Views from an Insider on the CCNP Automation Track: DCNAUTO 2.0 Edition

[ad_1] We’ve lastly arrived on the third and ultimate installment of this riveting weblog sequence. Whereas some could also be unhappy on the disappearance of additional sleep they obtained from studying this (my prose is normally an incredible remedy for insomnia), on this weblog, we’ll be overlaying the shiny new DCNAUTO specialization and applied sciences close to and expensive to my coronary heart. Similar to the weblog on AUTOCOR and ENAUTO 2.0, I hope that this can assist make clear the explanation and intent for the massive transforming of the examination subjects to help and help you in your research.

A fork within the street The unique DCAUTO examination had the “shortest” checklist of examination subjects (primarily based solely on my unscientific evaluation of the quantity of textual content on a PDF), however that doesn’t imply that the examination was easy. It lined a broad set of applied sciences with disparate terminology and spanned a number of (sometimes) separate groups (server/compute groups normally are separate from the datacenter networking groups).

However even when you had been in a corporation that had ACI and UCS, more often than not you're employed with just one expertise or the opposite, not each. This complication was solely exacerbated by the truth that the Unified Computing System (UCS) Supervisor Platform Emulator (UCSM-PE) couldn't be related to Cisco Intersight; solely sure builds which had been out there solely to particular groups like Cisco DevNet for his or her Sandbox may achieve this.

This lead to an enormous inside determination: How do we offer an automation certification that focuses on the datacenter, covers the community expertise out there at the moment, consists of platforms and gadgets, and covers the evolving realities within the datacenter (like Kubernetes and containers)? We had some powerful decisions to make, however the result's the DCNAUTO 2.0 (observe the “N” for networking)

Give it to me straight, what has been faraway from DCAUTO? <img loading="lazy" decoding="async" class="wp-image-478793 size-large" src="https://blogs.cisco.com/gcs/ciscoblogs/1/2025/09/image1-1024x657.png" alt="exam topics for Automating Cisco Data Center solutions exam" width="1024" height="657" srcset="https://blogs.cisco.com/gcs/ciscoblogs/1/2025/09/image1-300x193.png 300w, https://blogs.cisco.com/gcs/ciscoblogs/1/2025/09/image1-1024x657.png 1024w, https://blogs.cisco.com/gcs/ciscoblogs/1/2025/09/image1-768x493.png 768w, https://blogs.cisco.com/gcs/ciscoblogs/1/2025/09/image1-1536x986.png 1536w, https://blogs.cisco.com/gcs/ciscoblogs/1/2025/09/image1-2048x1314.png 2048w" sizes="auto, (max-width: 1024px) 100vw, 1024px"/> Determine 1 | Matters No Longer within the DCNAUTO 2.0 Examination

Based mostly on this picture, you possibly can see that a big chunk of the unique blueprint has been eliminated/modified ultimately(the highlighted sections). In some instances, the subjects had been eliminated for a similar as they had been in ENCOR 2.0; subjects like Git, fundamental APIs, or Python digital environments had been eliminated as a result of both (a) they're assumed information (b) lined within the core examination or (c) could be changed with different applied sciences which will work higher with bigger workflows (e.g. improvement inside a container with mapped volumes can change digital environments inside Python).

Inside area 2.0, we eliminated most of the particular API and SDK duties as they pertain to ACI. Whereas these two strategies of automation are nonetheless legitimate, a lot of the event and integration effort inside the datacenter has been centered on Infrastructure as Code (IaC) instruments. With the ability to automate platforms and applied sciences with instruments which have multi-platform help is essential as a result of these datacenters are more and more heterogeneous. So understanding use these instruments inside the community infrastructure turns into a essential ability.

Area 3.0 obtained a light-weight contact of adjustments, largely centered on refining and trimming down superfluous device-centric automation and app-hosting strategies. Whereas these capabilities are nonetheless built-in to our huge datacenter switching portfolio, we tried to concentrate on the most typical use-cases and applied sciences. Keep in mind, the main target of the brand new blueprints is to create practicality and applicability into exams, so we needed to trim away a number of the esoteric or much less used options and performance.

And also you dropped compute?! Sure.

I assume you’ll be in search of a motive on this one, too. Consider me, it wasn’t a simple determination. We went forwards and backwards on this and there have been sturdy arguments to each side, however finally, most of the time, the compute and server groups are utterly totally different than community infrastructure groups, and the practitioners inside these groups had vastly totally different skillsets, making the crossover to be that rather more troublesome.

Moderately than weakening the depth of the check (and the sensible purposes gained from it) to help added breadth, we determined to drop the compute automation utterly. I can already hear the sighs of aid from community automation of us, however I do know there are just a few of us that may miss the inclusion of Intersight and the UCSM APIs (my former compute Developer Advocate counterpart included).

