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CCNA · Domain 5

AI, and Network Operations and Management practice questions

AI, and Network Operations and Management is worth 10% of the CCNA exam — the lightest of the 5 domains. Agentic and generative AI in network operations, network management approaches, SNMP, Ansible and syslog. Official weighting 10%. 6 fully worked examples are further down this page, answers included.

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6 sample AI, and Network Operations and Management questions, fully explained

Questions from the CCNA bank mapped to domain 5, with the answer key and the reasoning behind every option. None of them repeat the examples on the main CCNA practice page.

Question 1AI, and Network Operations and Management

An operations manager is deciding whether the team needs an AI agent or whether the generative AI chat assistant it already licenses is enough. Which two capabilities would the agent add that the chat assistant, used only through typed prompts, does not provide? (Select TWO.)

Choose TWO.

  • a
    It breaks a goal into steps and picks the next step from earlier results Correct

    Planning and adapting across steps is a defining agent capability; a chat assistant answers one prompt and stops.

  • b
    It writes natural-language explanations of configuration and show output

    Both can do this: the agent usually runs on the same kind of language model as the chat assistant.

  • c
    It calls external tools, such as device APIs, to collect data and act Correct

    Tool use lets an agent read live state and act. A chat-only assistant sees only what the engineer pastes in.

  • d
    It was trained on large amounts of networking documentation and text

    Training data is a property of the underlying model, which a chat assistant has too.

  • e
    It gives an identical answer each time it receives the same prompt

    Neither is guaranteed to be deterministic; this does not separate the two.

The concept

Agentic AI adds planning across steps and tool use on top of a language model.

Why that’s the answer

An agent wraps a language model with a loop that plans, calls tools and uses each result to choose the next action. A chat assistant has the same language skills and the same kind of training but only returns text for each prompt. Explanations, training data and repeatability are not what separates them.

How to reason it out
  1. Remove anything both tools share (language model, text generation, training).
  2. Keep what needs autonomy: multi-step planning.
  3. Keep what needs access to other systems: tool and API calls.

Exam tip: Same model underneath; the agent adds a plan-act loop and tools.

CCNA Agentic AI and Prompting: AI Agents, Guardrails and Prompt Components for Network Operations — the lesson that teaches this.

Question 2AI, and Network Operations and Management

Engineers already paste show output into a generative AI chat tool to get troubleshooting ideas. The NOC now wants something that, when a WAN link flaps at 2 a.m., collects data from both routers, correlates it with syslog, and pages the on-call engineer with a summary without an engineer starting it. What does this require?

Choose one.

  • a
    An EEM applet on each router that emails its show output on link-down

    EEM reacts to the event and runs commands, but each router sends its own raw output: nothing correlates both routers with syslog or writes a summary.

  • b
    An AI agent that triggers on events and runs diagnostics on its own Correct

    Starting on an event, gathering data from devices and sending a page are agent behaviours: autonomy plus tool use.

  • c
    A saved prompt that asks for the answer as a table of probable causes

    A prompt template improves the output format, but someone still has to run it and supply the data.

  • d
    A chat assistant whose persona is set to an experienced NOC engineer

    A persona shapes tone and depth; it does not let the assistant react to events or reach the routers.

The concept

What an agent adds is the ability to start from an event and act through tools, not better wording.

Why that’s the answer

The requirement has three parts that need no person: trigger on an event, collect and correlate data from two routers and syslog, and send a summary. Those are agent capabilities (perceive, reason across sources, act through tools, report). An EEM applet is event-driven but only runs fixed commands on one router and mails raw output, with no correlation or summary. A saved prompt or a persona improves answers to a prompt that someone still has to send.

How to reason it out
  1. List what must happen without a human: trigger, data collection across devices, correlation, summary.
  2. Check which option both reacts to the event and reasons over data from several sources.
  3. Reject a fixed per-device script that only forwards raw output.
  4. Reject options that only improve the answer to a prompt someone still has to send.

Exam tip: If nobody types the prompt, you need an agent, not a better prompt.

CCNA Agentic AI and Prompting: AI Agents, Guardrails and Prompt Components for Network Operations — the lesson that teaches this.

