CCNA AI and Network Operations Practice Question

• ✓ A.Using machine learning to detect unusual traffic patterns that may indicate a security threat or network fault.
• B.Using historical data and ML models to forecast future network traffic loads and capacity requirements.
• C.Automatically generating network configuration scripts using natural language processing.
• ✓ D.Translating high-level business intent into network policies and continuously verifying that the network state matches the intended state.
• E.Using reinforcement learning to optimize routing protocol metrics in real time.

Why A: Options A and D are correct as per CCNA 200-301 v2.0 objective 5.1, which specifically lists anomaly detection using machine learning and intent-based networking as core AI/ML applications in network operations. Option A describes anomaly detection, where ML models trained on baseline traffic identify deviations indicating security threats or faults. Option D describes intent-based networking, where high-level business intent is translated into network policies with continuous verification. Option B (capacity forecasting) and Option C (NLP-based config generation) are not listed in objective 5.1, though they are related AI/ML uses in networking. Option E (reinforcement learning for routing optimization) is also not a core application covered in the CCNA syllabus.

• ✓ .Anomaly detection and proactive threat identification
• ✓ .Automated root cause analysis of network faults
• ✓ .Predictive maintenance of network hardware based on telemetry
• .Replacing all manual CLI configuration with AI-generated scripts
• .Eliminating the need for network monitoring tools entirely
• .Automating the physical installation of network cables

Why : 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.

• ✓ .Baseline profiling to detect unusual traffic patterns that may indicate an attack
• ✓ .Dynamic threshold tuning based on learned normal behavior to reduce false positives
• ✓ .Automated root cause analysis by correlating events across multiple network devices
• .Directly rewriting routing tables in OSPF without any protocol interaction
• .Replacing SNMP with AI-generated proprietary agents on every device
• .Eliminating the need for network logs by using only synthetic data

Why : 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.