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Quick Answer
The correct answer includes baseline learning of normal traffic patterns to detect deviations, as this is a foundational AIOps core capability for network operations. AIOps platforms use machine learning to establish a dynamic baseline of typical network behavior, so any deviation—such as sudden latency spikes or unusual packet loss—triggers an alert, enabling proactive troubleshooting before users are impacted. On the CCNA 200-301 v2 exam, this concept tests your understanding of how AIOps shifts network management from reactive firefighting to predictive analysis; a common trap is confusing baseline learning with simple threshold-based monitoring, which lacks the adaptive intelligence to filter out normal fluctuations. Remember that AIOps correlates alerts, enables self-healing by rolling back bad changes, and performs predictive maintenance on failing hardware—but it cannot directly manipulate physical-layer hardware or guarantee zero false positives. For a quick memory anchor, think of the acronym B.A.S.E.: Baseline learning, Alert correlation, Self-healing, and predictive maintEnance.
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.
A network engineer is implementing AIOps to improve network reliability. Which four of the following are core capabilities that AIOps platforms typically provide? (Choose four.)
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
✓
Correlation of alerts from multiple network devices to reduce noise
Correlation of alerts from multiple network devices is a core AIOps capability because it uses machine learning to analyze and group related alerts, reducing noise and helping engineers focus on root causes rather than individual symptoms. Self-healing by automatically reverting problematic configuration changes is a key feature, as AIOps can detect anomalies caused by changes and roll back to a stable state to minimize downtime. Predictive maintenance identifies devices likely to fail by analyzing historical performance data and telemetry, enabling proactive replacement or repair before failures occur. Baseline learning of normal traffic patterns allows AIOps to detect deviations that indicate potential issues. The incorrect options are not core AIOps capabilities: Direct manipulation of physical layer hardware is beyond the scope of AIOps, which focuses on software-based analysis and automation; guaranteed elimination of all false positives is unrealistic because no system can achieve 100% accuracy in alarm filtering.
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
Candidates often mistakenly assume that AIOps includes direct hardware control or promises zero false alarms, but these are not realistic or core capabilities; AIOps focuses on data-driven analysis and automated responses within the software layer.
Detailed technical explanation
How to think about this question
AIOps platforms typically ingest data from multiple sources (e.g., SNMP, syslog, NetFlow, streaming telemetry) and apply unsupervised learning algorithms to correlate alerts based on temporal and topological relationships, reducing thousands of alerts to a few incidents. Self-healing often relies on configuration management databases (CMDBs) and automated rollback scripts triggered by anomaly detection thresholds, such as a sudden spike in CPU or interface errors after a change. Predictive maintenance uses time-series analysis and failure pattern recognition, often leveraging models like random forests or LSTMs, to forecast hardware failures (e.g., disk or fan failures) days in advance, allowing proactive maintenance during maintenance windows.
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: Correlation of alerts from multiple network devices to reduce noise — Correlation of alerts from multiple network devices is a core AIOps capability because it uses machine learning to analyze and group related alerts, reducing noise and helping engineers focus on root causes rather than individual symptoms. Self-healing by automatically reverting problematic configuration changes is a key feature, as AIOps can detect anomalies caused by changes and roll back to a stable state to minimize downtime. Predictive maintenance identifies devices likely to fail by analyzing historical performance data and telemetry, enabling proactive replacement or repair before failures occur. Baseline learning of normal traffic patterns allows AIOps to detect deviations that indicate potential issues. The incorrect options are not core AIOps capabilities: Direct manipulation of physical layer hardware is beyond the scope of AIOps, which focuses on software-based analysis and automation; guaranteed elimination of all false positives is unrealistic because no system can achieve 100% accuracy in alarm filtering.
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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