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CCNA AI and Network Operations Practice Question
This 200-301 practice question tests your understanding of ai and network operations. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. 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 of the following are key applications of AI in network operations? (Choose three.)
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 and proactive threat identification
AI in network operations enhances efficiency by automating complex analytical tasks. Anomaly detection uses machine learning models to identify deviations from baseline traffic patterns, enabling proactive threat identification before they cause outages. Automated root cause analysis correlates events across the network to pinpoint the origin of a fault, reducing mean time to repair (MTTR). Predictive maintenance leverages telemetry data (e.g., from SNMP, NetFlow, or gRPC) to forecast hardware failures, allowing preemptive replacement and minimizing downtime.
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 AI augmenting versus replacing existing tools and processes, so the trap here is assuming AI can fully automate physical tasks or eliminate foundational monitoring infrastructure, when in reality AI works as an overlay to enhance human decision-making and existing systems.
Detailed technical explanation
How to think about this question
Predictive maintenance often uses time-series analysis of metrics like temperature, packet error rates, and fan speed from devices via telemetry protocols (e.g., gRPC dial-out or NETCONF/YANG). Anomaly detection models, such as Isolation Forests or autoencoders, are trained on historical NetFlow/IPFIX data to flag zero-day attacks or DDoS patterns. Automated root cause analysis employs graph-based algorithms (e.g., Bayesian networks) to traverse dependencies in a network topology, correlating syslog messages and SNMP traps to isolate the fault source.
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
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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 and proactive threat identification — AI in network operations enhances efficiency by automating complex analytical tasks. Anomaly detection uses machine learning models to identify deviations from baseline traffic patterns, enabling proactive threat identification before they cause outages. Automated root cause analysis correlates events across the network to pinpoint the origin of a fault, reducing mean time to repair (MTTR). Predictive maintenance leverages telemetry data (e.g., from SNMP, NetFlow, or gRPC) to forecast hardware failures, allowing preemptive replacement and minimizing downtime.
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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