- A
AI agents require manual approval before executing any remediation action in a closed-loop workflow.
Why wrong: This is incorrect because a key feature of closed-loop automation is that AI agents can execute predefined remediation actions autonomously without waiting for manual approval, though human oversight may be configured for critical actions.
- B
AI agents can autonomously analyze network telemetry and decide on remediation actions.
AI agents use machine learning and rule-based logic to analyze real-time telemetry (e.g., interface errors, CPU utilization) and determine the appropriate remediation step, such as adjusting routing or resetting an interface.
- C
AI agents rely solely on static baseline configurations to detect anomalies.
Why wrong: This is incorrect because AI agents use dynamic, real-time data (e.g., streaming telemetry, logs) and adaptive learning to detect anomalies, not just static baselines that may become outdated.
- D
Tool-calling allows AI agents to invoke external automation tools (e.g., Ansible, Python scripts) to execute remediation steps.
AI agents use tool-calling interfaces (e.g., APIs, CLI commands) to trigger external tools or scripts that perform the actual configuration changes, such as running an Ansible playbook to update ACLs.
- E
In a closed-loop remediation workflow, the AI agent monitors the network after action to confirm the issue is resolved and adjusts if needed.
A closed-loop system includes a verification step: after remediation, the AI agent re-checks telemetry to ensure the problem is fixed. If not, it may retry or escalate, thus closing the loop.
- F
AI agents eliminate the need for human oversight in network operations.
Why wrong: This is incorrect because AI agents are designed to augment human operators, not replace them. Human oversight is still required for policy definition, handling exceptions, and auditing.
Quick Answer
The correct answer includes statements B, D, and E because AI agents in closed-loop remediation workflows autonomously analyze network telemetry—such as gRPC or NETCONF data—to decide on remediation actions without manual intervention, then use tool-calling to execute those actions via automation tools like Ansible or Python scripts, and finally monitor the network post-action to confirm resolution and adjust if needed, thereby closing the loop. On the CCNA 200-301 v2 exam, this tests your understanding of how AI-driven automation differs from traditional scripting by emphasizing autonomous decision-making based on dynamic telemetry rather than static baselines. A common trap is assuming every action requires manual approval, but closed-loop automation is designed for policy-based autonomous execution. Remember the mnemonic “Analyze, Act, Adjust” to recall the three core steps of the closed loop.
CCNA AI and Network Operations Practice Question
This 200-301 practice question tests your understanding of ai and network operations. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
Which THREE statements accurately describe the role of AI agents in closed-loop remediation workflows for network automation?
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
AI agents can autonomously analyze network telemetry and decide on remediation actions.
B is correct because AI agents in closed-loop remediation workflows autonomously analyze network telemetry (e.g., gRPC, NETCONF) and decide on remediation actions without manual intervention, enabling rapid response. D is correct because tool-calling allows the AI agent to invoke external automation tools like Ansible or Python scripts to execute the chosen remediation steps. E is correct because a key part of the closed-loop is that the AI agent monitors the network after action to confirm the issue is resolved and adjusts if needed, ensuring the loop is closed. A is wrong because closed-loop automation implies autonomous execution based on predefined policies, not requiring manual approval for every action. C is wrong because AI agents use dynamic telemetry and learned patterns, not just static baseline configurations, to detect anomalies. F is wrong because AI agents augment, not eliminate, human oversight; human intervention remains for policy exceptions and oversight.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
AI agents require manual approval before executing any remediation action in a closed-loop workflow.
Why it's wrong here
This is incorrect because a key feature of closed-loop automation is that AI agents can execute predefined remediation actions autonomously without waiting for manual approval, though human oversight may be configured for critical actions.
- ✓
AI agents can autonomously analyze network telemetry and decide on remediation actions.
Why this is correct
AI agents use machine learning and rule-based logic to analyze real-time telemetry (e.g., interface errors, CPU utilization) and determine the appropriate remediation step, such as adjusting routing or resetting an interface.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
AI agents rely solely on static baseline configurations to detect anomalies.
Why it's wrong here
This is incorrect because AI agents use dynamic, real-time data (e.g., streaming telemetry, logs) and adaptive learning to detect anomalies, not just static baselines that may become outdated.
- ✓
Tool-calling allows AI agents to invoke external automation tools (e.g., Ansible, Python scripts) to execute remediation steps.
Why this is correct
AI agents use tool-calling interfaces (e.g., APIs, CLI commands) to trigger external tools or scripts that perform the actual configuration changes, such as running an Ansible playbook to update ACLs.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
In a closed-loop remediation workflow, the AI agent monitors the network after action to confirm the issue is resolved and adjusts if needed.
