Automating Cisco Data Center Solutions (DCAUTO): A Practical Preparation Guide
The current Cisco exam is 300-635 DCNAUTO, Automating Cisco Data Center Networking Solutions v2.0. It validates skills across network automation foundations, infrastructure as code, network-element programmability, operations, and AI in automation. It serves candidates pursuing the Cisco Certified Automation Specialist – Data Center Automation and Programmability certification, as well as professionals using it as a concentration exam for CCNP Data Center or CCNP Automation. This guide helps you decide which blueprint areas deserve the most study time, which tools to practise, and whether older DCAUTO material is still relevant.
Which exam should you schedule?
Schedule against the current 300-635 DCNAUTO listing, not an older page that only says DCAUTO. Cisco currently identifies the exam as Automating Cisco Data Center Networking Solutions v2.0. The change in title matters because the current blueprint and technology coverage should control your preparation plan. [https://www.cisco.com/site/us/en/learn/training-certifications/exams/dcnauto.html]
The legacy exam-topics document identifies Automating Cisco Data Center Solutions v1.1 as DCAUTO 300-635 and describes it as a 90-minute exam. That document is useful for understanding older study products, but it should not replace the current v2.0 topics document when you are planning to take the current exam. [https://learningcontent.cisco.com/documents/marketing/exam-topics/300-635-DCAUTO-v1.1.pdf]
A sensible first action is to open the current Cisco exam listing and the current exam-topics PDF together. Check the exam name, version, technology references, and domain headings before buying a course or using any practice material. If a resource focuses only on the legacy certification name or omits current technologies such as Cisco Nexus Hyperfabric, treat it as supplementary rather than as your blueprint. [https://learningcontent.cisco.com/documents/marketing/exam-topics/300-635-DCNAUTO-v2.0-7-9-2025.pdf]
What does passing the current exam do?
Passing the current exam earns the Cisco Certified Automation Specialist – Data Center Automation and Programmability certification. The exam also satisfies the concentration-exam requirement for the CCNP Data Center and CCNP Automation certifications, and Cisco states that it can be used toward recertification requirements. These are separate planning decisions: a candidate may be pursuing a specialist certification, a professional-level track, or recertification. [https://www.cisco.com/site/us/en/learn/training-certifications/exams/dcnauto.html]
The exam is therefore a reasonable choice when your target is data-center automation rather than a general programming credential. Its content connects automation methods to Cisco data-center platforms, including Cisco NX-OS, Cisco Nexus Dashboard, Cisco ACI in network-centric mode, and Cisco Nexus Hyperfabric. [https://learningcontent.cisco.com/documents/marketing/exam-topics/300-635-DCNAUTO-v2.0-7-9-2025.pdf]
Do not assume that an older certification description gives the current outcome. Cisco’s legacy DCAUI course page says that passing 300-635 DCAUTO v1.1 earned the Cisco Certified DevNet Specialist – Data Center Automation and Programmability certification. That is a legacy statement and should not be used to describe the current v2.0 result. [https://www.cisco.com/c/dam/en_us/training-events/training-services/courses/implementing-automation-for-cisco-data-center-solutions-dcaui.pdf]
How much study time belongs in each domain?
Use the current blueprint as a time-allocation tool: Network Automation Foundation is 15%, Infrastructure as Code is 25%, Network Element Programmability is 25%, and Operations is 25%. Each percentage is attached to its official domain here because the labels are more useful than isolated numbers when you build a study schedule. [https://learningcontent.cisco.com/documents/marketing/exam-topics/300-635-DCNAUTO-v2.0-7-9-2025.pdf]
The three 25% domains should receive the largest and most consistent practice blocks. Infrastructure as Code requires more than knowing definitions: you should be able to reason about Jinja2 templates, Ansible playbooks, Terraform plans, GitOps, and troubleshooting Ansible- and Terraform-based automation. Network Element Programmability requires a clear picture of models, protocols, and tooling. Operations requires you to connect automation changes with validation and troubleshooting decisions. [https://learningcontent.cisco.com/documents/marketing/exam-topics/300-635-DCNAUTO-v2.0-7-9-2025.pdf]
Network Automation Foundation still matters even though it is 15%. Use it to establish the vocabulary and design logic that the larger domains depend on. A candidate who skips foundational concepts may recognize individual tools but struggle to select the appropriate automation approach or interpret how a workflow interacts with the data-center environment.
Do not turn the percentages into a promise about how many questions will appear in a particular format. The supplied blueprint provides domain weights, not a guaranteed question count. Use the weights to prioritize revision, then study every listed objective rather than gambling on the largest domains alone.
What technologies and platforms must you connect?
Prepare to relate automation techniques to Cisco NX-OS, Cisco Nexus Dashboard, Cisco ACI in network-centric mode, and Cisco Nexus Hyperfabric. The goal is not to memorize a disconnected product list; it is to understand which automation concepts apply to the platforms named in the current v2.0 technology coverage. [https://learningcontent.cisco.com/documents/marketing/exam-topics/300-635-DCNAUTO-v2.0-7-9-2025.pdf]
Build a comparison sheet with one row for each platform and columns for the object or resource being automated, the interface or model involved, the desired state you are expressing, and the way you would validate the result. Only fill a cell when your lab or Cisco documentation supports it. This prevents a common mistake: transferring assumptions from one Cisco data-center technology to another without checking the relevant model or workflow.
