Cisco Designing, Deploying and Managing Network Automation Systems (350‑901 AUTOCOR) Practice Exams
About the Cisco 350-901 AUTOCOR exam
Exam at a glance
Professional tier. The Core exam for both CCNP Automation and CCIE Automation. Around 90–110 questions, 120 minutes, scaled passing score, $400 USD. Valid 3 years plus CE-credit recertification.
AUTOCOR is the modern automation Core (it replaced DEVCOR)
The 350-901 code stayed, but the exam was rebuilt as AUTOCOR v2.0 and Cisco retired the older DEVCOR blueprint. The track was renamed from CCNP DevNet to CCNP Automation, and the focus shifted from general software development toward running real networks:
- CCNP Automation = 350-901 AUTOCOR (Core) + one Concentration exam of your choice.
- CCIE Automation — 350-901 is also the qualifying written exam; pass it, then book the hands-on CCIE Automation lab within 3 years.
Passing 350-901 on its own also earns the standalone Cisco Certified Automation Specialist - Core badge, so the exam delivers a credential even before you complete a full track.
Domain weighting
- Network Automation — 30%
- Infrastructure as Code — 30%
- Operations — 20%
- AI in Automation — 20%
Network Automation and Infrastructure as Code together are roughly 60% of the exam, so most of your study time belongs there. AI in Automation is the newest domain, added in the v2.0 overhaul.
What kind of exam it is
AUTOCOR rewards judgment more than recall. Most questions hand you a scenario where two or three options all technically work, and the point is the one an experienced automation engineer would actually choose — whether to reach for RESTCONF, Ansible, or Terraform for a given change, whether to edit the code or the device, whether to fix a broken call by installing the signing CA chain or by quietly disabling certificate verification. The instinct it rewards again and again is the declarative, model-driven, least-capable, auditable choice.
Prerequisites
No formal prerequisites — Cisco removed the CCNA requirement for CCNP-level exams. Cisco recommends three to five years of hands-on experience with network automation and software development. Most successful candidates are comfortable with Python and REST APIs and have shipped real automation through a pipeline.
Why take this certification
- Single test, two career tracks. 350-901 satisfies the CCNP Automation Core and the CCIE Automation written at once — the most leveraged automation exam in Cisco's portfolio.
- The skills the market actually wants. The v2.0 overhaul dropped generic app-dev content and doubled down on network automation, CI/CD, and AI — the day-to-day toolkit of a modern network engineer.
- Gateway to CCIE. Passing 350-901 starts the 3-year clock for the CCIE Automation lab, Cisco's highest-tier expert credential on the automation track.
- CE-credit value. Cisco professional exams carry Continuing Education credits that can keep your other Cisco certifications current without sitting another full exam.
What you'll learn in the 350-901 AUTOCOR exam
AUTOCOR is deliberately practical — Cisco wants engineers who can build automation, deliver it safely through a pipeline, run it in production, and now supervise an AI assistant inside that workflow. Most questions are scenario-driven, often presenting a partial Python script, an Ansible task, a Docker Compose file, or a red pipeline job. Each free question and every premium exam maps to the four official domains below and to the course's core objectives.
Network automation (30%)
- Construct network automation solutions with Ansible resource modules, Terraform (HCL + a managed state file), RESTCONF (RFC 8040), and Python to manage VLANs, OSPF, interfaces, and ACLs.
- Judge every tool on two axes — declarative vs imperative intent and model-driven (YANG) vs CLI screen-scraping interface — then pick the least capability that meets the requirement.
- Selecting the right approach: stateful lifecycle and drift point to Terraform, one device with no controller to RESTCONF, agentless push across a fleet to Ansible, and logic no framework expresses to Python.
- Consuming REST APIs at scale — pagination, persistent authentication, rate-limit handling, and safe retries.
Infrastructure as code (30%)
- The golden rule of IaC: change the code, never the device — a device's running config is an output of the pipeline, not the thing you edit.
- Git version control — which operations add to shared history (safe) versus rewrite it (dangerous once pushed); revert over reset on a shared branch.
- Diagnosing and building GitLab CE CI/CD pipelines — the four stages (build, prevalidation, deploy, post-validation), where deploy is the only stage that writes to a device.
