241. Which AI technique is used for real-time network anomaly detection?
- a) Autoencoders
- b) Generative Adversarial Networks
- c) Reinforcement Learning
- d) Convolutional Neural Networks
Answer: A - Autoencoders learn normal traffic patterns and flag deviations through reconstruction error analysis.
242. What is the primary benefit of self-healing network architectures?
- a) Automatic fault detection and remediation
- b) Higher theoretical bandwidth
- c) Backward compatibility with legacy systems
- d) Lower hardware costs
Answer: A - Self-healing systems use closed-loop automation to maintain service continuity during failures.
243. Which protocol enables AI-driven traffic engineering in SD-WAN?
- a) BGP-LS (Link State)
- b) OSPF
- c) SNMP
- d) ARP
Answer: A - BGP-LS provides network topology and telemetry to AI controllers for optimal path computation.
244. What does the "digital twin" concept provide for AI-driven networks?
- a) Virtual simulation environment for testing changes
- b) Hardware acceleration for routing
- c) Quantum encryption
- d) Optical signal processing
Answer: A - Digital twins mirror production networks to safely evaluate AI recommendations before deployment.
245. Which technology enables intent-based traffic steering in AI-driven networks?
- a) Segment Routing with AI Policy (SR-AI)
- b) VLAN stacking
- c) Quantum routing
- d) Optical burst switching
Answer: A - SR-AI combines segment routing's explicit paths with ML-driven policy optimization.
246. What is the purpose of reinforcement learning in traffic engineering?
- a) Continuous optimization based on network feedback
- b) Encrypting management traffic
- c) Prioritizing IoT devices
- d) Replacing BGP
Answer: A - RL agents learn optimal routing strategies through reward/punishment mechanisms.
247. Which AI technique predicts network congestion before it occurs?
- a) Graph Neural Networks
- b) Vision Transformers
- c) Recurrent Neural Networks
- d) Generative AI
Answer: A - GNNs model network topology as graphs to forecast traffic bottlenecks.
248. What is the primary benefit of AI-driven microburst detection?
- a) Identifying sub-millisecond traffic spikes
- b) Reducing protocol overhead
- c) Encrypting bursty traffic
- d) Prioritizing video streams
Answer: A - ML algorithms analyze queueing dynamics to detect microbursts invisible to traditional monitoring.
249. Which protocol provides the telemetry foundation for AI-driven networks?
- a) gNMI (gRPC Network Management Interface)
- b) SNMP
- c) NETCONF
- d) BGP
Answer: A - gNMI's Subscribe method enables high-frequency streaming of interface counters and states.
250. What does the "self-driving network" concept emphasize?
- a) Autonomous operation with minimal human intervention
- b) Hardware acceleration
- c) Quantum-safe routing
- d) Optical bypass
Answer: A - Level 5 autonomy in networking parallels self-driving cars' capability classifications.
251. Which technology enables AI models to explain network decisions?
- a) Explainable AI (XAI)
- b) Blockchain ledgers
- c) Quantum computing
- d) Optical signal processing
Answer: A - XAI techniques like SHAP and LIME reveal feature importance in ML-driven networking.
252. What is the purpose of federated learning in distributed networks?
- a) Collaborative model training without raw data sharing
- b) Encrypting management traffic
- c) Prioritizing WAN traffic
- d) Replacing TCP/IP
Answer: A - Edge devices train local models that are aggregated centrally, preserving privacy.
253. Which AI technique optimizes wireless spectrum allocation?
- a) Deep Q-Networks
- b) Generative Adversarial Networks
- c) Convolutional Neural Networks
- d) Transformer models
Answer: A - DQNs learn optimal channel/frequency assignments through reinforcement learning.
254. What is the primary benefit of AI-driven root cause analysis?
- a) Distinguishing symptoms from underlying faults
- b) Reducing protocol overhead
- c) Encrypting diagnostic data
- d) Prioritizing management traffic
Answer: A - Causal AI models identify true failure sources amidst multiple correlated alerts.
255. Which protocol enables AI-driven congestion control?
- a) BBR (Bottleneck Bandwidth and Round-trip time)
- b) TCP Reno
- c) UDP
- d) ICMP
Answer: A - BBR uses machine learning to model network path characteristics for optimal sending rates.
256. What does the "digital twin" concept provide for AI-driven networks?
- a) Virtual simulation environment for testing changes
- b) Hardware acceleration for routing
- c) Quantum encryption
- d) Optical signal processing
Answer: A - Digital twins mirror production networks to safely evaluate AI recommendations before deployment.
257. Which technology enables intent-based traffic steering in AI-driven networks?
- a) Segment Routing with AI Policy (SR-AI)
- b) VLAN stacking
- c) Quantum routing
- d) Optical burst switching
Answer: A - SR-AI combines segment routing's explicit paths with ML-driven policy optimization.
258. What is the purpose of reinforcement learning in traffic engineering?
- a) Continuous optimization based on network feedback
- b) Encrypting management traffic
- c) Prioritizing IoT devices
- d) Replacing BGP
Answer: A - RL agents learn optimal routing strategies through reward/punishment mechanisms.
259. Which AI technique predicts network congestion before it occurs?
- a) Graph Neural Networks
- b) Vision Transformers
- c) Recurrent Neural Networks
- d) Generative AI
Answer: A - GNNs model network topology as graphs to forecast traffic bottlenecks.
260. What is the primary benefit of AI-driven microburst detection?
- a) Identifying sub-millisecond traffic spikes
- b) Reducing protocol overhead
- c) Encrypting bursty traffic
- d) Prioritizing video streams
Answer: A - ML algorithms analyze queueing dynamics to detect microbursts invisible to traditional monitoring.