Cisco ACI in network-centric mode deserves deliberate attention because the current blueprint names that mode specifically. Do not silently substitute a broad ACI study plan for the exact scope in the exam topics. Similarly, include Cisco Nexus Hyperfabric in your review because it is explicitly named in v2.0, even if your day-to-day environment is centered on NX-OS or another platform. [https://learningcontent.cisco.com/documents/marketing/exam-topics/300-635-DCNAUTO-v2.0-7-9-2025.pdf]
A useful study exercise is to take one intended change, such as creating or modifying a data-center resource, and describe it in four ways: the desired state, the model or API representation, the automation tool that applies it, and the operational checks that confirm success. The exercise is a preparation recommendation, not a claim about a particular exam question.
How should you study infrastructure as code?
Study infrastructure as code as a controlled workflow rather than as a collection of commands. The current topics explicitly include Jinja2 templates, Ansible playbooks, Terraform plans, GitOps, and troubleshooting Ansible- and Terraform-based automation. Practise how inputs become rendered configuration or planned resources, how changes are reviewed, and how failures are isolated. [https://learningcontent.cisco.com/documents/marketing/exam-topics/300-635-DCNAUTO-v2.0-7-9-2025.pdf]
For Jinja2, work through variable substitution, conditional rendering, and the effect of missing or incorrectly scoped data. Your objective is to inspect the generated result and identify whether the defect originated in the input data, the template logic, or the target configuration. Avoid spending all your time writing attractive templates; the exam preparation value comes from understanding the behavior and the resulting configuration.
For Ansible, trace a playbook from inventory and variables through task execution and validation. Practise identifying where idempotence, ordering, error handling, or an incorrect variable can produce an unwanted result. For Terraform, learn to read a plan as a proposed change, distinguish configuration intent from provider or platform behavior, and investigate why the plan does not match the desired outcome.
For GitOps, focus on the relationship between the version-controlled desired state and the process that applies it. A practical drill is to make a small change in a branch, review the difference, render or plan the result, and then define the validation evidence you would require before accepting the change. This builds decision-making skill without relying on memorized questions.
A small lab sequence for IaC
Start with a static data set and a Jinja2 template. Inspect the rendered output before involving a device or platform. Next, place the template or variables inside an Ansible workflow and add a validation step. Then read a Terraform configuration and its plan, deliberately introduce one incorrect input, and trace the resulting difference. Finish by documenting how the change would move through a GitOps review process.
Keep a fault log. Record the symptom, the layer you checked, the evidence you found, and the correction. This is more useful than copying a successful run because it teaches you to separate syntax, data, transport, authorization, platform, and post-change validation problems.
What must you know about network-element programmability?
The current exam topics include OpenConfig, IETF and native YANG models, NETCONF, gNMI, gRPC, gNOI, YANG Suite, and pyang. Prepare to explain how models and protocols fit together, what each tool contributes, and how you would validate a request or response. [https://learningcontent.cisco.com/documents/marketing/exam-topics/300-635-DCNAUTO-v2.0-7-9-2025.pdf]
Separate the model from the protocol in your notes. OpenConfig, IETF, and native YANG describe data models; NETCONF, gNMI, gRPC, and gNOI are part of the programmable interaction and operations landscape. Treating every term as an interchangeable API is a preparation error because it prevents you from reasoning about payload structure, capabilities, transport, and the operation being requested.
Use YANG Suite and pyang as inspection and learning tools, not merely names to memorize. Practise examining a model, checking its structure or validity, and identifying the data path or operation relevant to a task. Then connect that model knowledge to a protocol workflow: determine what you want to read or change, how the request is represented, and what evidence would show that the device accepted and applied it.
Build a one-page decision table with columns for model family, protocol or interface, typical use in your lab, request or response representation, and validation method. Mark unknown entries for follow-up instead of filling them with guesses. This table becomes a fast revision aid and exposes gaps that broad reading often hides.
A troubleshooting drill for programmability
Take one failed automation action and classify the failure before attempting a fix. Ask whether the model path is wrong, the payload is invalid, the protocol interaction is unsuitable, access is denied, the device rejected the operation, or the change was accepted but did not produce the intended state. This layered approach is more durable than memorizing isolated error messages.
Repeat the drill with both configuration and operational data. The point is to understand what the automation system requested and what the network element reported. Compare the intended state with the observed state, then record the smallest corrective change.
How should operations and AI in automation fit the plan?