Sufficient about what was dropped, what do we have to research? Throughout the datacenter, there are just a few key applied sciences that we selected to concentrate on. As with the AUTOCOR and ENAUTO 2.0, reference the highest paragraph of the examination subjects checklist to get an understanding of the in-scope platforms. These platforms shouldn’t come as a shock, nevertheless it’s useful to set context round your research.

Infrastructure as Code (IaC) The datacenter should be:

Agile Multivendor Even multicloud

This implies click-ops or particular person automations for various platforms gained’t at all times be accepted. The unifying issue to all of that is one thing like Ansible or Terraform, whereby the syntax throughout platforms and clouds is identical and the one distinction is the modules/collections or suppliers in use.

The DCNAUTO examination displays this, as 25% of the examination falls inside the IaC area. This requires you to be accustomed to the instruments and management options in addition to the platforms lined by the blueprint.

On-box automation and programmability With the scale and scale of recent datacenter networks, platforms are sometimes used to handle the material. Nevertheless, there could also be both particular community automation options or day 0 provisioning that dictate a “box-by-box” course of. Due to this, we’ve included particular examination subjects to validate a learner’s information round these “community component” automation duties in Area 3.

When it comes to particular community component programmability, we’ve included:

NETCONF help, as YANG fashions equivalent to OpenConfig are utilized in massive, doubtlessly multi-vendor or web-scale datacenters, because it normalizes configuration throughout quite a lot of gadgets Familiarity with NETCONF and ncclient, which can be utilized to ship XML-structured payloads to a tool by way of code written in Python Understanding the day-0 provisioning of a tool outdoors of using a controller, and the on-box programmability strategies out there inside the Nexus platform Information round NXAPI and the move of making bespoke templates (which might then be utilized as coverage) inside Nexus Dashboard rounds out the area

Operations (together with Linux Networking!) One of many bigger shifts (throughout all new CCNP-Automation exams) has been the concentrate on operational facets of an automation resolution. In any case, what good is deploying a change with out understanding the impression of that change on the community? That is no totally different inside the datacenter and a few would argue that it’s extra essential; datacenters are finely tuned devices to maneuver knowledge in a short time from place to position. If it doesn’t work, it’s usually costing massive sums of cash.

On this examination, we’ve not a lot “eliminated” subjects, however shifted them in complexity. The unique DCAUTO examination had components that touched on model-driven telemetry and understanding subscriptions to knowledge., together with next-generation protocols like gNMI and gRPC. We additionally embody digital twins and pyATS validation, as we have now in different exams. To not be forgotten, we additionally cowl the flexibility to retrieve well being info by way of Python in opposition to gadgets as effectively.

Lastly, we additionally added the requirement to troubleshoot packet flows from Linux-based hosts operating containers. Everyone knows that containers are the brand new VMs, however the hosts operating these containers don’t use the identical instruments and terminology as a Kind-1 Hypervisor; we should perceive how Linux networking works and the way it's configured.

This consists of how interfaces, subinterfaces, and bonded interfaces are created, in addition to how normal bridges are outlined and the connection between digital Ethernet (veth) interfaces on the host degree and interfaces outlined inside the container runtime. These expertise are not optionally available and we felt it essential to grasp them effectively sufficient to repair them once they break.

We needed to toss in some AI, too Similar to with the remainder of the skilled automation specializations, some AI wanted to be included inside the examination subjects checklist; it’s being talked about in all places and our certifications needs to be no totally different.

Understanding the safety implications of utilizing AI inside the datacenter is essential to guard the huge quantities and worth of that knowledge. Right here there might be unintended penalties round knowledge publicity and as a vector for exfiltration. As agentic AI turns into mainstream, understanding how these brokers join to numerous platforms, gadgets, and controllers is a baseband job; one thing that everybody ought to perceive. With the prevalence of automation and orchestration inside the datacenter, describing and understanding how generative AI can be utilized to speed up prototyping and iteration over community automation options will not be an optionally available ability. It ought to validated for any automation skilled.

Bringing all of it collectively By way of this weblog, and the earlier ones on the AUTOCOR and ENAUTO 2.0, I hope you’ve gained somewhat bit extra perception into the certification and the particular exams (each core and focus). This isn’t simply associated to the exams and subjects themselves, but in addition the mindset shift and totally different method in creating the examination subjects checklist, shifting from software program engineers which can be studying “community” to community engineers which can be studying “automation.” It sounds refined, however the final result could be fairly totally different. By way of this distinction, we hope that you simply discover that the brand new exams align to your automation work in a way more impactful approach.

As at all times, blissful studying! When you have any questions, please contact me on X (@qsnyder) or via the Cisco Studying Community message boards.

Efficient February 3, 2026, the 300-635 DCNAUTO examination will probably be up to date to v2.0 and renamed, “Automating Cisco Information Heart Networking Options v2.0.” Obtain examination subjects