.
Use GitOps as a workflow question
Treat GitOps as a controlled operating model, not merely as a repository containing automation files. Study how a desired configuration is represented, reviewed, changed, applied, and reconciled, and where evidence of approval or drift would be found.
When reviewing a scenario, ask what should happen before deployment, how an unintended change is detected, and how the workflow returns the environment to an approved state. This naturally connects GitOps to version control, infrastructure as code, validation, and operations.
Add AI in automation without losing engineering discipline
The current exam covers AI in automation, but the supplied official facts do not define a separate percentage, tool list, or required implementation exercise for that topic. Prepare by applying normal engineering controls: define the intended outcome, validate generated artifacts, protect credentials and sensitive data, test changes, and verify the resulting network state.
Do not treat generated code or explanations as authoritative. A useful study exercise is to review an automation snippet for incorrect assumptions, unsafe defaults, missing error handling, or a mismatch between the chosen model and the target platform. Then explain how you would test and correct it before execution.
Keep this subject connected to operations. The important preparation decision is not whether a tool can produce a script, but whether you can evaluate its output, identify uncertainty, maintain reviewability, and prevent an unverified change from reaching production systems.
Follow a staged roadmap instead of tool-hopping
A staged plan is more efficient than alternating randomly between Python, APIs, Ansible, Terraform, and Cisco platform notes. Move from blueprint orientation to interface fundamentals, then to repeatable workflows, followed by troubleshooting and mixed review. Adjust the emphasis when your lab results show a specific weakness.
Use the roadmap below as a sequence, not as a fixed calendar. The official sources establish the domains and topics; the order and exercises are practical recommendations for turning that information into usable preparation.
Stage one: map the current scope
Read the current 300-635 DCNAUTO v2.0 exam-topics document from beginning to end. Copy each domain and subtopic into a checklist. Mark every item as explain, demonstrate, or unfamiliar. A topic should not be marked complete merely because you recognize its name.
Separate current v2.0 notes from legacy DCAUTO v1.1 notes. Keep the older material only where it clarifies a foundation you still need. This step prevents outdated certification labels and topic boundaries from controlling your study plan.
Stage two: strengthen the foundation
Review the automation concepts needed to discuss intended state, data representation, interfaces, repeatability, validation, and version-controlled change. Build a small glossary in your own words, then test it by explaining how each concept appears in a real workflow.
At this point, identify whether your main weakness is networking, programming, or automation operations. If networking is weak, prioritize Cisco data-center architecture and device behavior. If programming is weak, practise data structures, variables, templating logic, and error interpretation. If operations is weak, focus on verification, drift, failure isolation, and recovery.
Stage three: practise programmability
Work through the current model-driven topics in a connected sequence. Inspect YANG structures with YANG Suite or pyang where possible, compare OpenConfig, IETF, and native approaches, and trace how a model becomes a request through NETCONF, gNMI, gRPC, or gNOI.
For every exercise, write down the expected result and the diagnostic evidence you would need if it failed. This makes the lab useful for both Network Element Programmability and Operations rather than treating them as isolated subjects.
Stage four: build infrastructure-as-code workflows
Create small Jinja2, Ansible, and Terraform exercises that each have an input, a proposed or rendered result, an execution step, and a verification step. Store changes in version control and review the difference between the previous and intended states.
Deliberately introduce a bad variable, incorrect path, missing prerequisite, or target mismatch in a safe environment. Observe how the tool reports the problem. Then document the correction and the validation that proves the fix worked.
Stage five: rehearse mixed scenarios
Combine domains in one exercise: select a model-driven interface, represent desired state, use an automation tool to apply it, and verify the result. Add a GitOps-style review step and a troubleshooting branch. Mixed scenarios reveal gaps that topic-by-topic revision can hide.
At the end of this stage, explain why your chosen method is appropriate and what alternative you rejected. The ability to justify a workflow is a stronger readiness signal than the ability to recite a command sequence.
Stage six: review by evidence
Return to the blueprint and require evidence for every checked item. Evidence may be a completed lab, a clear explanation, a model inspection, a troubleshooting record, or a comparison you can defend. Revisit any item supported only by recognition or vague notes.
Use practice questions, when available through legitimate Cisco or approved preparation resources, to identify reasoning gaps. Do not seek leaked questions or exam dumps, and do not assume memorization guarantees a passing result. The objective is to understand the tested skills and apply them to unfamiliar situations.