- Network Simulation with Cisco Modeling Labs (CML) as a disposable test bed, interpreting Docker Compose files (services, networks, volumes), source-of-truth integration (IPAM/DCIM/versioned variables), and encoding a YANG model as YAML/JSON (RFC 7951).
Operations (20%)
- Observe: model-driven telemetry (YANG-Push, RFC 8641) instead of SNMP polling, and a logging strategy that routes automation events through Python's logging framework to syslog or webhooks.
- Verify: diagnosing automation problems by isolating the layer that broke (connectivity → TLS → auth → request) and reading the protocol's own error body; change validation with pyATS — structured before-and-after diffs.
- Secure: CA-signed TLS certificates served with their intermediate chain, and secure coding — validated input, scoped short-lived tokens, and secrets kept out of source and off disk.
- The always-wrong shortcut: disabling certificate verification (
verify=False), hardcoding a token, or committing a secret. A green HTTP 200 does not mean the change is watched, proven, or safe.
AI in automation (20%)
- AI-assisted code — benefits and risks: weigh the productivity gain against data privacy, IP ownership, and the duty to validate every draft.
- AI security risks using the OWASP Top 10 for LLM applications — prompt injection, over-privileged tools, and why "you are read-only" in a prompt is guidance, not a control.
- Building an MCP server with Python FastMCP — exposing network data and actions to an agent through the vendor-neutral Model Context Protocol; and constructing LLM conversational agents where the model asks and your code acts behind allow-lists and approval gates.
- Evaluating AI recommendation accuracy — checking every recommendation against ground truth in CML and pyATS, behind a human sign-off, before it reaches production. The rule: the AI drafts; a human and your code decide.
How the practice exams help
Each free question and every premium exam mirrors the scenario-driven format AUTOCOR uses — partial configs, code snippets, multi-step reasoning across tools. Detailed explanations cover not just why the right answer is right but why the distractors are wrong, so you learn the trade-offs (RESTCONF vs Ansible vs Terraform, revert vs reset, install the CA chain vs disable verification) rather than memorising answers.
How to prepare for the 350-901 AUTOCOR exam
AUTOCOR spans four domains from hands-on tooling to AI supervision, so successful preparation balances reading with real time at a keyboard building and breaking automation. Recommended approach for an engineer with a couple of years of scripting and network experience:
- Read the four domains with the free study guide (4–8 weeks). Work through the official Cisco 350-901 AUTOCOR exam topics domain by domain, and use our free 350-901 study guide as the plain-language companion — it separates the look-alike answer choices with comparison tables and decision trees. Spend the most time on Network Automation and Infrastructure as Code; together they are about 60% of the exam.
- Hands-on labs (in parallel, 4–8 weeks). Use Cisco DevNet sandboxes and Cisco Modeling Labs (CML) to build real automation without owning hardware. Push the same VLAN or OSPF change four ways — Ansible, Terraform, RESTCONF, and Python — so the tool trade-offs become instinct. Build a working GitLab CE pipeline that renders, tests in a CML replica, and deploys.
- Operations and AI week (1–2 weeks). Stream model-driven telemetry to a collector, wire Python logging out to syslog/webhooks, and validate a change with pyATS before-and-after diffs. Then stand up a small FastMCP server, drive it from an LLM agent behind a code-enforced allow-list, and practise checking the model's output against ground truth. Read the OWASP Top 10 for LLM applications.
- Practice exams (2–3 weeks). Start with the free 10-question sample above to gauge your level, then work the 10 full premium practice exams — 1,000 questions with a detailed explanation on every option. Take them timed, review every wrong answer, and aim for a consistent 85%+ before scheduling, since the scaled passing score is unpublished.
Recommended timeline
10–16 weeks of focused study (10–12 hours per week) for engineers already comfortable with Python and REST APIs. Add 4–6 weeks if Ansible, Terraform, CI/CD pipelines, or the AI-in-automation tooling are new to you.
Official resources
Start with the official Cisco 350-901 AUTOCOR exam topics page on the Cisco Learning Network. Pair it with Cisco DevNet for sandboxes and API docs and Cisco Modeling Labs for a disposable lab, then reinforce with our free 350-901 study guide and the practice exams on this page.