Question 3AI, and Network Operations and Management

An AI agent is asked to find why users in VLAN 30 cannot reach the internet. It runs a show command, reads the output, chooses which device to check next based on what it saw, and repeats this until it finds the cause. What is this repeating cycle called?

Choose one.

  • a
    The agent loop: observe, reason, act, then observe the result Correct

    Reading output (observe), deciding the next check (reason), running it (act) and reading the new result is the agent loop.

  • b
    Fine-tuning, where the agent updates its model weights after each output

    Fine-tuning changes the model offline with training data; the agent here only reads results and chooses actions.

  • c
    Prompt chaining, where an engineer types each follow-up prompt by hand

    In the scenario the agent chooses each next step itself; no engineer is writing the follow-ups.

  • d
    Retrieval, where the agent looks up stored answers

    Retrieval supplies reference text; it does not run commands on devices and adjust based on the results.

The concept

Agents work in a loop: observe, reason, act, and feed the result back into the next decision.

Why that’s the answer

The agent observes device output, reasons about what it means, acts by running the next command, and uses the new result to decide again. That feedback cycle continues until the goal is reached. Fine-tuning changes the model itself, prompt chaining needs a person to drive each step, and retrieval only looks up stored text.

How to reason it out
  1. Notice who chooses the next step: the agent.
  2. Notice that each result feeds the next decision.
  3. That cycle of observe, reason and act is the agent loop.

Exam tip: Agent = a loop: observe, reason, act, observe again.

CCNA Agentic AI and Prompting: AI Agents, Guardrails and Prompt Components for Network Operations — the lesson that teaches this.

Question 4AI, and Network Operations and Management

An AI agent has been given the goal of restoring reachability to 10.20.30.0/24, which is connected to R2. Its action log so far reads:1 R1: show ip route 10.20.30.0 -> % Subnet not in table 2 R1: show ip ospf neighbor -> 2.2.2.2 FULL/DR Gi0/1 3 R2: show ip interface brief -> Gi0/2 10.20.30.1 up up 4 R2: show ip ospf interface brief -> Gi0/1 listed, Gi0/2 not listedWhat is the most useful next step for the agent?

Choose one.

  • a
    Report the goal as met, because the R1 to R2 OSPF adjacency is FULL

    A FULL adjacency is not the goal; R1 still has no route to 10.20.30.0/24.

  • b
    Read R2's OSPF configuration to see why Gi0/2 is not in OSPF Correct

    The last observation narrows the cause to R2's OSPF setup for Gi0/2, so the next action should examine exactly that.

  • c
    Clear the OSPF process on R1, because R1 is missing the route

    Step 4 shows the problem is on R2: OSPF is not running on Gi0/2. Resetting R1 ignores that evidence and disrupts every neighbor.

  • d
    Run the R1 route lookup again until 10.20.30.0/24 appears

    Repeating a check without acting on what was learned does not move the agent toward the goal.

The concept

In the agent loop, each observation should narrow the next action.

Why that’s the answer

Step 4 is the key observation: the subnet's interface on R2 is up but not in OSPF, so R2 never advertises 10.20.30.0/24 and R1 has no route. A well-built agent uses that result to choose its next action, which is to inspect R2's OSPF network or interface configuration. Declaring success on the adjacency, resetting R1 or polling the same command all ignore the latest evidence.

How to reason it out
  1. Restate the goal: a route to 10.20.30.0/24 on R1.
  2. Read the newest observation: Gi0/2 on R2 is up but not in OSPF.
  3. Pick the action that tests or fixes what that observation points to.

Exam tip: A good agent lets the last result choose the next action; it does not loop blindly.

CCNA Agentic AI and Prompting: AI Agents, Guardrails and Prompt Components for Network Operations — the lesson that teaches this.

Question 5AI, and Network Operations and Management

A company is piloting an AI agent that answers troubleshooting questions about 200 IOS XE switches. For the pilot, the agent must be able to collect operational data from any switch, but security requires that it has no way to change a configuration even if it misinterprets a request. How should its device access be set up?