Operations is one of the current blueprint’s 25% domains, and Cisco’s current exam listing also identifies AI in automation as covered content. Prepare for the operational consequences of automation: validation, observability, safe change handling, and troubleshooting. Treat AI as an automation topic to understand and evaluate, not as a substitute for verifying network state. [https://learningcontent.cisco.com/documents/marketing/exam-topics/300-635-DCNAUTO-v2.0-7-9-2025.pdf] [https://www.cisco.com/site/us/en/learn/training-certifications/exams/dcnauto.html]
For operations study, define what success means before running an automation workflow. Identify the pre-change state, the intended post-change state, the evidence you will collect, and the rollback or correction path if validation fails. Apply the same discipline to Ansible- and Terraform-based troubleshooting because those tools are explicitly included in the current topics. [https://learningcontent.cisco.com/documents/marketing/exam-topics/300-635-DCNAUTO-v2.0-7-9-2025.pdf]
For AI-related preparation, focus on responsible use in an automation workflow: what input is being provided, what output is produced, how you would check it, and what must not be applied without human review. A generated suggestion can be syntactically plausible and still be wrong for the target model, platform, or intended state. Your study notes should therefore include verification steps rather than treating generated content as authoritative.
A practical exercise is to write an automation change, ask a tool or assistant to suggest a refinement if that is available in your learning environment, and then review the result against the model, plan, or rendered configuration. The learning objective is the review process. Do not use undisclosed live exam content or assume that memorization of generated answers demonstrates operational competence.
What should a six-stage study roadmap look like?
Use a staged roadmap that moves from scope to models, then from tools to integrated troubleshooting. Start with the current blueprint, give the three 25% domains the largest share of practice, and reserve the final stage for timed review and weak-area repair. The sequence below is a practical recommendation, not an official Cisco schedule.
Stage one: establish the current scope
Read the current exam listing and v2.0 topics document before opening legacy material. Write down the four domains, their weights, the named platforms, and the named tools. Create a separate legacy-notes section so that older DCAUTO information cannot silently replace current objectives.
At the end of this stage, you should be able to explain why you are taking 300-635 DCNAUTO and identify whether your goal is the specialist certification, a CCNP concentration requirement, or recertification planning.
Stage two: refresh automation foundations
Review the concepts needed to reason about data-center automation: desired state, source data, models, interfaces, execution, validation, and error handling. Map these concepts to the current platform list. Keep the review active by answering “what would I inspect next?” for each scenario rather than producing a vocabulary-only glossary.
Use the 15% Network Automation Foundation domain as the base for the larger domains. Do not let this stage expand indefinitely; once you can explain the workflow and identify its control points, move into the tool-specific practice.
Stage three: build the model and protocol layer
Work through OpenConfig, IETF and native YANG models, then connect them to NETCONF, gNMI, gRPC, and gNOI. Use YANG Suite and pyang in hands-on or documented exercises where possible. For every exercise, save the model path, the request or operation, the response, and the validation result.
If your notes describe a protocol without showing what data or operation it carries, they are incomplete. If they show commands without explaining the model, they are also incomplete. The exam topics require the concepts to work together, so your revision should do the same.
Stage four: practise IaC workflows
Create a progression from Jinja2 rendering to Ansible execution and Terraform planning, then place the work under a GitOps-style review process. Introduce controlled faults such as an incorrect variable, an invalid rendered value, or a plan that differs from the intended change. Diagnose each fault by layer and retain the evidence.
Spend more time reading and troubleshooting than copying finished files. A candidate who can explain why a plan or playbook behaves incorrectly is better prepared than one who has only repeated a successful demonstration.
Stage five: integrate operations
Combine a model-driven or IaC change with operational validation. Define what you will check before and after the change, how you will recognize partial success, and what information you need when the result differs from the desired state. Include Ansible- and Terraform-based troubleshooting because both are named in the current topics.
Add an AI review exercise if your study environment supports one. Compare any suggested automation or explanation with the authoritative model, platform behavior, and observed state. Record the reason for accepting or rejecting the suggestion.
Stage six: audit readiness
Return to every objective in the current topics document and mark it as understood, practised, or unresolved. Revisit unresolved objectives first. Then perform mixed exercises that require you to choose a model, tool, protocol, or troubleshooting step rather than studying each technology in isolation.
Use timed sessions to practise reading carefully and making a defensible choice under the exam’s 90-minute limit. Timing practice is a recommendation based on the published duration; it is not evidence about the number or format of questions. [https://www.cisco.com/site/us/en/learn/training-certifications/exams/dcnauto.html]
Conclusion
The strongest preparation plan for 300-635 DCNAUTO v2.0 is blueprint-led and evidence-based: confirm the current exam identity, prioritize Infrastructure as Code, Network Element Programmability, and Operations, then connect models, protocols, platforms, IaC tools, and validation in practical workflows. Keep legacy DCAUTO material clearly separated from current objectives. Before scheduling, verify the official listing for the details that affect your decision, including the published 90-minute duration, US$300 price or Cisco Learning Credits option, and English and Japanese availability. [https://www.cisco.com/site/us/en/learn/training-certifications/exams/dcnauto.html]
Related exams
- 300-610 exam — Designing Cisco Data Center Infrastructure (DCID)
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