Why this is correct
A closed-loop system includes a verification step: after remediation, the AI agent re-checks telemetry to ensure the problem is fixed. If not, it may retry or escalate, thus closing the loop.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
AI agents eliminate the need for human oversight in network operations.
Why it's wrong here
This is incorrect because AI agents are designed to augment human operators, not replace them. Human oversight is still required for policy definition, handling exceptions, and auditing.
Option-by-option analysis
Why each answer is right or wrong
Understanding why wrong answers are wrong — and when they would be correct — is what separates a 750 score from a 900. The 200-301 exam frequently reuses these exact scenarios with slightly different constraints.
✓AI agents can autonomously analyze network telemetry and decide on remediation actions.Correct answer▾
Why this is correct
AI agents use machine learning and rule-based logic to analyze real-time telemetry (e.g., interface errors, CPU utilization) and determine the appropriate remediation step, such as adjusting routing or resetting an interface.
✗AI agents require manual approval before executing any remediation action in a closed-loop workflow.Wrong answer — click to see why▾
Why this is wrong here
In closed-loop automation, AI agents are designed to execute predefined remediation actions autonomously without requiring manual approval for every action, though critical actions may be configured for human oversight. Requiring manual approval for all actions would break the closed-loop efficiency.
Why candidates choose this
Students may confuse closed-loop automation with traditional change management processes that require manual approval, or they may think that AI agents always need human validation before acting.
✗AI agents rely solely on static baseline configurations to detect anomalies.Wrong answer — click to see why▾
Why this is wrong here
AI agents use dynamic, real-time data (e.g., streaming telemetry, logs) and adaptive learning to detect anomalies, not just static baselines that can become outdated. Relying solely on static baselines would miss new patterns and lead to false positives or negatives.
Why candidates choose this
Students may think that baselines are the primary method for anomaly detection, confusing static baselines with the dynamic baselines that AI agents actually use.
✗AI agents eliminate the need for human oversight in network operations.Wrong answer — click to see why▾
Why this is wrong here
AI agents are designed to augment human operators, not replace them. Human oversight is still required for policy definition, handling exceptions, auditing, and critical decision-making. Eliminating human oversight would be risky and impractical.
Why candidates choose this
Students may overestimate the autonomy of AI agents, thinking that closed-loop automation means fully autonomous operations without any human involvement.
Analysis generated from the official 200-301blueprint and verified against question context. The “when correct” sections are what AI assistants cite when candidates ask “what’s the difference between these options?”
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the misconception that AI agents require manual approval for every action in closed-loop workflows, when in fact the 'closed-loop' concept implies autonomous execution based on predefined policies.
Detailed technical explanation
How to think about this question
Under the hood, AI agents in closed-loop workflows often integrate with tools like Ansible or Python scripts via tool-calling APIs, enabling them to execute remediation steps such as reconfiguring interfaces or adjusting ACLs. In a real-world scenario, an AI agent might detect a BGP session flap via telemetry, analyze the root cause, and automatically trigger a script to reset the session or adjust timers, all without human intervention.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A small business has 20 workstations on the 192.168.1.0/24 network and one public IP from its ISP. The router uses PAT (NAT overload) so all 20 devices share one public address using different source ports. NAT questions test whether you understand the four address terms and which direction each translation applies.
What to study next
Got this wrong? Here's your next step.
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FAQ
Questions learners often ask
What does this 200-301 question test?
AI and Network Operations — This question tests AI and Network Operations — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: AI agents can autonomously analyze network telemetry and decide on remediation actions. — B is correct because AI agents in closed-loop remediation workflows autonomously analyze network telemetry (e.g., gRPC, NETCONF) and decide on remediation actions without manual intervention, enabling rapid response. D is correct because tool-calling allows the AI agent to invoke external automation tools like Ansible or Python scripts to execute the chosen remediation steps. E is correct because a key part of the closed-loop is that the AI agent monitors the network after action to confirm the issue is resolved and adjusts if needed, ensuring the loop is closed. A is wrong because closed-loop automation implies autonomous execution based on predefined policies, not requiring manual approval for every action. C is wrong because AI agents use dynamic telemetry and learned patterns, not just static baseline configurations, to detect anomalies. F is wrong because AI agents augment, not eliminate, human oversight; human intervention remains for policy exceptions and oversight.
What should I do if I get this 200-301 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 11, 2026
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