Avoid preparation traps that waste time
The most damaging mistakes are usually planning mistakes: studying the wrong version, equating syntax with understanding, ignoring operations, or treating a successful command as proof of a correct network state. Correct these early by tying every resource and lab to a current blueprint item.
A second trap is over-specializing in a favorite tool. Comfort with Ansible does not automatically demonstrate Terraform planning, model-driven programmability, platform knowledge, or operational diagnosis. Keep a visible checklist and require yourself to move between tools and layers.
Mistake: relying on legacy naming
Older resources may refer to DCAUTO 300-635 and the legacy Cisco Certified DevNet Specialist outcome. The current Cisco listing uses 300-635 DCNAUTO and identifies the current outcome as Cisco Certified Automation Specialist – Data Center Automation and Programmability. Confirm that each resource matches the current v2.0 blueprint before making it central to your plan.
Mistake: memorizing protocol labels
Knowing that NETCONF, gNMI, gRPC, or gNOI appears in the syllabus is not the same as knowing when and how the interface participates in an automation workflow. Pair each term with a model, an operation, expected evidence, and a failure-diagnosis path.
Mistake: ignoring the second run
A workflow that works once may still be unsuitable for repeatable automation. Run it again, inspect what the tool believes should change, and determine whether the result is stable and explainable. Repetition exposes problems with templates, state, idempotence, variables, and verification.
Mistake: studying deployment without recovery
Operational competence includes knowing what to inspect when automation fails or produces an unexpected result. Add controlled failures to your labs and record the order in which you check credentials, connectivity, model paths, input data, tool output, target state, and environmental conditions.
Check scheduling and certification implications
Before scheduling, confirm the current exam identity, blueprint, delivery information, language availability, price, and any payment or credit arrangements on Cisco’s current exam page. The supplied Cisco listing states that the current 300-635 DCNAUTO exam is 90 minutes long, costs US$300 or may be taken using Cisco Learning Credits, and is available in English and Japanese.
These details are scheduling facts, not reasons to rush. Schedule only after your preparation evidence covers all four domains and your practical work includes both successful execution and troubleshooting. If you are pursuing CCNP Data Center or CCNP Automation, confirm how this concentration exam fits your complete certification plan rather than assuming it is sufficient by itself.
Cisco states that the current exam can be used toward recertification requirements. Check the current certification and recertification rules when planning that use, because the exam’s role in your personal certification timeline depends on the requirements that apply to your credential.
Final readiness checklist
Before you commit to a date, confirm that you can identify the current exam as 300-635 DCNAUTO, explain all four blueprint domains, and describe the relationship between models, protocols, tools, desired state, and verification.
You should also be able to work through a small automation change using Jinja2, Ansible, or Terraform concepts; interpret a plan, rendered configuration, or execution result; discuss OpenConfig, IETF, and native YANG considerations; and outline a troubleshooting sequence for a failed automation workflow.
Finally, review your platform coverage. The current topics include Cisco NX-OS, Cisco Nexus Dashboard, Cisco ACI in network-centric mode, and Cisco Nexus Hyperfabric. If one of these is unfamiliar, use the official blueprint to direct final reading and mark the boundary between knowledge gained from documentation and behavior verified in a lab.
Take the next practical steps
Open the current Cisco exam page and v2.0 topics document, then build a four-column checklist for Network Automation Foundation, Infrastructure as Code, Network Element Programmability, and Operations. Assign your next lab or reading task to the weakest column, while reserving review time for AI in automation and the named Cisco technologies.
After each study session, record one decision you can now make, one failure you can now diagnose, and one topic still requiring evidence. When the checklist is supported by explanations and repeatable practice—not just completed reading—recheck Cisco’s current scheduling information and decide whether the exam belongs in your immediate certification plan.
Conclusion
Prepare for the current 300-635 DCNAUTO v2.0 exam as an automation engineering assessment, not as a collection of commands to memorize. Give equal attention to Infrastructure as Code, Network Element Programmability, and Operations because each domain is weighted at 25%, while still covering the 15% Network Automation Foundation domain. Keep legacy DCAUTO material clearly separated, practise the full intent-to-verification lifecycle, and schedule only after your blueprint checklist is supported by practical evidence.
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