Choose one.

  • a
    A privilege 15 account, with a system prompt that forbids configuration commands

    A prompt instruction is guidance the model may not follow; the account could still change configurations.

  • b
    An account whose AAA command authorization allows only show commands Correct

    Command authorization enforces read-only access on the device side, so a bad decision by the agent cannot become a change.

  • c
    A shared admin account, with weekly review of the commands the agent ran

    Reviewing afterwards detects a change but does not stop one, so it misses the 'no way to change' requirement.

  • d
    An account with privilege 15 on lab switches and read access on production

    The pilot covers the 200 production switches; lab rights do not help, and the mix adds risk without meeting the requirement.

The concept

Least privilege for agents is enforced by the credentials and tools they are given, not by instructions in the prompt.

Why that’s the answer

The requirement is that the agent cannot change configuration even if it gets something wrong. Only a control outside the model does that: an account whose authorization allows show commands and nothing else. A system-prompt rule can be ignored or misread by the model, and a weekly review only finds a change after it has happened.

How to reason it out
  1. Spot the hard constraint: no way to change configuration.
  2. Separate guidance (prompt text) from enforcement (device authorization).
  3. Choose the option the device enforces whatever the agent decides.

Exam tip: Never rely on the prompt for security; scope the agent's credentials.

CCNA Agentic AI and Prompting: AI Agents, Guardrails and Prompt Components for Network Operations — the lesson that teaches this.

Question 6AI, and Network Operations and Management

An AI agent reaches the network through a Model Context Protocol (MCP) server that offers three tools: get_interface_status, get_running_config and push_config. Security requires that the agent can propose fixes but cannot apply any, and that enable secrets, SNMP community strings and VPN keys never reach the model. Which design meets both requirements?

Choose one.

  • a
    Keep the three tools, and add a system-prompt rule that forbids pushing changes

    A prompt rule is guidance, not enforcement, and get_running_config would still pass secrets to the model.

  • b
    Remove push_config, and make get_running_config mask secrets in its output Correct

    Removing the write tool enforces 'propose only', and masking at the tool keeps the secrets out of the model's context.

  • c
    Keep the three tools, and make push_config require an engineer's approval token

    Approval adds a human check, but once approved the agent still applies the change itself, and the unmasked configuration still hands secrets to the model.

  • d
    Remove push_config, and add a system-prompt rule to ignore the secrets it sees

    Removing the write tool meets the first requirement, but the secrets still arrive in the model's context; telling the model to ignore them does not keep them out.

The concept

An agent can only do what its tools allow and only sees what its tools return; both are controlled at the tool layer.

Why that’s the answer

There are two separate requirements. 'Cannot apply' is met only when the agent has no write tool, so push_config must go; an approval token still leaves the agent applying approved changes. 'Secrets never reach the model' is met only when the read tool filters its output before returning it, because whatever a tool returns enters the model's context; a prompt rule to ignore secrets is applied after they have already arrived. Each wrong option misses at least one requirement or relies on a prompt instruction instead of a control.

How to reason it out
  1. Split the stem into its two requirements.
  2. For each option, check whether the write path is removed (not merely discouraged).
  3. Check whether secrets are filtered before they reach the model.
  4. Only one option satisfies both.

Exam tip: Restrict the tools to limit what an agent can do; filter tool output to limit what it can see.

CCNA Agentic AI and Prompting: AI Agents, Guardrails and Prompt Components for Network Operations — the lesson that teaches this.

What CCNA domain 5 tests, topic by topic

The official exam guide breaks AI, and Network Operations and Management into 4 topics. The question bank follows the same split, so a weak topic shows up as a cluster of misses you can go back and read.

