CCNA AI and Network Operations Practice Question
Which three of the following are key benefits of integrating AI into network operations? (Choose three.)
⚠ Common exam trap
Cisco often tests the distinction between AI as an augmentation tool versus a replacement for human administrators, and the trap here is assuming AI can guarantee 100% uptime or eliminate all manual tasks, which contradicts real-world network reliability principles.
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
✓
Automated detection and correlation of anomalies across the network
The three correct answers highlight practical AI benefits: anomaly detection correlates diverse telemetry (NetFlow, SNMP) to identify issues faster; real-time traffic classification uses ML models for dynamic policy enforcement without manual rule updates; predictive maintenance analyzes historical data to forecast failures, enabling proactive intervention. The wrong options are unrealistic: AI cannot eliminate all human administrators (complex troubleshooting still needs humans), cannot guarantee 100% uptime (failures still occur), and cannot automatically reconfigure physical cabling (that requires physical access).
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Automated detection and correlation of anomalies across the network
Why this is correct
AI-driven anomaly detection ingests high-volume telemetry from switches, routers, and endpoints to establish dynamic baselines of normal behavior. Rather than relying on static thresholds, it correlates subtle deviations across multiple network layers—latency shifts, packet drops, and authentication failures—to surface a single coherent incident. This reduces alert fatigue and accelerates root-cause analysis.
- ✓
Real-time traffic classification and policy enforcement using machine learning models
Why this is correct
Machine learning models can classify encrypted or dynamic application traffic in real time by examining flow metadata such as packet size, inter-arrival time, and session duration. These classifications enable automatic enforcement of QoS priorities, security ACLs, or segmentation policies that adapt as new applications emerge. This is a key benefit because rule-based methods fail when traffic is obfuscated or constantly changes.
- ✓
Predictive maintenance by analyzing historical performance data to forecast failures
Why this is correct
Predictive maintenance applies regression and time-series models to historical device counters—interface errors, CPU utilization, power-supply health, and environmental sensor readings—to estimate time-to-failure for individual components. Detecting degradation trends weeks before an outage allows operators to schedule replacements during maintenance windows. This proactive stance directly reduces unplanned downtime and spares cost.
- ✗
Complete elimination of the need for human network administrators
Why it's wrong here
AI does not remove the human role; it offloads routine monitoring and initial fault isolation, while engineers still handle change approval, security policy decisions, exception handling, and vendor troubleshooting. Autonomous actions are typically bounded by human-defined guardrails, and full accountability for service outcomes remains with operators. The claim is false because it ignores the irreplaceable judgment and ownership that humans provide.
- ✗
Guaranteed 100% network uptime through self-healing algorithms
Why it's wrong here
No self-healing algorithm can guarantee 100% uptime because failures can occur in fiber spans, power feeds, or hardware that cannot be repaired by software alone. Automation can reroute around faults and restart services, but detection, remediation, and validation still introduce measurable downtime, and complex fault domains may require physical repair. Therefore 'guaranteed 100%' is a realistic impossibility, not a key benefit.
- ✗
Automatic reconfiguration of physical cabling without manual intervention
Why it's wrong here
AI-driven network management operates at the logical layer—it can adjust routes, VLAN assignments, and firewall policies—but it cannot physically move patch cables, replace connectors, or repair fiber breaks without robotic hardware. Reconfiguring physical cabling requires an on-site technician to perform the change, so automated policies cannot eliminate manual intervention at the physical plant. This option misstates the scope of AI's control and is not a credible benefit.
Go deeper
Related to this question
Learn chapter
Agentic AI in Network Operations
Key term
NetFlow
NetFlow is a network protocol developed by Cisco that collects and monitors IP traffic data to provide visibility into network usage, performance, and security.
Key term
SNMP
A network protocol used to collect and organize information about managed devices on IP networks and to modify that information to change device behavior.
About these practice questions
Courseiva writes every 200-301 question from scratch — 1,389 in total, each with an explanation and a wrong-answer breakdown. None are copied from real exams or dumps. Learn why practice questions differ from exam dumps →
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This 200-301 practice question is part of Courseiva's free Cisco certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the 200-301 exam.