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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 options describe common applications of AI/ML in network telemetry and monitoring? (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
✓
Baseline profiling to detect unusual traffic patterns that may indicate an attack
Baseline profiling (correct) uses machine learning to learn normal traffic patterns and detect anomalies like attacks. Dynamic threshold tuning (correct) leverages learned behavior to adjust thresholds automatically, reducing false positives. Automated root cause analysis (correct) correlates events across devices using AI to identify the source of issues. Directly rewriting routing tables in OSPF (wrong) is not an AI/ML application—OSPF has its own protocol mechanisms, and AI would not bypass them without integration. Replacing SNMP with AI-generated proprietary agents (wrong) is impractical and unnecessary; AI enhances rather than replaces standard protocols. Eliminating network logs with synthetic data (wrong) contradicts monitoring needs; logs remain essential for audit and analysis, and AI uses real data for training.
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/ML applications that *augment* existing network operations (like anomaly detection and threshold tuning) versus options that propose unrealistic or protocol-breaking changes (like directly modifying OSPF tables or replacing SNMP), so candidates must recognize that AI/ML works *with* standard protocols, not against them.
Detailed technical explanation
How to think about this question
Dynamic threshold tuning leverages unsupervised learning algorithms (e.g., K-means clustering or Gaussian mixture models) to continuously adapt alert thresholds based on time-of-day, day-of-week, and seasonal traffic patterns. For example, a network monitoring tool like Cisco Catalyst Center uses ML to adjust CPU utilization thresholds during business hours versus maintenance windows, reducing false positives by 40-60% in production environments. Automated root cause analysis (RCA) employs graph-based correlation engines that map events across devices using topological dependencies, often integrating with streaming telemetry (e.g., gRPC or NETCONF) to pinpoint the exact interface or protocol failure within seconds.
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 network engineer at a university connects two campus buildings via a fibre link. Both routers run OSPF, but no adjacency forms — even though both routers can ping each other. The engineer finds one router is in area 0 and the other in area 1. OSPF adjacency requires matching area numbers, hello/dead timers, and network type. IP reachability alone is not enough.
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: Baseline profiling to detect unusual traffic patterns that may indicate an attack — Baseline profiling (correct) uses machine learning to learn normal traffic patterns and detect anomalies like attacks. Dynamic threshold tuning (correct) leverages learned behavior to adjust thresholds automatically, reducing false positives. Automated root cause analysis (correct) correlates events across devices using AI to identify the source of issues. Directly rewriting routing tables in OSPF (wrong) is not an AI/ML application—OSPF has its own protocol mechanisms, and AI would not bypass them without integration. Replacing SNMP with AI-generated proprietary agents (wrong) is impractical and unnecessary; AI enhances rather than replaces standard protocols. Eliminating network logs with synthetic data (wrong) contradicts monitoring needs; logs remain essential for audit and analysis, and AI uses real data for training.
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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