Published CCNA practice questions per topic in AI, and Network Operations and Management
TopicWhat it coversQuestions
Agentic AI and prompting for network operationsOfficial CCNA v2.0 exam topics 5.1 and 5.2: Describe the role of agentic AI in network operations. Select a prompt to send to a generative AI system to support network operations considering prompt components such as data classification, output format, persona, and instructions. (Exam description: candidates may be required to evaluate output and recommendations from agentic AI and digital network assistants.)24
Network management approachesOfficial CCNA v2.0 exam topic 5.3: Describe network management approaches (device-based, cloud-based, controller-based, automation-based, and infrastructure as code).24
SNMP and syslogOfficial CCNA v2.0 exam topics 5.4 and 5.6: Describe the function of SNMP in network operations. Interpret syslog message content, severity levels, and facilities.24
Configuration management with AnsibleOfficial CCNA v2.0 exam topic 5.5: Use configuration management mechanisms such as Ansible to execute commands.24
Total96

Revise AI, and Network Operations and Management before you drill it

CCNA Agentic AI and Prompting: AI Agents, Guardrails and Prompt Components for Network OperationsQuestions on this topic are scenarios. Some describe an AI tool or an agent's action log and ask what kind of system it is, what the agent should do next, or how its access and changes should be controlled under a stated constraint, such as no way to change configuration, secrets never reaching the model, an engineer deciding each change, or limiting how many sites a bad change can affect. Others show configuration or show output with an AI recommendation or diagnosis and ask you to evaluate it, which draws on CCNA knowledge from other domains (ACLs, trunks, spanning tree, duplex, DHCP relay, OSPF). Prompt items give a goal and a problem, such as data policy, a parser failing, an audience that cannot follow the answer or a reply that used the wrong platform, and ask which prompt or prompt component fixes it. Some items ask you to select two answers.CCNA Network Management Approaches: Device, Cloud, Controller, Automation and Infrastructure as CodeQuestions on this topic are mostly short scenarios. Some describe how a team makes changes and ask which management approach it is. Others give constraints, such as staffing at each site, where configuration data may be stored, firewall policy, vendor mix or the need for review and rollback, and ask which approach or practice fits. Expect to reason about how cloud-managed devices are onboarded and reached, what keeps working when a management platform is unavailable, how to classify an interface or code excerpt as northbound or southbound, declarative versus imperative definitions, idempotence, and how drift arises and is controlled. Some items show a short code or configuration excerpt, and some ask you to select two answers.CCNA SNMP and Syslog: SNMPv3, Traps, Informs and Syslog Severity LevelsQuestions on this topic are mostly evidence-based scenarios: a log message or a sequence of messages, a snmp-server or logging configuration, or a description of a packet capture is shown, and you are asked what it means, what reaches a destination, or which command meets a requirement. Expect to decode %FACILITY-SEVERITY-MNEMONIC tags and timestamps, rank severity levels, work out which messages pass a logging threshold, choose the right logging destination command, decode a PRI value, and order events across devices. On the SNMP side, expect to place the agent, manager and MIB, match operations to ports and versions, choose between traps and informs or between polling and notifications under a stated constraint, identify an SNMPv3 security level, and spot why an NMS times out. Some items ask you to select two answers.CCNA Ansible: Configuration Management with ios_command and ios_configQuestions on this topic are mostly evidence-based: a YAML task, an inventory or group_vars file, a VTY configuration, a command line or a PLAY RECAP is shown, and you are asked what it does, why it fails, or which change meets a requirement. Expect to tell ios_command from ios_config, place parents and lines correctly, choose between match and save_when values, predict wait_for and register behaviour, pick the right become and connection variables and where to scope them, and separate unreachable from failed. Distractors are real Ansible options used for the wrong job, so learn what each option actually does.

Other CCNA domains

Domain 1: Network Infrastructure and Connectivity25% of the exam · 239 questionsDomain 2: Switching and Network Access25% of the exam · 240 questionsDomain 3: IP Routing20% of the exam · 190 questionsDomain 4: Network Services and Security20% of the exam · 192 questionsAll CCNA practice questionsMixed sessions across all 5 domains — the way the real exam interleaves them.

AI, and Network Operations and Management: your questions

AI, and Network Operations and Management is domain 5 of the CCNA exam guide and carries 10% of the scored content — the lightest of the 5 domains. Cisco has not yet published the exam's question count, so there is no per-domain number of questions to give.

Source

The domain weight and topic list on this page come from the official CCNA exam guide.