Question 837 of 1,819
AI and Network OperationsmediumMultiple SelectObjective-mapped

Quick Answer

The answer is dynamic optimization of traffic routing based on real-time patterns, along with anomaly detection for security threats, automated root cause analysis, and predictive maintenance. These four represent core AI and machine learning use cases in network operations because they rely on adaptive algorithms that learn from data—anomaly detection uses ML to spot deviations from baseline traffic, root cause analysis correlates events across devices, predictive maintenance forecasts hardware failures from historical logs, and dynamic routing applies reinforcement learning to adjust paths as conditions change. On the CCNA 200-301 v2 exam, this topic tests your understanding of how AI/ML enhances automation and proactive management, often appearing as a multi-select question where the traps are static, rule-based tasks like manual VLAN configuration or unrealistic claims like fully autonomous engineers replacing staff. A quick memory tip: think of the four A’s—Anomaly, Analysis, Anticipation, and Adaptation—to recall the key AI-driven operations.

CCNA AI and Network Operations Practice Question

This 200-301 practice question tests your understanding of ai and network operations. This is a configuration task: choose the command set that satisfies every stated requirement. Small differences — like 'secret' vs 'password' or 'transport input ssh' vs 'all' — change whether the answer is correct. 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 four of the following are common use cases or features of AI and Machine Learning in network operations? (Choose all that apply.)

Question 1mediummulti select
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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

Anomaly detection for security threats

Anomaly detection for security threats uses ML to identify deviations from normal traffic patterns, enabling proactive threat identification. Automated root cause analysis leverages AI to correlate events and faults across the network to quickly pinpoint the source of issues. Predictive maintenance applies ML models to historical hardware data (e.g., error logs, temperature) to forecast failures before they occur. Dynamic traffic routing uses reinforcement learning or other AI techniques to adapt routing decisions in real time based on changing network conditions. The two incorrect options are not AI/ML use cases: manual VLAN configuration is a static, rule-based task that does not involve learning or adaptation, and replacing all network engineers with fully autonomous AI is an unrealistic, futuristic concept not representative of current AI/ML applications in network operations.

Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.

Common exam traps

Common exam trap: answer the scenario, not the keyword

Cisco often tests the distinction between tasks that are deterministic and manually configured (like static VLANs) versus those that benefit from adaptive, pattern-based automation (like anomaly detection), so candidates mistakenly think any automation feature qualifies as AI/ML, but manual configuration is not an AI/ML use case.

Detailed technical explanation

How to think about this question

Under the hood, anomaly detection often uses unsupervised learning algorithms (e.g., Isolation Forest or autoencoders) trained on NetFlow/IPFIX data or syslog messages to identify outliers in real time. For example, a sudden increase in failed authentication attempts from a single IP might trigger an alert, allowing the network team to investigate before a breach occurs—this is far more adaptive than static threshold-based rules.

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 help-desk technician troubleshoots why a newly connected PC cannot reach shared printers on the same floor. The cable is good, the switch port is active, but the PC is in VLAN 20 and the printers are in VLAN 10. The uplink trunk only allows VLAN 10. A trunk being up does not mean every VLAN crosses it.

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: Anomaly detection for security threats — Anomaly detection for security threats uses ML to identify deviations from normal traffic patterns, enabling proactive threat identification. Automated root cause analysis leverages AI to correlate events and faults across the network to quickly pinpoint the source of issues. Predictive maintenance applies ML models to historical hardware data (e.g., error logs, temperature) to forecast failures before they occur. Dynamic traffic routing uses reinforcement learning or other AI techniques to adapt routing decisions in real time based on changing network conditions. The two incorrect options are not AI/ML use cases: manual VLAN configuration is a static, rule-based task that does not involve learning or adaptation, and replacing all network engineers with fully autonomous AI is an unrealistic, futuristic concept not representative of current AI/ML applications in network operations.

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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