The blueprint mentions network programmability and automation, but I only have experience with basic CLI. How can I get hands-on with Python and REST APIs without having a massive enterprise lab environment? What are the best tools to simulate this for someone at the CCNA level trying to reach CCNP?
Practice network automation for the ENCOR exam by utilizing virtual simulation platforms like Cisco Modeling Labs, leveraging Cisco DevNet sandboxes for API testing, and developing Python proficiency for parsing device data and making programmatic configuration changes.
6 answers
You can build a functional lab for automation on a standard laptop by following these steps:
- Install Cisco Modeling Labs or GNS3 to host your virtual network nodes.
- Set up a Linux VM running Ubuntu to act as your primary management station.
- Use the Cisco DevNet Sandbox to practice against live REST APIs without building the back-end yourself.
- Write Python scripts to parse CLI outputs using libraries like TextFSM.
- Learn to execute API calls using Postman before moving to pure Python scripts.
You should prioritize Cisco DevNet sandboxes and CML-Personal over physical gear to build your automation skills for ENCOR. Relying on CLI-only habits will create a bottleneck during the exam, so you must integrate Python scripting against these virtual environments immediately.
I remember dragging my feet on automation back when I was prepping for my CCNP, convinced that my CLI muscle memory was enough to get me through the day. I spent a weekend trying to scrape a few switch configs manually before realizing that the time I wasted was better spent just writing a simple Python script to do the heavy lifting for me.
It wasn't until I spun up a local virtual environment that it finally clicked how these API calls were actually translating to the same commands I had been typing by hand for years. Once you stop treating it like a foreign language and start viewing it as a faster way to do your job, the fear of the code goes away.
Whether you choose the Cisco DevNet Sandbox or a local simulation environment depends on your budget and hardware constraints. The DevNet Sandbox is superior for pure API exposure because it provides instant access to production-grade gear without the overhead of configuring the underlying devices yourself. However, a local environment like CML is better if you want to understand the full stack, including how the network control plane responds to automation triggers.
I find that for most students, the DevNet Sandbox is the most efficient path for passing the exam, but local simulation is the better path for long-term career viability.
Automation is just a different interface for the same configuration tasks you already understand. Stop overthinking the environment and just start using Python to pull data from a single virtual router in GNS3. If you can automate a simple show command, you have already conquered the hardest part of the ENCOR requirements.
Transitioning from CLI to network programmability requires a fundamental shift in how you view the control plane. You aren't just sending commands anymore; you are interacting with structural data models like YANG, which define the state of your network devices through APIs.
To start, download an IDE like VS Code and install the Python extension. You do not need a massive lab; even a single instance of a virtual Nexus or IOS-XE device running in a container is sufficient to test RESTCONF and NETCONF calls. Focus on understanding the request-response cycle. When you send a GET request via the API, look at the JSON output and try to map those keys to the CLI fields you recognize. This bridge between the human-readable CLI output and the machine-readable JSON is where the exam tests your comprehension.
The **DevNet** platform offers free, pre-configured labs that are essentially mirrors of the exam's own internal testing environment. Use these to practice specific tasks like credential management and authentication headers. If you get comfortable with the Postman tool first, you will find it much easier to write the actual Python code later, as you will already have a roadmap of the required endpoints and data structures. Spend your time learning how to authenticate against the device using a token, as that is a prerequisite for every automated task you will perform in the real world or in an exam scenario.
